Vento_Aureo_Transfer_UNet_Segmentation.ipynb

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    "# Artificial Neural Networks and Deep Learning\n",
    "# Homework 2: Image Segmentation\n",
    "\n",
    "Students:\n",
    "\n",
    "*   Julián Jiménez: 10657117\n",
    "*   Samuel Polo: 10670388\n",
    "\n",
    "## Transfer Learning Approach:\n",
    "\n",
    "We first developed a simple test regarding the initial FCNN model and then proceeded to make some further improvements with preprocessing. After this, we decided to advance with the Transfer Learning approach, where we could use a successful pre-trained model in 2 ways: using fine tuning or using only the pre-trained weights.\n",
    "\n",
    "We also decided to implement the U-net architecture as it was described in class that it had excellent performance on the image segmentation task and further expanded on the basic encoder-decoder model that we had implemented before.\n",
    "\n",
    "For the contracting path we would use the downloaded model and we chose VGG19. Although it is not the best model in terms of accuracy or efficiency, we chose it as it is relatively simple with a low number of layers. This would allow us to go quickly through the model summary and pick out the outputs that we needed for the skip connections. Also the vgg19 model is very similar to the contracting path used in the u-net architecture so we believed that it would fit nicely within this architecture.\n",
    "\n",
    "We implemented the expanding path utilizing transpose convolutions, swapping them with upsampling layers, concatenation layers which allowed for the skip connections, batch normalization and further convolutions. We initially chose the upsampling layers as they reduced the number of trainable parameters and improved training time, but transpose convolutions increased performance as these extra parameters allowed the model to learn a more effective way of performing the expanding path.\n",
    "\n",
    "After successfully developing the U-net Model we proceeded to make an initial test with Transfer Learning, giving us a result of: 0.50850. Which improved in 119% our previous score with FCNN Model made from scratch.\n",
    "\n",
    "Our first test was to use Transfer Learning without Fine Tuning, as this would improve the training time and give us an idea of the performance of the model, using the pre-trained VGG19 Model along with its pre-processing function that was added to both training and validation images. \n",
    "\n",
    "Finally we decided to use fine tuning as we had the idea that the imagenet dataset was trained for a large number of classes of images, while in this problem we are only trying to recognize buildings. Thus we found it useful for the model to learn the weights for contracting the image and specialize more for the specific problem at hand. This approach indeed created better results for the model.\n",
    "\n",
    "Finally we experimented with other loss functions different to binary cross entropy, such as dice loss or a combination of the 2, but these led to lower performances.\n",
    "\n",
    "We trained all models initially for 50 epochs without early stopping, monitoring the validation loss. If the model looked promising we would do more sets of 50 or 20 epochs of training until we saw no decrease in the validation loss. For the generation of results we would restore the model to the weights it had when it obtained the lowest validation loss.\n",
    "\n",
    "\n",
    "Submited Tests, results without Fine Tuning:\n",
    "*   Transfer Learning, vgg10, u-net model, 100 epochs : 0.52050\n",
    "*   The previous model, 120 epochs: 0.52171\n",
    "\n",
    "Submitted Tests with Fine Tuning:\n",
    "*   Transfer Learning, vgg19, u-net model, 50 epochs: 0.58638\n",
    "*   The previous model, 100 epochs: 0.61936\n",
    "*   The previous model, 120 epochs: 0.62722\n",
    "*   The previous model, 150 epochs: 0.47974\n",
    "*   Transfer Learning, vgg19, u-net model, dice loss, 50 epochs: 0.53517\n",
    "\n",
    "References:\n",
    "\n",
    "* Ronneberger, Fischer, Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation - https://arxiv.org/pdf/1505.04597.pdf\n",
    "* Simonyan, Zisserman. VGG19: VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION - https://arxiv.org/pdf/1409.1556.pdf\n",
    "* Image segmentation tensorflow tutorial - https://www.tensorflow.org/tutorials/images/segmentation\n",
    "* Nieradzik. Losses for Image Segmentation - https://lars76.github.io/neural-networks/object-detection/losses-for-segmentation/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "name": "stdout",
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     "text": [
      "2.1.0-rc1\n"
     ]
    }
   ],
   "source": [
    "%tensorflow_version 2.x\n",
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "y_y_VPDLupDq",
    "outputId": "780cfdaf-d8a3-40d7-ee62-d4bddeebe77b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "# Code to bring data from MyGoogleDrive to Python Notebook.\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9ZVsNbq4eINQ"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "colab_type": "code",
    "id": "A69w3uuGeUhK",
    "outputId": "0da56636-cc27-4741-e81e-68d2417fed94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content\n"
     ]
    }
   ],
   "source": [
    "# Set the seed for random operations. \n",
    "# This allows our process to be reproducible. \n",
    "\n",
    "SEED = 1234\n",
    "tf.random.set_seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "\n",
    "# Get current working directory\n",
    "cwd = os.getcwd()\n",
    "print(cwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vQOzjsLpzM9h"
   },
   "outputs": [],
   "source": [
    "# Set the directories for all, training and validation images.\n",
    "\n",
    "dataset_dir = os.path.join(cwd, 'Segmentation_Dataset')\n",
    "training_dir = os.path.join(dataset_dir,'training')\n",
    "valid_dir = os.path.join(dataset_dir,'validation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sa_3rfgO0Iz3"
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   "source": [
    "# **Validation Directory Creation**\n",
    "\n",
    "This code is used to create the validation directory and fill it up with images from all the classes. The validation set is created with stratified sampling since all classes are not represented equally in the given dataset. In this way from each class we select the same proportion of images. This is done to reduce sampling error and allow all classes to be represented in the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JRhcFbMS0VWN"
   },
   "outputs": [],
   "source": [
    "# Creates validation directory if it does not exist already.\n",
    "_ = os.makedirs(valid_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GcKKNr47_iDo"
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   "source": [
    "# Proportion for Hold-out set validation.\n",
    "train_valid_split = 0.2\n",
    "\n",
    "# Fills the validation set \n",
    "subfolders = [f.path for f in os.scandir(training_dir) if f.is_dir() ] \n",
    "flag = True\n",
    "for subfold in subfolders:\n",
    "  head_tail = os.path.split(subfold)\n",
    "  _ = os.makedirs(os.path.join(valid_dir,head_tail[1],'img'), exist_ok=True)\n",
    "  list_files = [name for name in os.scandir(os.path.join(subfold,'img')) if os.path.isfile(name)]\n",
    "  num_files = len(list_files)\n",
    "  if flag:\n",
    "     rand = np.random.choice(num_files, int(num_files*train_valid_split) ,replace=False)\n",
    "     flag = False\n",
    "  for num in rand:\n",
    "    file_name = os.path.split(list_files[num])\n",
    "    os.rename(list_files[num],os.path.join(valid_dir,head_tail[1],'img',file_name[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k3Bl6fIhBbBv"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "dataset_split = {}\n",
    "training_list = []\n",
    "list_files = [name for name in os.scandir(os.path.join(training_dir,'images','img')) if os.path.isfile(name)]\n",
    "for f in list_files:\n",
    "  file_name = os.path.split(f)\n",
    "  training_list.append(file_name[1])\n",
    "valid_list = []\n",
    "list_files = [name for name in os.scandir(os.path.join(valid_dir,'images','img')) if os.path.isfile(name)]\n",
    "for f in list_files:\n",
    "  file_name = os.path.split(f)\n",
    "  valid_list.append(file_name[1])\n",
    "\n",
    "dataset_split['training'] = training_list\n",
    "dataset_split['validation'] = valid_list\n",
    "\n",
    "with open('dataset_split.json', 'w') as fp:\n",
    "     json.dump(dataset_split, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6_GnaMdJ0pnX"
   },
   "source": [
    "# **Training and Validation Generators and Datasets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wfO9kY5Reypf"
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   "outputs": [],
   "source": [
    "# ImageDataGenerator\n",
    "# ------------------\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications.vgg19 import preprocess_input\n",
    "\n",
    "apply_data_augmentation = True\n",
    "\n",
    "# Create training ImageDataGenerator object\n",
    "# We need two different generators for images and corresponding masks\n",
    "if apply_data_augmentation:\n",
    "    train_img_data_gen = ImageDataGenerator(rotation_range=10,\n",
    "                                            width_shift_range=10,\n",
    "                                            height_shift_range=10,\n",
    "                                            zoom_range=0.3,\n",
    "                                            horizontal_flip=True,\n",
    "                                            vertical_flip=True,\n",
    "                                            fill_mode='reflect',\n",
    "                                            rescale = 1./255,\n",
    "                                            preprocessing_function=preprocess_input) \n",
    "    train_mask_data_gen = ImageDataGenerator(rotation_range=10,\n",
    "                                             width_shift_range=10,\n",
    "                                             height_shift_range=10,\n",
    "                                             zoom_range=0.3,\n",
    "                                             horizontal_flip=True,\n",
    "                                             vertical_flip=True,\n",
    "                                             fill_mode='reflect',\n",
    "                                             rescale=1./255) \n",
    "# Use rescale=1./255 in the mask ImageDataGenerator to obtain binary values {0, 1} as target.\n",
    "else:\n",
    "    train_img_data_gen = ImageDataGenerator()\n",
    "    train_mask_data_gen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# Create validation and test ImageDataGenerator objects\n",
    "valid_img_data_gen = ImageDataGenerator(rescale = 1./255,preprocessing_function=preprocess_input)\n",
    "valid_mask_data_gen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "C_K_GvGLgYKk",
    "outputId": "9b189ddd-896e-4923-d46a-b8eb7d027cf2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 6118 images belonging to 1 classes.\n",
      "Found 6118 images belonging to 1 classes.\n",
      "Found 1529 images belonging to 1 classes.\n",
      "Found 1529 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "# Create generators to read images from dataset directory\n",
    "# -------------------------------------------------------\n",
    "dataset_dir = os.path.join(cwd, 'Segmentation_Dataset')\n",
    "\n",
    "# Batch size\n",
    "bs = 24\n",
    "\n",
    "# img shape\n",
    "img_h = 256\n",
    "img_w = 256\n",
    "\n",
    "# Training\n",
    "# Two different generators for images and masks\n",
    "# ATTENTION: here the seed is important!! We have to give the same SEED to both the generator\n",
    "# to apply the same transformations/shuffling to images and corresponding masks\n",
    "training_dir = os.path.join(dataset_dir, 'training')\n",
    "train_img_gen = train_img_data_gen.flow_from_directory(os.path.join(training_dir, 'images'),\n",
    "                                                       target_size=(img_h, img_w),\n",
    "                                                       batch_size=bs, \n",
    "                                                       class_mode=None, # Because we have no class subfolders in this case\n",
    "                                                       shuffle=True,\n",
    "                                                       interpolation='bilinear',\n",
    "                                                       seed=SEED)  \n",
    "train_mask_gen = train_mask_data_gen.flow_from_directory(os.path.join(training_dir, 'masks'),\n",
    "                                                         target_size=(img_h, img_w),\n",
    "                                                         batch_size=bs,\n",
    "                                                         color_mode = 'grayscale',\n",
    "                                                         class_mode=None, # Because we have no class subfolders in this case\n",
    "                                                         shuffle=True,\n",
    "                                                         interpolation='bilinear',\n",
    "                                                         seed=SEED)\n",
    "train_gen = zip(train_img_gen, train_mask_gen)\n",
    "\n",
    "# Validation\n",
    "validation_dir = os.path.join(dataset_dir, 'validation')\n",
    "valid_img_gen = valid_img_data_gen.flow_from_directory(os.path.join(validation_dir, 'images'),\n",
    "                                                       target_size=(img_h, img_w),\n",
    "                                                       batch_size=bs, \n",
    "                                                       class_mode=None, # Because we have no class subfolders in this case\n",
    "                                                       shuffle=False,\n",
    "                                                       interpolation='bilinear',\n",
    "                                                       seed=SEED)\n",
    "valid_mask_gen = valid_mask_data_gen.flow_from_directory(os.path.join(validation_dir, 'masks'),\n",
    "                                                         target_size=(img_h, img_w),\n",
    "                                                         batch_size=bs, \n",
    "                                                         color_mode='grayscale',\n",
    "                                                         class_mode=None, # Because we have no class subfolders in this case\n",
    "                                                         shuffle=False,\n",
    "                                                         interpolation='bilinear',\n",
    "                                                         seed=SEED)\n",
    "valid_gen = zip(valid_img_gen, valid_mask_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ajpMINA1geCH"
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   "source": [
    "# Create Dataset objects\n",
    "# ----------------------\n",
    "\n",
    "# Training\n",
    "# --------\n",
    "train_dataset = tf.data.Dataset.from_generator(lambda: train_gen,\n",
    "                                               output_types=(tf.float32, tf.float32),\n",
    "                                               output_shapes=([None, img_h, img_w, 3], [None, img_h, img_w, 1]))\n",
    "\n",
    "# We cast the masks to integer first to have 0,1 values\n",
    "# Then back to float in order to manipulate them with functions that require float\n",
    "\n",
    "def prepare_target(x_, y_):\n",
    "    y_ = tf.cast(y_, tf.int32)\n",
    "    y_ = tf.cast(y_, tf.float32)\n",
    "    return x_, y_\n",
    "\n",
    "train_dataset = train_dataset.map(prepare_target)\n",
    "\n",
    "# Repeat\n",
    "train_dataset = train_dataset.repeat()\n",
    "\n",
    "# Validation\n",
    "# ----------\n",
    "valid_dataset = tf.data.Dataset.from_generator(lambda: valid_gen, \n",
    "                                               output_types=(tf.float32, tf.float32),\n",
    "                                               output_shapes=([None, img_h, img_w, 3], [None, img_h, img_w, 1]))\n",
    "valid_dataset = valid_dataset.map(prepare_target)\n",
    "\n",
    "# Repeat\n",
    "valid_dataset = valid_dataset.repeat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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     "height": 204
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    "outputId": "1d434de5-fbfa-48c3-ac54-02bce5f7da6e"
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   "outputs": [
    {
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kZDe1fuMq165coF5vYoRgZqbD8uLuis9vKWArCc1kRXnkLfXMHRC5f+mXfkr2U7zeuCPI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\ng8FgmEOMczcYDIY5xDh3g8FgmEN+Aq9DA1s7FNZkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing data generator\n",
    "# -------------------------\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "fig.show()\n",
    "\n",
    "# Assign a color to each class\n",
    "colors_dict = {}\n",
    "colors_dict[0] = [252, 186, 3]  # foreground\n",
    "colors_dict[1] = [0, 0, 0]  # background\n",
    "\n",
    "iterator = iter(train_dataset)\n",
    "\n",
    "for _ in range(100):\n",
    "    augmented_img, target = next(iterator)\n",
    "    augmented_img = augmented_img[0]   # First element\n",
    "    augmented_img = augmented_img * 255  # denormalize\n",
    "    \n",
    "    target = np.array(target[0, ..., 0])   # First element (squeezing channel dimension)\n",
    "    \n",
    "    # Assign colors for visualization\n",
    "    target_img = np.zeros([target.shape[0], target.shape[1], 3])\n",
    "    \n",
    "    target_img[np.where(target == 0)] = colors_dict[0]\n",
    "    target_img[np.where(target == 1)] = colors_dict[1]\n",
    "    \n",
    "    ax[0].imshow(np.uint8(augmented_img))\n",
    "    ax[1].imshow(np.uint8(target_img))\n",
    "    \n",
    "    fig.canvas.draw()\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lmCMGk9rgpli"
   },
   "source": [
    "# Model\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FuxTDa4kCXRP"
   },
   "source": [
    "## Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 935
    },
    "colab_type": "code",
    "id": "pn16g5XbuDx9",
    "outputId": "fc85e8df-4f0a-4acf-a43d-3524bd7d8ee7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "80142336/80134624 [==============================] - 2s 0us/step\n",
      "Model: \"vgg19\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 256, 256, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv4 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv4 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv4 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
      "=================================================================\n",
      "Total params: 20,024,384\n",
      "Trainable params: 20,024,384\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Base Model\n",
    "# -----------\n",
    "\n",
    "img_h = 256\n",
    "img_w = 256\n",
    "\n",
    "#Downloading model: For this task we decided to use vgg19\n",
    "#We do not include the fully connected layer\n",
    "\n",
    "base_model = tf.keras.applications.vgg19.VGG19(weights='imagenet', include_top=False, input_shape=(img_h, img_w, 3),pooling='None')\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l5r28iOcp0fR"
   },
   "outputs": [],
   "source": [
    "# Layers from which we are going to extract the output to use for the skip connections.\n",
    "# The names are extracted from the above summary of the downloaded model\n",
    "\n",
    "layer_names = [\n",
    "    'block1_conv2',   # 256x256\n",
    "    'block2_conv2',   # 128x128\n",
    "    'block3_conv4',   # 64x64\n",
    "    'block4_conv4',  # 32x32\n",
    "    'block5_conv4',  # 16x16\n",
    "    'block5_pool', #8x8\n",
    "    ]\n",
    "  \n",
    "layers = [base_model.get_layer(name).output for name in layer_names]\n",
    "\n",
    "# Create the feature extraction model\n",
    "down_stack = tf.keras.Model(inputs=base_model.input, outputs=layers)\n",
    "\n",
    "# Fine Tuning\n",
    "down_stack.trainable = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iFksLTLbCcsJ"
   },
   "source": [
    "## U-Net Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mDno0Xvu4rRq"
   },
   "outputs": [],
   "source": [
    "def unet_model(depth, start_f, num_classes, transpose):\n",
    "\n",
    "  inputs = tf.keras.layers.Input(shape=[img_h, img_w, 3])\n",
    "  ini = inputs\n",
    "\n",
    "  # Contracting Path: vgg19 model\n",
    "  skips = down_stack(ini)\n",
    "  latent = skips[-1] # Last output of contracting path\n",
    "  skips = skips[::-1] # Reverse outputs for use in expansive path\n",
    "\n",
    "  # Further convolutions on last output to increase channels\n",
    "  latent = tf.keras.layers.Conv2D(filters=start_f,\n",
    "                                         kernel_size=(3, 3),\n",
    "                                         strides=(1, 1),\n",
    "                                         padding='same',\n",
    "                                         activation = 'relu')(latent)\n",
    "  latent = tf.keras.layers.Conv2D(filters=start_f,\n",
    "                                         kernel_size=(3, 3),\n",
    "                                         strides=(1, 1),\n",
    "                                         padding='same',\n",
    "                                         activation = 'relu')(latent)\n",
    "                                        \n",
    "  start_f = start_f//2\n",
    "\n",
    "  # Expanding Path\n",
    "  # 1. Transpose Convolution or Upsampling\n",
    "  # 2. Concatenation\n",
    "  # 3. Batch Normalization\n",
    "  # 4. 2 Convolutions\n",
    "  for i in range(1,depth):\n",
    "    if transpose == True:\n",
    "      up = tf.keras.layers.Conv2DTranspose(filters=start_f,\n",
    "                                          kernel_size=(2, 2),\n",
    "                                          strides=(2, 2),\n",
    "                                          padding='same',\n",
    "                                          activation = 'relu')(latent)\n",
    "    if transpose == False:\n",
    "      up = tf.keras.layers.UpSampling2D(2, interpolation='bilinear')(latent)\n",
    "    up = tf.keras.layers.Concatenate()([up,skips[i]])\n",
    "    up = tf.keras.layers.BatchNormalization()(up)\n",
    "    latent = tf.keras.layers.Conv2D(filters=start_f,\n",
    "                                         kernel_size=(3, 3),\n",
    "                                         strides=(1, 1),\n",
    "                                         padding='same',\n",
    "                                         activation = 'relu')(up)\n",
    "    latent = tf.keras.layers.Conv2D(filters=start_f,\n",
    "                                         kernel_size=(3, 3),\n",
    "                                         strides=(1, 1),\n",
    "                                         padding='same',\n",
    "                                         activation = 'relu')(latent)                                     \n",
    "    if i != 1:\n",
    "      start_f = start_f // 2\n",
    "\n",
    "  # Final prediction layer, sigmoid activation function used\n",
    "                                         \n",
    "  prediction = tf.keras.layers.Conv2D(filters=num_classes,\n",
    "                                     kernel_size=(1, 1),\n",
    "                                     strides=(1, 1),\n",
    "                                     padding='same',\n",
    "                                     activation='sigmoid')(latent)\n",
    "\n",
    "  return tf.keras.Model(inputs=inputs, outputs=prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "6F7AhFgLg6k6",
    "outputId": "a69b6e06-d0f8-4fcb-85a5-b79e99720bee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            [(None, 256, 256, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "model (Model)                   [(None, 256, 256, 64 20024384    input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 8, 8, 1024)   4719616     model[3][5]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 8, 8, 1024)   9438208     conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_10 (Conv2DTran (None, 16, 16, 512)  2097664     conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_10 (Concatenate)    (None, 16, 16, 1024) 0           conv2d_transpose_10[0][0]        \n",
      "                                                                 model[3][4]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 16, 16, 1024) 4096        concatenate_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 16, 16, 512)  4719104     batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 16, 16, 512)  2359808     conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_11 (Conv2DTran (None, 32, 32, 512)  1049088     conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_11 (Concatenate)    (None, 32, 32, 1024) 0           conv2d_transpose_11[0][0]        \n",
      "                                                                 model[3][3]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 32, 32, 1024) 4096        concatenate_11[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 32, 32, 512)  4719104     batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 32, 32, 512)  2359808     conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_12 (Conv2DTran (None, 64, 64, 256)  524544      conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_12 (Concatenate)    (None, 64, 64, 512)  0           conv2d_transpose_12[0][0]        \n",
      "                                                                 model[3][2]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 64, 64, 512)  2048        concatenate_12[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 64, 64, 256)  1179904     batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 64, 64, 256)  590080      conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_13 (Conv2DTran (None, 128, 128, 128 131200      conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_13 (Concatenate)    (None, 128, 128, 256 0           conv2d_transpose_13[0][0]        \n",
      "                                                                 model[3][1]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 128, 128, 256 1024        concatenate_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 128, 128, 128 295040      batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 128, 128, 128 147584      conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_14 (Conv2DTran (None, 256, 256, 64) 32832       conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_14 (Concatenate)    (None, 256, 256, 128 0           conv2d_transpose_14[0][0]        \n",
      "                                                                 model[3][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 256, 256, 128 512         concatenate_14[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 256, 256, 64) 73792       batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 256, 256, 1)  65          conv2d_37[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 54,510,529\n",
      "Trainable params: 54,504,641\n",
      "Non-trainable params: 5,888\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = unet_model(depth=6, \n",
    "                     start_f=1024, \n",
    "                     num_classes=1,\n",
    "                   transpose=True)\n",
    "\n",
    "# Visualize created model as a table\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EqIT9VbMg8ow"
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nszLyaJCZT3g"
   },
   "source": [
    "## **Intersection of Union (IoU) Metric for Validation**\n",
    "We use the following metric to monitor the model performance while it is being trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-O55u7A8Ysdt"
   },
   "outputs": [],
   "source": [
    "def my_IoU(y_true, y_pred):\n",
    "    # from probability to predicted class {0, 1}\n",
    "    y_pred = tf.cast(y_pred > 0.5, tf.float32)\n",
    "    \n",
    "    \n",
    "    # A and B\n",
    "    intersection = tf.reduce_sum(y_true * y_pred)\n",
    "    # A or B\n",
    "    union = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) - intersection\n",
    "    # IoU\n",
    "    return intersection / union"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hYM2tmH2Zlv4"
   },
   "source": [
    "## **Optimization Parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6PmYqFKYhCui"
   },
   "outputs": [],
   "source": [
    "# Optimization params\n",
    "# -------------------\n",
    "\n",
    "# Loss\n",
    "# Binary Cross Entropy as we are using sigmoid activation for the final prediction layer\n",
    "loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n",
    "\n",
    "# Alternative loss function that is a combination of binary cross entropy and dice loss.\n",
    "# Taken from: Nieradzik. Losses for Image Segmentation - https://lars76.github.io/neural-networks/object-detection/losses-for-segmentation/\n",
    "def custom_loss(y_true, y_pred):\n",
    "    def dice_loss(y_true, y_pred):\n",
    "        numerator = 2. * tf.reduce_sum(y_true * y_pred, axis=(1,2,3))\n",
    "        denominator = tf.reduce_sum(y_true + y_pred, axis=(1,2,3))\n",
    "\n",
    "        return tf.reshape(1. - numerator / denominator, (-1, 1, 1))\n",
    "\n",
    "    return tf.keras.losses.binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)\n",
    "\n",
    "# learning rate\n",
    "lr = 1e-3\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=lr)\n",
    "# -------------------\n",
    "\n",
    "# Validation metrics\n",
    "# ------------------\n",
    "metrics = [my_IoU]\n",
    "# ------------------\n",
    "\n",
    "# Compile Model\n",
    "model.compile(optimizer=optimizer, loss=loss, metrics=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "llwsIT-ihGou"
   },
   "source": [
    "## **Training with callbacks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 714
    },
    "colab_type": "code",
    "id": "rzguHxP_hFm9",
    "outputId": "78db6faf-1a67-4de5-f782-af9af96fe6fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 255 steps, validate for 64 steps\n",
      "Epoch 1/20\n",
      "255/255 [==============================] - 202s 790ms/step - loss: 0.2577 - my_IoU: 0.6977 - val_loss: 22.2748 - val_my_IoU: 0.7017\n",
      "Epoch 2/20\n",
      "255/255 [==============================] - 199s 781ms/step - loss: 0.2407 - my_IoU: 0.7172 - val_loss: 0.2451 - val_my_IoU: 0.7266\n",
      "Epoch 3/20\n",
      "255/255 [==============================] - 199s 781ms/step - loss: 0.2360 - my_IoU: 0.7234 - val_loss: 0.2411 - val_my_IoU: 0.7143\n",
      "Epoch 4/20\n",
      "255/255 [==============================] - 197s 772ms/step - loss: 0.2339 - my_IoU: 0.7221 - val_loss: 0.2499 - val_my_IoU: 0.7206\n",
      "Epoch 5/20\n",
      "255/255 [==============================] - 199s 779ms/step - loss: 0.2256 - my_IoU: 0.7312 - val_loss: 0.2329 - val_my_IoU: 0.7229\n",
      "Epoch 6/20\n",
      "255/255 [==============================] - 197s 772ms/step - loss: 0.2252 - my_IoU: 0.7327 - val_loss: 0.2666 - val_my_IoU: 0.6745\n",
      "Epoch 7/20\n",
      "255/255 [==============================] - 199s 782ms/step - loss: 0.2221 - my_IoU: 0.7388 - val_loss: 0.2289 - val_my_IoU: 0.7341\n",
      "Epoch 8/20\n",
      "255/255 [==============================] - 197s 772ms/step - loss: 0.2210 - my_IoU: 0.7372 - val_loss: 0.2612 - val_my_IoU: 0.7425\n",
      "Epoch 9/20\n",
      "255/255 [==============================] - 199s 781ms/step - loss: 0.2204 - my_IoU: 0.7382 - val_loss: 0.2236 - val_my_IoU: 0.7355\n",
      "Epoch 10/20\n",
      "255/255 [==============================] - 199s 781ms/step - loss: 0.2198 - my_IoU: 0.7375 - val_loss: 0.2123 - val_my_IoU: 0.7533\n",
      "Epoch 11/20\n",
      "255/255 [==============================] - 197s 772ms/step - loss: 0.2189 - my_IoU: 0.7444 - val_loss: 0.2258 - val_my_IoU: 0.7334\n",
      "Epoch 12/20\n",
      "255/255 [==============================] - 201s 788ms/step - loss: 0.2089 - my_IoU: 0.7510 - val_loss: 0.2101 - val_my_IoU: 0.7539\n",
      "Epoch 13/20\n",
      "255/255 [==============================] - 197s 773ms/step - loss: 0.2159 - my_IoU: 0.7482 - val_loss: 0.2139 - val_my_IoU: 0.7482\n",
      "Epoch 14/20\n",
      "255/255 [==============================] - 197s 773ms/step - loss: 0.2097 - my_IoU: 0.7503 - val_loss: 0.2378 - val_my_IoU: 0.7312\n",
      "Epoch 15/20\n",
      "255/255 [==============================] - 197s 774ms/step - loss: 0.2131 - my_IoU: 0.7480 - val_loss: 0.2297 - val_my_IoU: 0.7410\n",
      "Epoch 16/20\n",
      "255/255 [==============================] - 197s 774ms/step - loss: 0.2144 - my_IoU: 0.7461 - val_loss: 0.2410 - val_my_IoU: 0.7262\n",
      "Epoch 17/20\n",
      "255/255 [==============================] - 197s 773ms/step - loss: 0.2130 - my_IoU: 0.7481 - val_loss: 0.4070 - val_my_IoU: 0.7362\n",
      "Epoch 18/20\n",
      "255/255 [==============================] - 197s 774ms/step - loss: 0.2077 - my_IoU: 0.7554 - val_loss: 0.2557 - val_my_IoU: 0.7226\n",
      "Epoch 19/20\n",
      "255/255 [==============================] - 197s 774ms/step - loss: 0.2118 - my_IoU: 0.7481 - val_loss: 0.2255 - val_my_IoU: 0.7375\n",
      "Epoch 20/20\n",
      "255/255 [==============================] - 197s 773ms/step - loss: 0.2094 - my_IoU: 0.7515 - val_loss: 0.2426 - val_my_IoU: 0.7306\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from datetime import datetime\n",
    "\n",
    "cwd = os.getcwd()\n",
    "\n",
    "exps_dir = os.path.join(cwd, 'segmentation_experiments')\n",
    "if not os.path.exists(exps_dir):\n",
    "    os.makedirs(exps_dir)\n",
    "\n",
    "now = datetime.now().strftime('%b%d_%H-%M-%S')\n",
    "\n",
    "model_name = 'CNN'\n",
    "\n",
    "exp_dir = os.path.join(exps_dir, model_name + '_' + str(now))\n",
    "if not os.path.exists(exp_dir):\n",
    "    os.makedirs(exp_dir)\n",
    "    \n",
    "callbacks = []\n",
    "\n",
    "#Model checkpoint\n",
    "# ----------------\n",
    "ckpt_dir = os.path.join(exp_dir, 'ckpts')\n",
    "if not os.path.exists(ckpt_dir):\n",
    "    os.makedirs(ckpt_dir)\n",
    "\n",
    "ckpt_callback = tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(ckpt_dir, 'cp_{epoch:02d}.ckpt'), \n",
    "                                                   save_weights_only=True, save_best_only=True, mode='min')  # False to save the model directly\n",
    "callbacks.append(ckpt_callback)\n",
    "\n",
    "\n",
    "# Early Stopping\n",
    "# --------------\n",
    "early_stop = False\n",
    "if early_stop:\n",
    "    es_callbacks = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "    callbacks.append(es_callbacks)\n",
    "\n",
    "\n",
    "model_history = model.fit(x=train_dataset,\n",
    "          epochs=50,  #### set repeat in training dataset\n",
    "          steps_per_epoch=len(train_img_gen),\n",
    "          validation_data=valid_dataset,\n",
    "          validation_steps=len(valid_img_gen), \n",
    "          callbacks=callbacks)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "420DNIeu8bcU"
   },
   "source": [
    "## Visualizing Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "TpxpMTJbxNwq",
    "outputId": "638f7d22-f48f-477b-caa7-53a54427f0e7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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96N276v4QQhk3rjAI+B0+7ObHi+VITKBzzoEPPnDDT8dD//6wfTusTfoBcMov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\na5GChzkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = model_history.history['loss']\n",
    "val_loss = model_history.history['val_loss']\n",
    "\n",
    "epochs = range(20)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'r', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'bo--', label='Validation loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss Value')\n",
    "plt.ylim([0, 0.5])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vEgYNYaV_5tn"
   },
   "source": [
    "## Loading Best Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_ZplKatG_112"
   },
   "outputs": [],
   "source": [
    "model.load_weights(os.path.join(\n",
    "        cwd, 'segmentation_experiments', 'CNN_Dec14_11-54-06', 'ckpts', 'cp_10.ckpt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2_LLXg-rhOQJ"
   },
   "source": [
    "# **Testing Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "noYmm69raRNg",
    "outputId": "d105af34-9ede-412a-9ca7-183f9d23f236"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C7uKEFXb/4Hs0qkuMDPYx0NtFWymPgkTVTexQYEcaVU/BiQRVx2e56fLNF17m8InTmJkM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4GmSm43yiHaucSVC69y+tRJvv+pJ5FjgbNKxKu6VlWDfFIaYrK/sb6+zvT0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\nZQ28P996NScAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing Image----------------------------------------------------------------------------\n",
    "from PIL import Image\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "test_dir = os.path.join(dataset_dir, 'test','images','img') #Path of our test images. \n",
    "\n",
    "img_filenames = next(os.walk(test_dir,'images','img'))[2] #Array of all test image files.\n",
    "# Testing Zone--------------------------------------------------------------------------\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "fig.show()\n",
    "\n",
    "# Assign a color to each class\n",
    "colors_dict = {}\n",
    "colors_dict[0] = [252, 186, 3]  # foreground\n",
    "colors_dict[1] = [0, 0, 0]      # background\n",
    "\n",
    "results = {}\n",
    "\n",
    "img_filename = img_filenames[0]\n",
    "    \n",
    "img = Image.open(os.path.join(test_dir, img_filename))\n",
    "img = img.resize((256, 256))    \n",
    "img_arr = np.expand_dims(np.array(img), 0)\n",
    "    \n",
    "\n",
    "img_arr = preprocess_input(img_arr)\n",
    "    \n",
    "predicted_class = model.predict(x=img_arr / 255.)\n",
    "\n",
    "predicted_class = tf.cast(predicted_class> 0.5, tf.float32)\n",
    "predicted_class = predicted_class[0]\n",
    "\n",
    "\n",
    "target = np.array(predicted_class)\n",
    "target = np.squeeze(target, axis=-1)\n",
    "\n",
    "    \n",
    "# Assign colors for visualization\n",
    "target_img = np.zeros([target.shape[0], target.shape[1], 3])  \n",
    "target_img[np.where(target == 0)] = colors_dict[0]\n",
    "target_img[np.where(target == 1)] = colors_dict[1]\n",
    "ax[0].imshow(np.uint8(img))\n",
    "ax[1].imshow(np.uint8(target_img))\n",
    "    \n",
    "fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "p7tO0Cj88-5I"
   },
   "source": [
    "# Further Training of Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uc-OVSdB9SVb"
   },
   "source": [
    "## Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MaxNqzGAtLlq"
   },
   "outputs": [],
   "source": [
    "model.save('transfer_segmentation_fine_tuning.h5') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YOkIoNPa9abr"
   },
   "source": [
    "## Loading Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dbqgEGCFwVvn"
   },
   "outputs": [],
   "source": [
    "loaded_model = tf.keras.models.load_model('transfer_segmentation_fine_tuning2.h5',custom_objects={'my_IoU': my_IoU})\n",
    "loaded_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6ZNNy5nP-Rxx"
   },
   "source": [
    "## Further Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VdGmp_Gq6zMh"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from datetime import datetime\n",
    "\n",
    "cwd = os.getcwd()\n",
    "\n",
    "exps_dir = os.path.join(cwd, 'segmentation_experiments')\n",
    "if not os.path.exists(exps_dir):\n",
    "    os.makedirs(exps_dir)\n",
    "\n",
    "now = datetime.now().strftime('%b%d_%H-%M-%S')\n",
    "\n",
    "model_name = 'CNN'\n",
    "\n",
    "exp_dir = os.path.join(exps_dir, model_name + '_' + str(now))\n",
    "if not os.path.exists(exp_dir):\n",
    "    os.makedirs(exp_dir)\n",
    "    \n",
    "callbacks = []\n",
    "\n",
    " #Model checkpoint\n",
    "# ----------------\n",
    "ckpt_dir = os.path.join(exp_dir, 'ckpts')\n",
    "if not os.path.exists(ckpt_dir):\n",
    "    os.makedirs(ckpt_dir)\n",
    "\n",
    "ckpt_callback = tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(ckpt_dir, 'cp_{epoch:02d}.ckpt'), \n",
    "                                                   save_weights_only=True, save_best_only=True, mode='min')  # False to save the model directly\n",
    "callbacks.append(ckpt_callback)\n",
    "\n",
    "\n",
    "# Early Stopping\n",
    "# --------------\n",
    "early_stop = False\n",
    "if early_stop:\n",
    "    es_callbacks = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "    callbacks.append(es_callbacks)\n",
    "\n",
    "\n",
    "loaded_ model_history= loaded_model.fit(x=train_dataset,\n",
    "                                        epochs=20,  #### set repeat in training dataset\n",
    "                                        steps_per_epoch=len(train_img_gen),\n",
    "                                        validation_data=valid_dataset, \n",
    "                                        validation_steps=len(valid_img_gen), \n",
    "                                        callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DWktii5i_YKW"
   },
   "source": [
    "## Visualizing Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1mGHdNTz_Iaa"
   },
   "outputs": [],
   "source": [
    "loss = loaded_model_history.history['loss']\n",
    "val_loss = loaded_model_history.history['val_loss']\n",
    "\n",
    "epochs = range(20)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'r', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'bo--', label='Validation loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss Value')\n",
    "plt.ylim([0, 0.5])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pxpAma6V_m_K"
   },
   "source": [
    "## Loading Best Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hi4FX-epWsLG"
   },
   "outputs": [],
   "source": [
    "loaded_model.load_weights(os.path.join(\n",
    "        cwd, 'segmentation_experiments', 'CNN_Dec14_11-54-06', 'ckpts', 'cp_10.ckpt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gVL6YvwjQc37"
   },
   "source": [
    "# **Output of the Solution**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U3TkVukkQgpz"
   },
   "source": [
    "## **CSV Creation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WrVH5cO-Qlbh"
   },
   "outputs": [],
   "source": [
    "def create_csv(results, results_dir='./'):\n",
    "\n",
    "    csv_fname = 'results_'\n",
    "    csv_fname += datetime.now().strftime('%b%d_%H-%M-%S') + '.csv'\n",
    "\n",
    "    with open(csv_fname, 'w') as f:\n",
    "\n",
    "      f.write('ImageId,EncodedPixels,Width,Height\\n')\n",
    "\n",
    "      for key, value in results.items():\n",
    "          f.write(key + ',' + str(value) + ',' + '256' + ',' + '256' + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JcILX29RRTlE"
   },
   "source": [
    "##**Encode Prediction Masks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yv3vZolLoIlp"
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import time\n",
    "\n",
    "def rle_encode(img): #Input img is the predicted mask with 0 and 1 values.\n",
    "      # Flatten column-wise\n",
    "      pixels = img.T.flatten()\n",
    "      pixels = np.concatenate([[0], pixels, [0]])\n",
    "      runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n",
    "      runs[1::2] -= runs[::2]\n",
    "      return ' '.join(str(x) for x in runs)\n",
    "\n",
    "\n",
    "test_dir = os.path.join(dataset_dir, 'test','images','img') #Path of our test images. \n",
    "\n",
    "image_filenames = next(os.walk(test_dir))[2] #Array of all test image files.\n",
    "\n",
    "results = {}\n",
    "for img_filename in image_filenames:\n",
    "    \n",
    "    mask_filename = img_filename[:-4]\n",
    "    \n",
    "    img = Image.open(os.path.join(test_dir, img_filename))\n",
    "    img = img.resize((256, 256))\n",
    "    \n",
    "    img_arr = np.expand_dims(np.array(img), 0)\n",
    "\n",
    "    img_arr = preprocess_input(img_arr)\n",
    "    \n",
    "    predicted_class = model.predict(x=img_arr / 255.)\n",
    "\n",
    "    predicted_class = tf.cast(predicted_class> 0.5, tf.float32)\n",
    "    predicted_class = predicted_class[0]\n",
    "\n",
    "    target = np.array(predicted_class)\n",
    "    results[mask_filename] = rle_encode(target)\n",
    "\n",
    "create_csv(results)"
   ]
  }
 ],
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