{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c831b6f8-79ef-4d1b-b963-47422c056731",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Install the necessary dependencies\n",
    "\n",
    "import os\n",
    "import sys\n",
    "!{sys.executable} -m pip install --quiet seaborn pandas scikit-learn numpy matplotlib jupyterlab_myst ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e62993e",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "---\n",
    "license:\n",
    "    code: MIT\n",
    "    content: CC-BY-4.0\n",
    "github: https://github.com/ocademy-ai/machine-learning\n",
    "venue: By Ocademy\n",
    "open_access: true\n",
    "bibliography:\n",
    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfee6a78",
   "metadata": {},
   "source": [
    "#  Image segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eb70ed9",
   "metadata": {},
   "source": [
    "## What is Image segmentation?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4556a6ff",
   "metadata": {},
   "source": [
    "In an image segmentation task (specifically semantic segmentation), the network assigns a label (or class) to each input image. However, suppose you want to know the shape of that object, which pixel belongs to which object, etc. In this case, you need to assign a class to each pixel of the image—this task is known as segmentation. A segmentation model returns much more detailed information about the image. Image segmentation has many applications in medical imaging, self-driving cars and satellite imaging, just to name a few."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0bc7ea14",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<p style=\"text-align: center;\">\n",
       "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/image-segmentation/index.html\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
       "A demo of image segmentation 1. <a href=\"https://generic-github-user.github.io/Image-Convolution-Playground/src/\">[source]</a>\n",
       "</p>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "display(\n",
    "    HTML(\n",
    "        \"\"\"\n",
    "<p style=\"text-align: center;\">\n",
    "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/image-segmentation/index.html\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
    "A demo of image segmentation 1. <a href=\"https://generic-github-user.github.io/Image-Convolution-Playground/src/\">[source]</a>\n",
    "</p>\n",
    "\"\"\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "479cbe5b",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<p style=\"text-align: center;\">\n",
       "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/image-segmentation-2/index.html\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
       "A demo of image segmentation 2. <a href=\"https://github.com/selvam85/deeplab\">[source]</a>\n",
       "</p>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "display(\n",
    "    HTML(\n",
    "        \"\"\"\n",
    "<p style=\"text-align: center;\">\n",
    "<iframe src=\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/image-segmentation-2/index.html\" width=\"105%\" height=\"700px;\" style=\"border:none;\"></iframe>\n",
    "A demo of image segmentation 2. <a href=\"https://github.com/selvam85/deeplab\">[source]</a>\n",
    "</p>\n",
    "\"\"\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c12424",
   "metadata": {},
   "source": [
    "## How to train a model for image segmentation?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87abaf96",
   "metadata": {},
   "source": [
    "This tutorial uses the Oxford-IIIT Pet Dataset. The dataset consists of images of 37 pet breeds, with 200 images per breed (~100 each in the training and test splits). Each image includes the corresponding labels, and pixel-wise masks. The masks are class-labels for each pixel. Each pixel is given one of three categories:\n",
    "\n",
    "- Class 1: Pixel belonging to the pet.\n",
    "- Class 2: Pixel bordering the pet.\n",
    "- Class 3: None of the above/a surrounding pixel.\n",
    "\n",
    "And it can be downloaded from [The Oxford-IIIT Pet Dataset](https://academictorrents.com/details/b18bbd9ba03d50b0f7f479acc9f4228a408cecc1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "543eb431",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import tensorflow_datasets as tfds\n",
    "\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ce6010",
   "metadata": {},
   "source": [
    "### Prepare the Oxford-IIIT Pets dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3013b20e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset, info = tfds.load(\"oxford_iiit_pet:3.*.*\", with_info=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "620b3181",
   "metadata": {},
   "source": [
    "In addition, the image color values are normalized to the range [0, 1]. Finally, as mentioned above the pixels in the segmentation mask are labeled either {1, 2, 3}. For the sake of convenience, subtract 1 from the segmentation mask, resulting in labels that are : {0, 1, 2}."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9fc90e7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def normalize(input_image, input_mask):\n",
    "    input_image = tf.cast(input_image, tf.float32) / 255.0\n",
    "    input_mask -= 1\n",
    "    return input_image, input_mask\n",
    "\n",
    "\n",
    "def load_image(datapoint):\n",
    "    input_image = tf.image.resize(datapoint[\"image\"], (128, 128))\n",
    "    input_mask = tf.image.resize(\n",
    "        datapoint[\"segmentation_mask\"],\n",
    "        (128, 128),\n",
    "        method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,\n",
    "    )\n",
    "\n",
    "    input_image, input_mask = normalize(input_image, input_mask)\n",
    "\n",
    "    return input_image, input_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0389a2e1",
   "metadata": {},
   "source": [
    "The dataset already contains the required training and test splits, so continue to use the same splits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c333e38f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "TRAIN_LENGTH = info.splits[\"train\"].num_examples\n",
    "BATCH_SIZE = 64\n",
    "BUFFER_SIZE = 1000\n",
    "STEPS_PER_EPOCH = TRAIN_LENGTH // BATCH_SIZE\n",
    "train_images = dataset[\"train\"].map(load_image, num_parallel_calls=tf.data.AUTOTUNE)\n",
    "test_images = dataset[\"test\"].map(load_image, num_parallel_calls=tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "863b8813",
   "metadata": {},
   "source": [
    "The following class performs a simple augmentation by randomly-flipping an image. Go to the Image augmentation tutorial to learn more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "18ec715e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Augment(tf.keras.layers.Layer):\n",
    "    def __init__(self, seed=42):\n",
    "        super().__init__()\n",
    "        # both use the same seed, so they'll make the same random changes.\n",
    "        self.augment_inputs = tf.keras.layers.RandomFlip(mode=\"horizontal\", seed=seed)\n",
    "        self.augment_labels = tf.keras.layers.RandomFlip(mode=\"horizontal\", seed=seed)\n",
    "\n",
    "    def call(self, inputs, labels):\n",
    "        inputs = self.augment_inputs(inputs)\n",
    "        labels = self.augment_labels(labels)\n",
    "        return inputs, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ca9679f",
   "metadata": {},
   "source": [
    "Build the input pipeline, applying the augmentation after batching the inputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "257dd4fd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_batches = (\n",
    "    train_images.cache()\n",
    "    .shuffle(BUFFER_SIZE)\n",
    "    .batch(BATCH_SIZE)\n",
    "    .repeat()\n",
    "    .map(Augment())\n",
    "    .prefetch(buffer_size=tf.data.AUTOTUNE)\n",
    ")\n",
    "\n",
    "test_batches = test_images.batch(BATCH_SIZE)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "276f1976",
   "metadata": {},
   "source": [
    "Visualize an image example and its corresponding mask from the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "337a3ade-b648-4a53-8139-b9128cefdf4c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display(display_list):\n",
    "    plt.figure(figsize=(15, 15))\n",
    "\n",
    "    title = [\"Input Image\", \"True Mask\", \"Predicted Mask\"]\n",
    "\n",
    "    for i in range(len(display_list)):\n",
    "        plt.subplot(1, len(display_list), i + 1)\n",
    "        plt.title(title[i])\n",
    "        plt.imshow(tf.keras.utils.array_to_img(display_list[i]))\n",
    "        plt.axis(\"off\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "for images, masks in train_batches.take(2):\n",
    "    sample_image, sample_mask = images[0], masks[0]\n",
    "    display([sample_image, sample_mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75537524",
   "metadata": {},
   "source": [
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/01_display_dataset.png\n",
    "---\n",
    "name: '01_dataset_example_mask' \n",
    "width: 90%\n",
    "---\n",
    "An example from the dataset\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e667c5",
   "metadata": {},
   "source": [
    "### Define the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f35f9de",
   "metadata": {},
   "source": [
    "The model being used here is a modified U-Net. A U-Net consists of an encoder (downsampler) and decoder (upsampler). To learn robust features and reduce the number of trainable parameters, use a pretrained model—MobileNetV2—as the encoder. For the decoder, you will use the upsample block, which is already implemented in the pix2pix example in the TensorFlow Examples repo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742dcf22",
   "metadata": {},
   "source": [
    ":::{note}\n",
    "The link for paper is https://arxiv.org/pdf/1505.04597.pdf.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd227d99",
   "metadata": {},
   "source": [
    "As mentioned, the encoder is a pretrained MobileNetV2 model. You will use the model from tf.keras.applications. The encoder consists of specific outputs from intermediate layers in the model. Note that the encoder will not be trained during the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "08769272",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "base_model = tf.keras.applications.MobileNetV2(\n",
    "    input_shape=[128, 128, 3], include_top=False\n",
    ")\n",
    "\n",
    "# Use the activations of these layers\n",
    "layer_names = [\n",
    "    \"block_1_expand_relu\",  # 64x64\n",
    "    \"block_3_expand_relu\",  # 32x32\n",
    "    \"block_6_expand_relu\",  # 16x16\n",
    "    \"block_13_expand_relu\",  # 8x8\n",
    "    \"block_16_project\",  # 4x4\n",
    "]\n",
    "base_model_outputs = [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=base_model_outputs)\n",
    "\n",
    "down_stack.trainable = False\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7ce2a2",
   "metadata": {},
   "source": [
    "The decoder/upsampler is simply a series of upsample blocks implemented in TensorFlow examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69c5e34d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers\n",
    "\n",
    "\n",
    "def upsample(filters, size, apply_dropout=False):\n",
    "    initializer = tf.random_normal_initializer(0.0, 0.02)\n",
    "    result = tf.keras.Sequential()\n",
    "    result.add(\n",
    "        layers.Conv2DTranspose(\n",
    "            filters,\n",
    "            size,\n",
    "            strides=2,\n",
    "            padding=\"same\",\n",
    "            kernel_initializer=initializer,\n",
    "            use_bias=False,\n",
    "        )\n",
    "    )\n",
    "    result.add(layers.BatchNormalization())\n",
    "    if apply_dropout:\n",
    "        result.add(layers.Dropout(0.5))\n",
    "    result.add(layers.ReLU())\n",
    "    return result\n",
    "\n",
    "\n",
    "up_stack = [\n",
    "    upsample(512, 3),  # 4x4 -> 8x8\n",
    "    upsample(256, 3),  # 8x8 -> 16x16\n",
    "    upsample(128, 3),  # 16x16 -> 32x32\n",
    "    upsample(64, 3),  # 32x32 -> 64x64\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "abdf340c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def unet_model(output_channels: int):\n",
    "    inputs = tf.keras.layers.Input(shape=[128, 128, 3])\n",
    "\n",
    "    # Downsampling through the model\n",
    "    skips = down_stack(inputs)\n",
    "    x = skips[-1]\n",
    "    skips = reversed(skips[:-1])\n",
    "\n",
    "    # Upsampling and establishing the skip connections\n",
    "    for up, skip in zip(up_stack, skips):\n",
    "        x = up(x)\n",
    "        concat = tf.keras.layers.Concatenate()\n",
    "        x = concat([x, skip])\n",
    "\n",
    "    # This is the last layer of the model\n",
    "    last = tf.keras.layers.Conv2DTranspose(\n",
    "        filters=output_channels, kernel_size=3, strides=2, padding=\"same\"\n",
    "    )  # 64x64 -> 128x128\n",
    "\n",
    "    x = last(x)\n",
    "\n",
    "    return tf.keras.Model(inputs=inputs, outputs=x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f75a52",
   "metadata": {},
   "source": [
    "Note that the number of filters on the last layer is set to the number of it. This will be one output channel per class.output_channels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b16ba3a",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b089cc9",
   "metadata": {},
   "source": [
    "Now, all that is left to do is to compile and train the model. Since this is a multiclass classification problem, use the 'tf.keras.losses.CategoricalCrossentropy' loss function with the argument set to , since the labels are scalar integers instead of vectors of scores for each pixel of every class.from_logitsTrue. When running inference, the label assigned to the pixel is the channel with the highest value. This is what the function is doing.create_mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "20c83f6b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "OUTPUT_CLASSES = 3\n",
    "\n",
    "model = unet_model(output_channels=OUTPUT_CLASSES)\n",
    "model.compile(\n",
    "    optimizer=\"adam\",\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    metrics=[\"accuracy\"],\n",
    ")\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ab0185",
   "metadata": {},
   "source": [
    "Try out the model to check what it predicts before training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e13f2332",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_mask(pred_mask):\n",
    "    pred_mask = tf.math.argmax(pred_mask, axis=-1)\n",
    "    pred_mask = pred_mask[..., tf.newaxis]\n",
    "    return pred_mask[0]\n",
    "\n",
    "\n",
    "def show_predictions(dataset=None, num=1):\n",
    "    if dataset:\n",
    "        for image, mask in dataset.take(num):\n",
    "            pred_mask = model.predict(image)\n",
    "            display([image[0], mask[0], create_mask(pred_mask)])\n",
    "    else:\n",
    "        display(\n",
    "            [\n",
    "                sample_image,\n",
    "                sample_mask,\n",
    "                create_mask(model.predict(sample_image[tf.newaxis, ...])),\n",
    "            ]\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "528aa0c5",
   "metadata": {},
   "source": [
    "Perhaps now we can try to show the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "296c64df",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 51ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(128, 128, 3), dtype=float32, numpy=\n",
       " array([[[0.11404718, 0.13299297, 0.06537655],\n",
       "         [0.10460062, 0.11903076, 0.06525736],\n",
       "         [0.08964461, 0.11171325, 0.0555621 ],\n",
       "         ...,\n",
       "         [0.0688029 , 0.1292794 , 0.03267463],\n",
       "         [0.06164216, 0.13725491, 0.03026961],\n",
       "         [0.10509942, 0.17176609, 0.0447134 ]],\n",
       " \n",
       "        [[0.11912291, 0.13436471, 0.06040135],\n",
       "         [0.10782758, 0.11567072, 0.06227022],\n",
       "         [0.08925183, 0.10601041, 0.05068934],\n",
       "         ...,\n",
       "         [0.05174632, 0.11988789, 0.02060786],\n",
       "         [0.04015012, 0.10651042, 0.02277928],\n",
       "         [0.06948027, 0.12280441, 0.03265429]],\n",
       " \n",
       "        [[0.09274592, 0.12934355, 0.05409845],\n",
       "         [0.10449817, 0.11234131, 0.05743935],\n",
       "         [0.08157576, 0.10613463, 0.04915341],\n",
       "         ...,\n",
       "         [0.02356819, 0.05816076, 0.00605086],\n",
       "         [0.03669362, 0.09913234, 0.02744117],\n",
       "         [0.0348944 , 0.08588317, 0.01743954]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0.21351941, 0.08421487, 0.05667198],\n",
       "         [0.19903924, 0.09312624, 0.06556804],\n",
       "         [0.20899203, 0.08946078, 0.06870405],\n",
       "         ...,\n",
       "         [0.1874428 , 0.18851821, 0.07555147],\n",
       "         [0.17259929, 0.19464949, 0.08206309],\n",
       "         [0.18781619, 0.17865972, 0.06704963]],\n",
       " \n",
       "        [[0.20663297, 0.08898591, 0.05504988],\n",
       "         [0.20439573, 0.09592476, 0.05581988],\n",
       "         [0.21836272, 0.09207596, 0.0638174 ],\n",
       "         ...,\n",
       "         [0.18036583, 0.19105823, 0.06886704],\n",
       "         [0.17238052, 0.18714815, 0.07394277],\n",
       "         [0.15900736, 0.17111912, 0.0677799 ]],\n",
       " \n",
       "        [[0.21384971, 0.10061107, 0.05919117],\n",
       "         [0.21271446, 0.10134804, 0.05899204],\n",
       "         [0.25641564, 0.12936294, 0.08377614],\n",
       "         ...,\n",
       "         [0.17176777, 0.19460641, 0.07534395],\n",
       "         [0.17725185, 0.18717328, 0.07554621],\n",
       "         [0.17233455, 0.18175551, 0.07665441]]], dtype=float32)>,\n",
       " <tf.Tensor: shape=(128, 128, 1), dtype=float32, numpy=\n",
       " array([[[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
       "         ...,\n",
       "         [1.],\n",
       "         [1.],\n",
       "         [1.]],\n",
       " \n",
       "        [[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
       "         ...,\n",
       "         [1.],\n",
       "         [1.],\n",
       "         [1.]],\n",
       " \n",
       "        [[1.],\n",
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       "         [1.],\n",
       "         ...,\n",
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       " \n",
       "        ...,\n",
       " \n",
       "        [[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
       "         ...,\n",
       "         [1.],\n",
       "         [1.],\n",
       "         [1.]],\n",
       " \n",
       "        [[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
       "         ...,\n",
       "         [1.],\n",
       "         [1.],\n",
       "         [1.]],\n",
       " \n",
       "        [[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
       "         ...,\n",
       "         [1.],\n",
       "         [1.],\n",
       "         [1.]]], dtype=float32)>,\n",
       " <tf.Tensor: shape=(128, 128, 1), dtype=int64, numpy=\n",
       " array([[[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]],\n",
       " \n",
       "        [[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]],\n",
       " \n",
       "        [[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]],\n",
       " \n",
       "        [[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]],\n",
       " \n",
       "        [[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]]], dtype=int64)>]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for images, masks in train_batches.take(1):\n",
    "    sample_image, sample_mask = images[0], masks[0]\n",
    "show_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e3d94b1",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 77ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(128, 128, 3), dtype=float32, numpy=\n",
       " array([[[0.11404718, 0.13299297, 0.06537655],\n",
       "         [0.10460062, 0.11903076, 0.06525736],\n",
       "         [0.08964461, 0.11171325, 0.0555621 ],\n",
       "         ...,\n",
       "         [0.0688029 , 0.1292794 , 0.03267463],\n",
       "         [0.06164216, 0.13725491, 0.03026961],\n",
       "         [0.10509942, 0.17176609, 0.0447134 ]],\n",
       " \n",
       "        [[0.11912291, 0.13436471, 0.06040135],\n",
       "         [0.10782758, 0.11567072, 0.06227022],\n",
       "         [0.08925183, 0.10601041, 0.05068934],\n",
       "         ...,\n",
       "         [0.05174632, 0.11988789, 0.02060786],\n",
       "         [0.04015012, 0.10651042, 0.02277928],\n",
       "         [0.06948027, 0.12280441, 0.03265429]],\n",
       " \n",
       "        [[0.09274592, 0.12934355, 0.05409845],\n",
       "         [0.10449817, 0.11234131, 0.05743935],\n",
       "         [0.08157576, 0.10613463, 0.04915341],\n",
       "         ...,\n",
       "         [0.02356819, 0.05816076, 0.00605086],\n",
       "         [0.03669362, 0.09913234, 0.02744117],\n",
       "         [0.0348944 , 0.08588317, 0.01743954]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[0.21351941, 0.08421487, 0.05667198],\n",
       "         [0.19903924, 0.09312624, 0.06556804],\n",
       "         [0.20899203, 0.08946078, 0.06870405],\n",
       "         ...,\n",
       "         [0.1874428 , 0.18851821, 0.07555147],\n",
       "         [0.17259929, 0.19464949, 0.08206309],\n",
       "         [0.18781619, 0.17865972, 0.06704963]],\n",
       " \n",
       "        [[0.20663297, 0.08898591, 0.05504988],\n",
       "         [0.20439573, 0.09592476, 0.05581988],\n",
       "         [0.21836272, 0.09207596, 0.0638174 ],\n",
       "         ...,\n",
       "         [0.18036583, 0.19105823, 0.06886704],\n",
       "         [0.17238052, 0.18714815, 0.07394277],\n",
       "         [0.15900736, 0.17111912, 0.0677799 ]],\n",
       " \n",
       "        [[0.21384971, 0.10061107, 0.05919117],\n",
       "         [0.21271446, 0.10134804, 0.05899204],\n",
       "         [0.25641564, 0.12936294, 0.08377614],\n",
       "         ...,\n",
       "         [0.17176777, 0.19460641, 0.07534395],\n",
       "         [0.17725185, 0.18717328, 0.07554621],\n",
       "         [0.17233455, 0.18175551, 0.07665441]]], dtype=float32)>,\n",
       " <tf.Tensor: shape=(128, 128, 1), dtype=float32, numpy=\n",
       " array([[[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
       "         ...,\n",
       "         [1.],\n",
       "         [1.],\n",
       "         [1.]],\n",
       " \n",
       "        [[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
       "         ...,\n",
       "         [1.],\n",
       "         [1.],\n",
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       " \n",
       "        [[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
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       " \n",
       "        ...,\n",
       " \n",
       "        [[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
       "         ...,\n",
       "         [1.],\n",
       "         [1.],\n",
       "         [1.]],\n",
       " \n",
       "        [[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
       "         ...,\n",
       "         [1.],\n",
       "         [1.],\n",
       "         [1.]],\n",
       " \n",
       "        [[1.],\n",
       "         [1.],\n",
       "         [1.],\n",
       "         ...,\n",
       "         [1.],\n",
       "         [1.],\n",
       "         [1.]]], dtype=float32)>,\n",
       " <tf.Tensor: shape=(128, 128, 1), dtype=int64, numpy=\n",
       " array([[[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]],\n",
       " \n",
       "        [[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]],\n",
       " \n",
       "        [[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
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       "         [1],\n",
       "         [1]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]],\n",
       " \n",
       "        [[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]],\n",
       " \n",
       "        [[1],\n",
       "         [1],\n",
       "         [1],\n",
       "         ...,\n",
       "         [1],\n",
       "         [1],\n",
       "         [1]]], dtype=int64)>]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sample Prediction after epoch 20\n",
      "\n",
      "57/57 [==============================] - 55s 976ms/step - loss: 0.0978 - accuracy: 0.9591 - val_loss: 0.3668 - val_accuracy: 0.8986\n"
     ]
    },
    {
     "data": {
      "image/png": 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nJld2KviECRMqzOvv7691arIQQqSkpIhOnToJOzs7ERgYKD755BPx6quvCnt7+0rehTv++ecfER4eLpydnYWnp6cYN26c5tTiu09jjo6OFk5OThXm19X3a9euiREjRghXV1fh5uYmRowYIQ4dOqT3qeDldu7cKQAIHx+fCqdfq1QqsWDBAuHv7y8UCoXo1KmT+O677ypsByGqPxVcCCGUSqWYO3eu8PHxEQ4ODqJPnz7ir7/+qvB+l5SUiFdffVVTr0ePHiI9PV307t1b9O7dW2u5O3bsEG3bttWcll++7rr6WFBQIF555RXh6+sr6tWrJ4KCgsSiRYu0Tk0vXxd9Pxf3Kv9MVjZ9/vnnQgghsrOzxejRo4Wnp6ews7MTwcHBFbbb5s2bxaOPPioaNWok7OzsRNOmTcULL7wgLl++rKnz5ptvim7dugl3d3fh4OAgWrduLd566y1RVlZWZT+JqiITog7995KIzCoqKoqn4RKR5HDMDdF94t5bJZw8eRK7du1Cnz59LNMhIiIT4Z4bovuEj48PRo0ahebNm+Ps2bNYsWIFSktLcejQoQrXbiEismYcUEx0n+jXrx82bNiArKwsKBQKhIWFYcGCBQw2RCQ5Fj0s9fPPPyMyMhK+vr6QyWTYvn17tfOkpaXhgQcegEKhQIsWLTQXEiOiqq1btw5nzpxBSUkJ8vLykJSUpHXTSSIiqbBouCkqKkJISAiWL1+uV/3MzEwMHDgQffv2xeHDhzF16lSMHTsWu3fvNnFPiYiIyFrUmTE3MpkM27ZtQ1RUVKV1pk+fjp07d+Kvv/7SlD3zzDPIzc2tcL8SIiIiuj9Z1Zib9PR0hIeHa5VFRETovCtvudLSUq0rXZbfGdjDw8OgS58TERGR5QghUFBQAF9f3wo3bb2XVYWbrKwseHl5aZV5eXkhPz8fxcXFOm9qmJCQgLlz55qri0RERGRC58+fR5MmTaqsY1XhpiZmzpyJmJgYzfO8vDw0bdoU58+fh6urqwV7RkRERPrKz8+Hn5+f1o1jK2NV4cbb2xvZ2dlaZdnZ2XB1ddW51wYAFAoFFApFhXJXV1eGGyIiIiujz5ASq7pCcVhYGFJSUrTKkpOTERYWZqEeERERUV1j0XBTWFiIw4cPa+5KnJmZicOHD+PcuXMA1IeURo4cqan/4osv4vTp03j99ddx4sQJfPTRR/j666/xyiuvWKL7REREVAdZNNz8+eef6NSpEzp16gQAiImJQadOnRAbGwsAuHz5siboAECzZs2wc+dOJCcnIyQkBO+99x4++eQTREREWKT/REREVPfUmevcmEt+fj7c3NyQl5fHMTdEREagVCpx69YtS3eDJMDOzq7S07wN+f22qgHFRERUdwghkJWVhdzcXEt3hSRCLpejWbNmsLOzq1U7DDdERFQj5cGmUaNGcHR05IVRqVZUKhUuXbqEy5cvo2nTprX6PDHcEBGRwZRKpSbYeHh4WLo7JBENGzbEpUuXcPv2bdSrV6/G7VjVqeBERFQ3lI+xcXR0tHBPSErKD0cplcpatcNwQ0RENcZDUWRMxvo8MdwQERGRpDDcEBER1VJAQACWLFmid/20tDTIZDKTn2mWmJgId3d3ky6jLmK4ISKi+4ZMJqtyio+Pr1G7f/zxB8aPH693/e7du+Py5ctwc3Or0fKoajxbioiI7huXL1/WPN64cSNiY2ORkZGhKXN2dtY8FkJAqVTC1rb6n8qGDRsa1A87Ozt4e3sbNA/pj3tuiIjovuHt7a2Z3NzcIJPJNM9PnDgBFxcXfP/99+jcuTMUCgV++eUX/Pfffxg0aBC8vLzg7OyMrl27Ys+ePVrt3ntYSiaT4ZNPPsETTzwBR0dHBAUF4ZtvvtG8fu9hqfLDR7t370abNm3g7OyMfv36aYWx27dvY/LkyXB3d4eHhwemT5+O6OhoREVFGfQerFixAoGBgbCzs0OrVq3w+eefa14TQiA+Ph5NmzaFQqGAr68vJk+erHn9o48+QlBQEOzt7eHl5YWnnnrKoGWbC8MNEREZhxBAUZFlJiPeSWjGjBl4++23cfz4cXTo0AGFhYUYMGAAUlJScOjQIfTr1w+RkZFa9z7UZe7cuXj66adx9OhRDBgwAMOHD8f169crrX/z5k28++67+Pzzz/Hzzz/j3LlzmDZtmub1d955B19++SXWrVuHffv2IT8/H9u3bzdo3bZt24YpU6bg1VdfxV9//YUXXngBo0ePRmpqKgBgy5YtWLx4MVatWoWTJ09i+/btCA4OBqC+H+TkyZMxb948ZGRkICkpCb169TJo+WYj7jN5eXkCgMjLy7N0V4iIrFZxcbH4559/RHFx8Z3CwkIh1DHD/FNhocHrsG7dOuHm5qZ5npqaKgCI7du3Vztvu3btxNKlSzXP/f39xeLFizXPAYjZs2ff9dYUCgDi+++/11rWjRs3NH0BIE6dOqWZZ/ny5cLLy0vz3MvLSyxatEjz/Pbt26Jp06Zi0KBBeq9j9+7dxbhx47TqDBkyRAwYMEAIIcR7770nWrZsKcrKyiq0tWXLFuHq6iry8/MrXV5t6fxc/T9Dfr+554aIiOguXbp00XpeWFiIadOmoU2bNnB3d4ezszOOHz9e7Z6bDh06aB47OTnB1dUVOTk5ldZ3dHREYGCg5rmPj4+mfl5eHrKzs9GtWzfN6zY2NujcubNB63b8+HH06NFDq6xHjx44fvw4AGDIkCEoLi5G8+bNMW7cOGzbtg23b98GADzyyCPw9/dH8+bNMWLECHz55Ze4efOmQcs3F4YbIiIyDkdHoLDQMpMRr5Ts5OSk9XzatGnYtm0bFixYgL179+Lw4cMIDg5GWVlZle3ce/sAmUwGlUplUH1hxMNt+vDz80NGRgY++ugjODg44OWXX0avXr1w69YtuLi44ODBg9iwYQN8fHwQGxuLkJCQOnnjVIYbIiIyDpkMcHKyzGTCKyXv27cPo0aNwhNPPIHg4GB4e3vjzJkzJlueLm5ubvDy8sIff/yhKVMqlTh48KBB7bRp0wb79u3TKtu3bx/atm2ree7g4IDIyEh8+OGHSEtLQ3p6Oo4dOwYAsLW1RXh4OBYuXIijR4/izJkz+PHHH2uxZqbBU8GJiIiqEBQUhK1btyIyMhIymQxz5sypcg+MqUyaNAkJCQlo0aIFWrdujaVLl+LGjRsG3bLgtddew9NPP41OnTohPDwc3377LbZu3ao5+ysxMRFKpRKhoaFwdHTEF198AQcHB/j7++O7777D6dOn0atXL9SvXx+7du2CSqVCq1atTLXKNcZwQ0REVIX3338fzz//PLp37w5PT09Mnz4d+fn5Zu/H9OnTkZWVhZEjR8LGxgbjx49HREQEbGxs9G4jKioKH3zwAd59911MmTIFzZo1w7p169CnTx8AgLu7O95++23ExMRAqVQiODgY3377LTw8PODu7o6tW7ciPj4eJSUlCAoKwoYNG9CuXTsTrXHNyYS5D+hZWH5+Ptzc3JCXlwdXV1dLd4eIyCqVlJQgMzMTzZo1g729vaW7c19SqVRo06YNnn76acyfP9/S3TGKqj5Xhvx+c88NERGRFTh79ix++OEH9O7dG6WlpVi2bBkyMzPx7LPPWrprdQ4HFBMREVkBuVyOxMREdO3aFT169MCxY8ewZ88etGnTxtJdq3O454aIiMgK+Pn5VTjTiXTjnhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiID9enTB1OnTtU8DwgIwJIlS6qcRyaTYfv27bVetrHaqUp8fDw6duxo0mWYEsMNERFZlFIJpKUBGzao/yqVpltWZGQk+vXrp/O1vXv3QiaT4ejRowa3+8cff2D8+PG17Z6WygLG5cuX0b9/f6MuS2oYboiIyGK2bgUCAoC+fYFnn1X/DQhQl5vCmDFjkJycjAsXLlR4bd26dejSpQs6dOhgcLsNGzaEo6OjMbpYLW9vbygUCrMsy1ox3BARkUVs3Qo89RRwb864eFFdboqA89hjj6Fhw4ZITEzUKi8sLMSmTZswZswYXLt2DcOGDUPjxo3h6OiI4OBgbNiwocp27z0sdfLkSfTq1Qv29vZo27YtkpOTK8wzffp0tGzZEo6OjmjevDnmzJmDW7duAQASExMxd+5cHDlyBDKZDDKZTNPnew9LHTt2DA899BAcHBzg4eGB8ePHo7CwUPP6qFGjEBUVhXfffRc+Pj7w8PDAhAkTNMvSh0qlwrx589CkSRMoFAp07NgRSUlJmtfLysowceJE+Pj4wN7eHv7+/khISAAACCEQHx+Ppk2bQqFQwNfXF5MnT9Z72TXB2y8QEZHZKZXAlCmAEBVfEwKQyYCpU4FBgwAbG+Mt19bWFiNHjkRiYiJmzZoFmUwGANi0aROUSiWGDRuGwsJCdO7cGdOnT4erqyt27tyJESNGIDAwEN26dat2GSqVCoMHD4aXlxd+//135OXlaY3PKefi4oLExET4+vri2LFjGDduHFxcXPD6669j6NCh+Ouvv5CUlIQ9e/YAANzc3Cq0UVRUhIiICISFheGPP/5ATk4Oxo4di4kTJ2oFuNTUVPj4+CA1NRWnTp3C0KFD0bFjR4wbN06v9+2DDz7Ae++9h1WrVqFTp05Yu3YtHn/8cfz9998ICgrChx9+iG+++QZff/01mjZtivPnz+P8+fMAgC1btmDx4sX46quv0K5dO2RlZeHIkSN6LbfGxH0mLy9PABB5eXmW7goRkdUqLi4W//zzjyguLq7R/KmpQqhjTNVTaqpRuy2EEOL48eMCgEi9q/GePXuK5557rtJ5Bg4cKF599VXN8969e4spU6Zonvv7+4vFixcLIYTYvXu3sLW1FRcvXtS8/v333wsAYtu2bZUuY9GiRaJz586a53FxcSIkJKRCvbvbWb16tahfv74oLCzUvL5z504hl8tFVlaWEEKI6Oho4e/vL27fvq2pM2TIEDF06NBK+3Lvsn19fcVbb72lVadr167i5ZdfFkIIMWnSJPHQQw8JlUpVoa333ntPtGzZUpSVlVW6vHJVfa4M+f3mYSkiIjK7y5eNW88QrVu3Rvfu3bF27VoAwKlTp7B3716MGTMGAKBUKjF//nwEBwejQYMGcHZ2xu7du3Hu3Dm92j9+/Dj8/Pzg6+urKQsLC6tQb+PGjejRowe8vb3h7OyM2bNn672Mu5cVEhICJycnTVmPHj2gUqmQkZGhKWvXrh1s7toF5uPjg5ycHL2WkZ+fj0uXLqFHjx5a5T169MDx48cBqA99HT58GK1atcLkyZPxww8/aOoNGTIExcXFaN68OcaNG4dt27bh9u3bBq2noRhuiIjI7Hx8jFvPUGPGjMGWLVtQUFCAdevWITAwEL179wYALFq0CB988AGmT5+O1NRUHD58GBERESgrKzPa8tPT0zF8+HAMGDAA3333HQ4dOoRZs2YZdRl3q1evntZzmUwGlUpltPYfeOABZGZmYv78+SguLsbTTz+Np556CoD6buYZGRn46KOP4ODggJdffhm9evUyaMyPoRhuiIjI7Hr2BJo0UY+t0UUmA/z81PVM4emnn4ZcLsf69evx2Wef4fnnn9eMv9m3bx8GDRqE5557DiEhIWjevDn+/fdfvdtu06YNzp8/j8t37Xb67bfftOr8+uuv8Pf3x6xZs9ClSxcEBQXh7NmzWnXs7OygrOa8+DZt2uDIkSMoKirSlO3btw9yuRytWrXSu89VcXV1ha+vL/bt26dVvm/fPrRt21ar3tChQ/Hxxx9j48aN2LJlC65fvw4AcHBwQGRkJD788EOkpaUhPT0dx44dM0r/dOGAYiIiMjsbG+CDD9RnRclk2gOLywPPkiXGHUx8N2dnZwwdOhQzZ85Efn4+Ro0apXktKCgImzdvxq+//or69evj/fffR3Z2ttYPeVXCw8PRsmVLREdHY9GiRcjPz8esWbO06gQFBeHcuXP46quv0LVrV+zcuRPbtm3TqhMQEIDMzEwcPnwYTZo0gYuLS4VTwIcPH464uDhER0cjPj4eV65cwaRJkzBixAh4eXnV7M3R4bXXXkNcXBwCAwPRsWNHrFu3DocPH8aXX34JAHj//ffh4+ODTp06QS6XY9OmTfD29oa7uzsSExOhVCoRGhoKR0dHfPHFF3BwcIC/v7/R+ncv7rkhIiKLGDwY2LwZaNxYu7xJE3X54MGmXf6YMWNw48YNREREaI2PmT17Nh544AFERESgT58+8Pb2RlRUlN7tyuVybNu2DcXFxejWrRvGjh2Lt956S6vO448/jldeeQUTJ05Ex44d8euvv2LOnDladZ588kn069cPffv2RcOGDXWeju7o6Ijdu3fj+vXr6Nq1K5566ik8/PDDWLZsmWFvRjUmT56MmJgYvPrqqwgODkZSUhK++eYbBAUFAVCf+bVw4UJ06dIFXbt2xZkzZ7Br1y7I5XK4u7vj448/Ro8ePdChQwfs2bMH3377LTw8PIzax7vJhNB1Ip505efnw83NDXl5eXB1dbV0d4iIrFJJSQkyMzPRrFkz2Nvb16otpRLYu1c9eNjHR30oylR7bKhuq+pzZcjvNw9LERGRRdnYAH36WLoXJCU8LEVERESSwnBDREREksJwQ0RERJLCcENERDV2n52TQiZmrM8Tww0RERms/Iq3N2/etHBPSErKr9BsU8vT5Xi2FBERGczGxgbu7u6a+xM5OjpqrvBLVBMqlQpXrlyBo6MjbG1rF08YboiIqEa8vb0BQO8bMBJVRy6Xo2nTprUOygw3RERUIzKZDD4+PmjUqJFJb4JI9w87OzvI5bUfMcNwQ0REtWJjY1PrMRJExsQBxURERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKRYPN8uXL0dAQADs7e0RGhqK/fv3V1l/yZIlaNWqFRwcHODn54dXXnkFJSUlZuotERER1XUWDTcbN25ETEwM4uLicPDgQYSEhCAiIgI5OTk6669fvx4zZsxAXFwcjh8/jjVr1mDjxo144403zNxzIiIiqqssGm7ef/99jBs3DqNHj0bbtm2xcuVKODo6Yu3atTrr//rrr+jRoweeffZZBAQE4NFHH8WwYcOq3dtDRERE9w+LhZuysjIcOHAA4eHhdzojlyM8PBzp6ek65+nevTsOHDigCTOnT5/Grl27MGDAgEqXU1paivz8fK2JiIiIpMvWUgu+evUqlEolvLy8tMq9vLxw4sQJnfM8++yzuHr1Kv73v/9BCIHbt2/jxRdfrPKwVEJCAubOnWvUvhMREVHdZfEBxYZIS0vDggUL8NFHH+HgwYPYunUrdu7cifnz51c6z8yZM5GXl6eZzp8/b8YeExERkblZbM+Np6cnbGxskJ2drVWenZ0Nb29vnfPMmTMHI0aMwNixYwEAwcHBKCoqwvjx4zFr1izI5RWzmkKhgEKhMP4KEBERUZ1ksT03dnZ26Ny5M1JSUjRlKpUKKSkpCAsL0znPzZs3KwQYGxsbAIAQwnSdJSIiIqthsT03ABATE4Po6Gh06dIF3bp1w5IlS1BUVITRo0cDAEaOHInGjRsjISEBABAZGYn3338fnTp1QmhoKE6dOoU5c+YgMjJSE3KIiIjo/mbRcDN06FBcuXIFsbGxyMrKQseOHZGUlKQZZHzu3DmtPTWzZ8+GTCbD7NmzcfHiRTRs2BCRkZF46623LLUKREREVMfIxH12PCc/Px9ubm7Iy8uDq6urpbtDREREejDk99uqzpYiIiIiqg7DDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJisXDzfLlyxEQEAB7e3uEhoZi//79VdbPzc3FhAkT4OPjA4VCgZYtW2LXrl1m6i0RERHVdbaWXPjGjRsRExODlStXIjQ0FEuWLEFERAQyMjLQqFGjCvXLysrwyCOPoFGjRti8eTMaN26Ms2fPwt3d3fydJyIiojpJJoQQllp4aGgounbtimXLlgEAVCoV/Pz8MGnSJMyYMaNC/ZUrV2LRokU4ceIE6tWrV6Nl5ufnw83NDXl5eXB1da1V/4mIiMg8DPn9tthhqbKyMhw4cADh4eF3OiOXIzw8HOnp6Trn+eabbxAWFoYJEybAy8sL7du3x4IFC6BUKitdTmlpKfLz87UmIiIiki6LhZurV69CqVTCy8tLq9zLywtZWVk65zl9+jQ2b94MpVKJXbt2Yc6cOXjvvffw5ptvVrqchIQEuLm5aSY/Pz+jrgcRERHVLRYfUGwIlUqFRo0aYfXq1ejcuTOGDh2KWbNmYeXKlZXOM3PmTOTl5Wmm8+fPm7HHREREZG61GlBcUlICe3v7Gs3r6ekJGxsbZGdna5VnZ2fD29tb5zw+Pj6oV68ebGxsNGVt2rRBVlYWysrKYGdnV2EehUIBhUJRoz4SERGR9TF4z41KpcL8+fPRuHFjODs74/Tp0wCAOXPmYM2aNXq3Y2dnh86dOyMlJUWr7ZSUFISFhemcp0ePHjh16hRUKpWm7N9//4WPj4/OYENERET3H4PDzZtvvonExEQsXLhQK1C0b98en3zyiUFtxcTE4OOPP8ann36K48eP46WXXkJRURFGjx4NABg5ciRmzpypqf/SSy/h+vXrmDJlCv7991/s3LkTCxYswIQJEwxdDSIiIpIogw9LffbZZ1i9ejUefvhhvPjii5rykJAQnDhxwqC2hg4diitXriA2NhZZWVno2LEjkpKSNIOMz507B7n8Tv7y8/PD7t278corr6BDhw5o3LgxpkyZgunTpxu6GkRERCRRBl/nxsHBASdOnIC/vz9cXFxw5MgRNG/eHP/88w+6deuGwsJCU/XVKHidGyIiIutj0uvctG3bFnv37q1QvnnzZnTq1MnQ5oiIiIiMyuDDUrGxsYiOjsbFixehUqmwdetWZGRk4LPPPsN3331nij4SERER6c3gPTeDBg3Ct99+iz179sDJyQmxsbE4fvw4vv32WzzyyCOm6CMRERGR3ix6bylL4JgbIiIi62MV95YiIiIiMgWDx9zI5XLIZLJKX6/qJpZEREREpmZwuNm2bZvW81u3buHQoUP49NNPMXfuXKN1jIiIiKgmjDbmZv369di4cSN27NhhjOZMhmNuiIiIrI9Fxtw8+OCDWveJIiIiIrIEo4Sb4uJifPjhh2jcuLExmiMiIiKqMYPH3NSvX19rQLEQAgUFBXB0dMQXX3xh1M4RERERGcrgcLN48WKtcCOXy9GwYUOEhoaifv36Ru0cERERkaEMDjejRo0yQTeIiIiIjEOvcHP06FG9G+zQoUONO0NERERUW3qFm44dO0Imk6G6s8ZlMhkv4kdEREQWpVe4yczMNHU/iIiIiIxCr3Dj7+9v6n4QERERGYXBA4rL/fPPPzh37hzKysq0yh9//PFad4qIiIiopgwON6dPn8YTTzyBY8eOaY3DKT89nGNuiIiIyJIMvkLxlClT0KxZM+Tk5MDR0RF///03fv75Z3Tp0gVpaWkm6CIRERGR/gzec5Oeno4ff/wRnp6ekMvlkMvl+N///oeEhARMnjwZhw4dMkU/iYiIiPRi8J4bpVIJFxcXAICnpycuXboEQD3oOCMjw7i9IyIiIjKQwXtu2rdvjyNHjqBZs2YIDQ3FwoULYWdnh9WrV6N58+am6CMRERGR3gwON7Nnz0ZRUREAYN68eXjsscfQs2dPeHh4YOPGjUbvIBEREZEh9A43Xbp0wdixY/Hss8/C1dUVANCiRQucOHEC169fr3C3cCIiIiJL0HvMTUhICF5//XX4+Phg5MiRWmdGNWjQgMGGiIiI6gS9w82aNWuQlZWF5cuX49y5c3j44YfRokULLFiwABcvXjRlH4mIiIj0ZtDZUo6Ojhg1ahTS0tLw77//4plnnsGqVasQEBCAgQMHYuvWrabqJxEREZFeZKK6W31XQwiBLVu24IUXXkBubm6dv0Jxfn4+3NzckJeXpxk7RERERHWbIb/fNb63FACkpaVh3bp12LJlC2xtbTFu3LjaNEdERERUawaHmwsXLiAxMRGJiYk4ffo0evbsiY8++ghDhgyBg4ODKfpIREREpDe9w83XX3+NtWvXIiUlBY0aNUJ0dDSef/55tGjRwpT9IyIiIjKI3uHmueeew8CBA7Ft2zYMGDAAcrnBd24gIiIiMjm9w82FCxfQqFEjU/aFiIiIqNb03v3CYENERETWgMeWiIiISFIYboiIiEhSGG6IiIhIUgwON+fPn8eFCxc0z/fv34+pU6di9erVRu0YERERUU0YHG6effZZpKamAgCysrLwyCOPYP/+/Zg1axbmzZtn9A4SERERGcLgcPPXX3+hW7duANQX9mvfvj1+/fVXfPnll0hMTDR2/4iIiIgMYnC4uXXrFhQKBQBgz549ePzxxwEArVu3xuXLl43bOyIiIiIDGRxu2rVrh5UrV2Lv3r1ITk5Gv379AACXLl2Ch4eH0TtIREREZAiDw80777yDVatWoU+fPhg2bBhCQkIAAN98843mcBURERGRpciEEMLQmZRKJfLz81G/fn1N2ZkzZ+Do6Fjnr2Scn58PNzc35OXlwdXV1dLdISIiIj0Y8vtt8J6b4uJilJaWaoLN2bNnsWTJEmRkZNT5YENERETSZ3C4GTRoED777DMAQG5uLkJDQ/Hee+8hKioKK1asMHoHiYiIiAxhcLg5ePAgevbsCQDYvHkzvLy8cPbsWXz22Wf48MMPjd5BIiIiIkMYHG5u3rwJFxcXAMAPP/yAwYMHQy6X48EHH8TZs2eN3kEiIiIiQxgcblq0aIHt27fj/Pnz2L17Nx599FEAQE5ODgfoEhERkcUZHG5iY2Mxbdo0BAQEoFu3bggLCwOg3ovTqVMno3eQiIiIyBA1OhU8KysLly9fRkhICORydT7av38/XF1d0bp1a6N30ph4KjgREZH1MeT327YmC/D29oa3t7fm7uBNmjThBfyIiIioTjD4sJRKpcK8efPg5uYGf39/+Pv7w93dHfPnz4dKpTJFH4mIiIj0ZvCem1mzZmHNmjV4++230aNHDwDAL7/8gvj4eJSUlOCtt94yeieJiIiI9GXwmBtfX1+sXLlSczfwcjt27MDLL7+MixcvGrWDxsYxN0RERNbHpLdfuH79us5Bw61bt8b169cNbY6IiIjIqAwONyEhIVi2bFmF8mXLlmnuEE5ERERkKQaPuVm4cCEGDhyIPXv2aK5xk56ejvPnz2PXrl1G7yARERGRIQzec9O7d2/8+++/eOKJJ5Cbm4vc3FwMHjwYGRkZmntOEREREVlKjS7ip8uFCxcwb948rF692hjNmQwHFBMREVkfkw4orsy1a9ewZs0aYzVHREREVCNGCzdEREREdUGdCDfLly9HQEAA7O3tERoaiv379+s131dffQWZTIaoqCjTdpCIiIishsXDzcaNGxETE4O4uDgcPHgQISEhiIiIQE5OTpXznTlzBtOmTeMgZiIiItKi94DiwYMHV/l6bm4ufvrpJyiVSoM6EBoaiq5du2qunaNSqeDn54dJkyZhxowZOudRKpXo1asXnn/+eezduxe5ubnYvn27XsvjgGIiIiLrY5K7gru5uVX7+siRI/VtDgBQVlaGAwcOYObMmZoyuVyO8PBwpKenVzrfvHnz0KhRI4wZMwZ79+6tchmlpaUoLS3VPM/Pzzeoj0RERGRd9A4369atM/rCr169CqVSCS8vL61yLy8vnDhxQuc8v/zyC9asWYPDhw/rtYyEhATMnTu3tl0lIiIiK2HxMTeGKCgowIgRI/Dxxx/D09NTr3lmzpyJvLw8zXT+/HkT95KIiIgsyeDbLxiTp6cnbGxskJ2drVWenZ0Nb2/vCvX/++8/nDlzBpGRkZoylUoFALC1tUVGRgYCAwO15lEoFFAoFCboPREREdVFFt1zY2dnh86dOyMlJUVTplKpkJKSorlv1d1at26NY8eO4fDhw5rp8ccfR9++fXH48GH4+fmZs/tERERUB1l0zw0AxMTEIDo6Gl26dEG3bt2wZMkSFBUVYfTo0QCAkSNHonHjxkhISIC9vT3at2+vNb+7uzsAVCgnIiKi+5PFw83QoUNx5coVxMbGIisrCx07dkRSUpJmkPG5c+cgl1vV0CAiIiKyIKPdONNa8Do3RERUVymVwN69wOXLgI8P0LMnYGNjPe2bkkmuc0NERESms3UrMGUKcOHCnbImTYAPPgCquY5unWgfqDvhiXtuiIjIrOrKD2BNmaL/W7cCTz0F3PuLLJOp/27eXLsAYur2y5dhyvBkyO83ww0REZmNOfYemJIp+q9UAgEB2m3eTSZTLyMzs2YhytTtA+YJT4b8fnOkLhERmUX5D+C9P7IXL6rLt261TL/0Zar+791befAA1IHh/Hl1vbrYvlKpDny6dpWUl02dqq5nLgw3RERkcub8AVQqgbQ0YMMG9V9jtWmq/l++bNx65m7f1OGpJhhuiIjI5Mz1A7h1q/oQTN++wLPPqv8GBNR+r5Ap++/jY9x65m7f1OGpJhhuiIjI5MzxA2jKw16m7H/PnuoxL+XjU+4lkwF+fup6NWHq9k0dnmqC4YaIiLSY4rCOqX8ATX3Yy5T9t7FRD0gGKgaQ8udLltR8sK+p2zd1eKoJhhsiItIw1WEdU/8Amvqwl6n7P3iw+oyixo21y5s0Mc6ZRqZs39ThqSYYboiICIBpD+uY+gfQ1Ie9zPEDPngwcOYMkJoKrF+v/puZabxT5E3ZvqnDmaF4nRsiui9Z+4XkjM0c10IBdF8nxs9PHQxq8wOYlqbey1Sd1FSgT5+aL8dU/ZcKU36veBG/KjDcEJG1X0jOFMwVDgDT/ACWh7OLF3WPuzFWOCtfFoOx+fHeUkRElajsSqrlh14ssQu9Joz9A2vO03ltbGofkHS1+cEH6m0ok2lvX2OP+zBF/8m4OOaGiO4bdfFKqjVhikG/dfF0XkPVtXEfZDk8LEVENWZtu+et/dALYLp7+JjzsI6pWdvnkvTDw1JEZHLWOG7FXIdeTPXeVLfnSSZT73kaNMjwH3NzHtYxNR42Ih6WIiKDWesNEM1x6MWU742pr+XCwzokFTwsRUQGMdcpw6Zg6kMvpn5vNmxQj7Gpzvr1wLBhhrdfjod1qC4y5Pebe26IyCB18Q7A+jL1hdhM/d6Ya9Bv+WGdYcPUfxlsyNow3BBVwRT32LH29uviHYANYcpDL6Z+b+riPXyI6iIOKCaqhKkHzFpr+1I5ZXjQIOMfejH1eyOlQb9EpsQxN0Q6mOp0Wym0b65Thq1x3Ie53hveAoDuR7z9QhUYbqg6ph4Uau3tA3fCE6B774Exwpm1nWZeztTvTTlrDH9EtcEBxXRfMfa4ElMPCrX29gHTjlux1tPMy5nrdGoO+iWqHMfckFUzxf/wTT0o1NrbL2eKcSumvEidOZlqTA8R6YfhxkpwF3RFproBoqkHhVp7+3cz9pVgDdnrVNevQMur5BJZDg9LWQFT3CTP2pnyBoimPt3W2ts3JWs/zZyI6gaGGyMx1fVKrH38AWCa98aU40pMfaE3a2/flKRwmjkRWR7DjRGYas+KKfdOmIup3htT/w/f1INCrb19U7HmvU5EVHfwVPBaMuX1RNLS1GGgOqmptTu2b6rxPHxvpN++KZjrVGoisi68zk0VjBlupHCTPFNdT8Rc13Ix9cXSyDJ4kToiuhevc2Mm1n6TPFOO5zH1e2PN40qoeoMHA2fOqPe8rV+v/puZyWBDRPphuKkFa75JnqnH85jjrBdrHVdC+uFF6oiophhuasFcN8kDjL93wtr3OpXj//CJiOheDDe1YI4zO0y1d8Ka9zrdi//DJyKiuzHc1IK5xn2YYu+ENe91IiIiqgrDTS1Z603yrHmvExERUVV4KriR8HoilbPG94aIiOoWXuemCqYKN9aK1xMhIiJrYMjvN+8Kfp8bPBgYNIh7VoiISDoYbkgznoeIiEgKOKCYiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJKVOhJvly5cjICAA9vb2CA0Nxf79+yut+/HHH6Nnz56oX78+6tevj/Dw8CrrExER0f3F4uFm48aNiImJQVxcHA4ePIiQkBBEREQgJydHZ/20tDQMGzYMqampSE9Ph5+fHx599FFcvHjRzD0nIiKiukgmhBCW7EBoaCi6du2KZcuWAQBUKhX8/PwwadIkzJgxo9r5lUol6tevj2XLlmHkyJHV1s/Pz4ebmxvy8vLg6upa6/4TERGR6Rny+23RPTdlZWU4cOAAwsPDNWVyuRzh4eFIT0/Xq42bN2/i1q1baNCggc7XS0tLkZ+frzURERGRdFk03Fy9ehVKpRJeXl5a5V5eXsjKytKrjenTp8PX11crIN0tISEBbm5umsnPz6/W/SYiIqK6y+Jjbmrj7bffxldffYVt27bB3t5eZ52ZM2ciLy9PM50/f97MvSQiIiJzsrXkwj09PWFjY4Ps7Gyt8uzsbHh7e1c577vvvou3334be/bsQYcOHSqtp1AooFAojNJfIiIiqvssuufGzs4OnTt3RkpKiqZMpVIhJSUFYWFhlc63cOFCzJ8/H0lJSejSpYs5ukpERERWwqJ7bgAgJiYG0dHR6NKlC7p164YlS5agqKgIo0ePBgCMHDkSjRs3RkJCAgDgnXfeQWxsLNavX4+AgADN2BxnZ2c4OztbbD2IiIiobrB4uBk6dCiuXLmC2NhYZGVloWPHjkhKStIMMj537hzk8js7mFasWIGysjI89dRTWu3ExcUhPj7enF0nIiKiOsji17kxN17nhoiIyPpYzXVuiIiIiIyN4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJMXit1+QjFOngOHDAU9P3VPDhnce168P2NhYusdERESSxHBjLJcuAfv361dXJgMaNKg8COma3NzU8xEREVGVGG6MpV07YMcO4MoV4OrVyqfcXEAI4No19ZSRoV/7traAhwfQuLF6atJE918XF5OuJhERUV3HG2ea261bwPXrVQege6fCQv3bd3GpPPiU//X0BOQcbkVERNbDkN9v7rkxt3r1AC8v9aSvkhL1Xp6cHPXhrwsXgIsXK/7NywMKCoDjx9VTZezsAF/fiqHHxwdwd1dPbm7qyd0dcHDgITEiIrIa3HMjJYWF2mFHVwDKzlYfFjOEre2doHN36DHksZ2d0VeXiIjuH9xzc79ydgZatVJPlbl1C7h8Wffen6ws9d6fvDz12KC8PEClAm7fvjNGqKYcHAAnJ3XI0XeqV8+w+nL5nUkmq/qvoXVsbNRhrUED9cSwRkRUZzHc3G/q1QOaNlVP1RECKCq6E3TuDj36PM7NvTNeqLhYPUmFs/OdoOPhcedxZWUeHupLACgUlu45EZHkMdxQ5WQy9Y+4s7N6XE5NKJVAfr466Ny8CZSVqadbt+48NnTSNW9pqXovkxDqv3c/vvdvVa9VVufWLfU63Lihfl5YqJ7OnTPs/XBy0h2A6tcHXF3VA8JdXO48vrfM2ZnXSCIiqgbDDZmWjY36h7t+fUv3xDhUKnXIuX79znTtWvXPy0NRUZF6On++5n1wcqo+BN1b5u6u3gZ3BymeMUdEEsVwQ2QIufzO3hZDqFTqQ3VVhZ+CAvWUn1/xcX6+euwTcCcgZWXVbj3uDTyV/b23jGfPEVEdx3BDZA5y+Z2gEBho+PxCqA+93Rt4dIUgXWXlwerGDfXYJ5XqTrj67z/D+mJnVzEAubpW3GNU1V9nZ/VZeEREJsB/XYisgUwG2Nurp4YNa9dWSYk65Ny4cSfw3Pu3srLbt9VjnLKyarfnCFDvAdInCDk5qQ9vVnYmW3VnulX32N7+zhgoDw/A0bF260VEFsdwQ3S/sbdXX7DRx8ew+coHUusKQffuOapqr1Jpqbq98jPosrONv4614eBwJ+jomjw9K5a5uXEME1EdwnBDRPqRye4MUNbnUgKVKSvTHXoqC0aFhdWf0abP48peLy6+cx2n27fVzy9cUE/6Kh+LpSv8ODvfuRaTQlHx+kw1KeMZc0RVYrghIvOys7vzw1+XCKEOVOVB59o19b3d7n6u67WiInVIKr8XnL43w60NudywYFTTEGVvf2fgeflfXq+JrADDDRERoN4zVT4wulkz/ecrLa06/Nx9fafSUt3XaKruefmZcuVUKvXYqZIS474H+nJw0A475dO9ZbqeOznxbDsyOYYbIqLaUCjUN6L19TXdMlQq7YtXlgeg0tI75bqCU1WhSp/y4uI7F6+8cUN91p0Qd8ZLXb5s+LrUq3cn9Li4qN+/2k729rrLbW3Vk42NeqruMUOXZDDcEBHVdXL5nR9sS1Kp1OOgysPOjRva4ae657dvq8PYlSvqqa6RyQwPQ7qm8rYMne6ez8am4qHCuw8Z1ras/BYybm6SDHUMN0REpJ/yiz+6uxt26A64c4XuuwNPYaF6L9G9U0mJ7nJDp9u31ZNSqZ5u31b3o6o+3rqlnu4X5VeRv/uWMPr8dXau06GI4YaIiEzv7nvV+flZrh9C3Ak6d/+t7HFVrwtxZypvu6rJkDpKZeWHEO8+LFnT1wsK1GFTqbwzGN4Q9erpvk9e+ePAQGDoUONtNwMx3BAR0f2j/NATr5CtDjl33wam/NYwVf29du3OWK/s7MqvUxUaynBDREREZqZQ1OyCnjdvVh+CAgJM0mV9MdwQERGR/hwd1VOTJpbuSaV4vXAiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikpQ6EW6WL1+OgIAA2NvbIzQ0FPv376+y/qZNm9C6dWvY29sjODgYu3btMlNPiYiIqK6zeLjZuHEjYmJiEBcXh4MHDyIkJAQRERHIycnRWf/XX3/FsGHDMGbMGBw6dAhRUVGIiorCX3/9ZeaeExERUV0kE0IIS3YgNDQUXbt2xbJlywAAKpUKfn5+mDRpEmbMmFGh/tChQ1FUVITvvvtOU/bggw+iY8eOWLlyZbXLy8/Ph5ubG/Ly8uDq6mq8FSEiIiKTMeT326J7bsrKynDgwAGEh4dryuRyOcLDw5Genq5znvT0dK36ABAREVFpfSIiIrq/2Fpy4VevXoVSqYSXl5dWuZeXF06cOKFznqysLJ31s7KydNYvLS1FaWmp5nleXh4AdQIkIiIi61D+u63PASeLhhtzSEhIwNy5cyuU+/n5WaA3REREVBsFBQVwc3Orso5Fw42npydsbGyQnZ2tVZ6dnQ1vb2+d83h7extUf+bMmYiJidE8V6lUuH79Ojw8PCCTyWq5Btry8/Ph5+eH8+fPS348D9dVuu6n9eW6Stf9tL73y7oKIVBQUABfX99q61o03NjZ2aFz585ISUlBVFQUAHX4SElJwcSJE3XOExYWhpSUFEydOlVTlpycjLCwMJ31FQoFFAqFVpm7u7sxul8pV1dXSX/A7sZ1la77aX25rtJ1P63v/bCu1e2xKWfxw1IxMTGIjo5Gly5d0K1bNyxZsgRFRUUYPXo0AGDkyJFo3LgxEhISAABTpkxB79698d5772HgwIH46quv8Oeff2L16tWWXA0iIiKqIyweboYOHYorV64gNjYWWVlZ6NixI5KSkjSDhs+dOwe5/M5JXd27d8f69esxe/ZsvPHGGwgKCsL27dvRvn17S60CERER1SEWDzcAMHHixEoPQ6WlpVUoGzJkCIYMGWLiXhlOoVAgLi6uwmEwKeK6Stf9tL5cV+m6n9b3flpXfVn8In5ERERExmTx2y8QERERGRPDDREREUkKww0RERFJCsMNERERSQrDjYGWL1+OgIAA2NvbIzQ0FPv376+y/qZNm9C6dWvY29sjODgYu3btMlNPay4hIQFdu3aFi4sLGjVqhKioKGRkZFQ5T2JiImQymdZkb29vph7XTnx8fIW+t27dusp5rHG7AkBAQECFdZXJZJgwYYLO+ta0XX/++WdERkbC19cXMpkM27dv13pdCIHY2Fj4+PjAwcEB4eHhOHnyZLXtGvqdN5eq1vfWrVuYPn06goOD4eTkBF9fX4wcORKXLl2qss2afBfMobptO2rUqAr97tevX7Xt1sVtW9266vr+ymQyLFq0qNI26+p2NSWGGwNs3LgRMTExiIuLw8GDBxESEoKIiAjk5OTorP/rr79i2LBhGDNmDA4dOoSoqChERUXhr7/+MnPPDfPTTz9hwoQJ+O2335CcnIxbt27h0UcfRVFRUZXzubq64vLly5rp7NmzZupx7bVr106r77/88kulda11uwLAH3/8obWeycnJAFDlpRWsZbsWFRUhJCQEy5cv1/n6woUL8eGHH2LlypX4/fff4eTkhIiICJSUlFTapqHfeXOqan1v3ryJgwcPYs6cOTh48CC2bt2KjIwMPP7449W2a8h3wVyq27YA0K9fP61+b9iwoco26+q2rW5d717Hy5cvY+3atZDJZHjyySerbLcubleTEqS3bt26iQkTJmieK5VK4evrKxISEnTWf/rpp8XAgQO1ykJDQ8ULL7xg0n4aW05OjgAgfvrpp0rrrFu3Tri5uZmvU0YUFxcnQkJC9K4vle0qhBBTpkwRgYGBQqVS6XzdWrcrALFt2zbNc5VKJby9vcWiRYs0Zbm5uUKhUIgNGzZU2o6h33lLuXd9ddm/f78AIM6ePVtpHUO/C5aga12jo6PFoEGDDGrHGratPtt10KBB4qGHHqqyjjVsV2Pjnhs9lZWV4cCBAwgPD9eUyeVyhIeHIz09Xec86enpWvUBICIiotL6dVVeXh4AoEGDBlXWKywshL+/P/z8/DBo0CD8/fff5uieUZw8eRK+vr5o3rw5hg8fjnPnzlVaVyrbtaysDF988QWef/75Km8ia83btVxmZiaysrK0tpubmxtCQ0Mr3W41+c7XZXl5eZDJZNXeW8+Q70JdkpaWhkaNGqFVq1Z46aWXcO3atUrrSmXbZmdnY+fOnRgzZky1da11u9YUw42erl69CqVSqbktRDkvLy9kZWXpnCcrK8ug+nWRSqXC1KlT0aNHjypvcdGqVSusXbsWO3bswBdffAGVSoXu3bvjwoULZuxtzYSGhiIxMRFJSUlYsWIFMjMz0bNnTxQUFOisL4XtCgDbt29Hbm4uRo0aVWkda96udyvfNoZst5p85+uqkpISTJ8+HcOGDavyxoqGfhfqin79+uGzzz5DSkoK3nnnHfz000/o378/lEqlzvpS2baffvopXFxcMHjw4CrrWet2rY06cfsFqrsmTJiAv/76q9rjs2FhYVp3Zu/evTvatGmDVatWYf78+abuZq30799f87hDhw4IDQ2Fv78/vv76a73+R2St1qxZg/79+8PX17fSOta8XUnt1q1bePrppyGEwIoVK6qsa63fhWeeeUbzODg4GB06dEBgYCDS0tLw8MMPW7BnprV27VoMHz682kH+1rpda4N7bvTk6ekJGxsbZGdna5VnZ2fD29tb5zze3t4G1a9rJk6ciO+++w6pqalo0qSJQfPWq1cPnTp1wqlTp0zUO9Nxd3dHy5YtK+27tW9XADh79iz27NmDsWPHGjSftW7X8m1jyHaryXe+rikPNmfPnkVycnKVe210qe67UFc1b94cnp6elfZbCtt27969yMjIMPg7DFjvdjUEw42e7Ozs0LlzZ6SkpGjKVCoVUlJStP5ne7ewsDCt+gCQnJxcaf26QgiBiRMnYtu2bfjxxx/RrFkzg9tQKpU4duwYfHx8TNBD0yosLMR///1Xad+tdbvebd26dWjUqBEGDhxo0HzWul2bNWsGb29vre2Wn5+P33//vdLtVpPvfF1SHmxOnjyJPXv2wMPDw+A2qvsu1FUXLlzAtWvXKu23tW9bQL3ntXPnzggJCTF4Xmvdrgax9Ihma/LVV18JhUIhEhMTxT///CPGjx8v3N3dRVZWlhBCiBEjRogZM2Zo6u/bt0/Y2tqKd999Vxw/flzExcWJevXqiWPHjllqFfTy0ksvCTc3N5GWliYuX76smW7evKmpc++6zp07V+zevVv8999/4sCBA+KZZ54R9vb24u+//7bEKhjk1VdfFWlpaSIzM1Ps27dPhIeHC09PT5GTkyOEkM52LadUKkXTpk3F9OnTK7xmzdu1oKBAHDp0SBw6dEgAEO+//744dOiQ5uygt99+W7i7u4sdO3aIo0ePikGDBolmzZqJ4uJiTRsPPfSQWLp0qeZ5dd95S6pqfcvKysTjjz8umjRpIg4fPqz1PS4tLdW0ce/6VvddsJSq1rWgoEBMmzZNpKeni8zMTLFnzx7xwAMPiKCgIFFSUqJpw1q2bXWfYyGEyMvLE46OjmLFihU627CW7WpKDDcGWrp0qWjatKmws7MT3bp1E7/99pvmtd69e4vo6Git+l9//bVo2bKlsLOzE+3atRM7d+40c48NB0DntG7dOk2de9d16tSpmvfFy8tLDBgwQBw8eND8na+BoUOHCh8fH2FnZycaN24shg4dKk6dOqV5XSrbtdzu3bsFAJGRkVHhNWverqmpqTo/t+Xro1KpxJw5c4SXl5dQKBTi4YcfrvAe+Pv7i7i4OK2yqr7zllTV+mZmZlb6PU5NTdW0ce/6VvddsJSq1vXmzZvi0UcfFQ0bNhT16tUT/v7+Yty4cRVCirVs2+o+x0IIsWrVKuHg4CByc3N1tmEt29WUZEIIYdJdQ0RERERmxDE3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0R035PJZNi+fbulu0FERsJwQ0QWNWrUKMhksgpTv379LN01IrJStpbuABFRv379sG7dOq0yhUJhod4QkbXjnhsisjiFQgFvb2+tqX79+gDUh4xWrFiB/v37w8HBAc2bN8fmzZu15j927BgeeughODg4wMPDA+PHj0dhYaFWnbVr16Jdu3ZQKBTw8fHBxIkTtV6/evUqnnjiCTg6OiIoKAjffPONaVeaiEyG4YaI6rw5c+bgySefxJEjRzB8+HA888wzOH78OACgqKgIERERqF+/Pv744w9s2rQJe/bs0QovK1aswIQJEzB+/HgcO3YM33zzDVq0aKG1jLlz5+Lpp5/G0aNHMWDAAAwfPhzXr18363oSkZFY+s6dRHR/i46OFjY2NsLJyUlreuutt4QQ6rvUv/jii1rzhIaGipdeekkIIcTq1atF/fr1RWFhoeb1nTt3CrlcrrkztK+vr5g1a1alfQAgZs+erXleWFgoAIjvv//eaOtJRObDMTdEZHF9+/bFihUrtMoaNGigeRwWFqb1WlhYGA4fPgwAOH78OEJCQuDk5KR5vUePHlCpVMjIyIBMJsOlS5fw8MMPV9mHDh06aB47OTnB1dUVOTk5NV0lIrIghhsisjgnJ6cKh4mMxcHBQa969erV03ouk8mgUqlM0SUiMjGOuSGiOu+3336r8LxNmzYAgDZt2uDIkSMoKirSvL5v3z7I5XK0atUKLi4uCAgIQEpKiln7TESWwz03RGRxpaWlyMrK0iqztbWFp6cnAGDTpk3o0qUL/ve//+HLL7/E/v37sWbNGgDA8OHDERcXh+joaMTHx+PKlSuYNGkSRowYAS8vLwBAfHw8XnzxRTRq1Aj9+/dHQUEB9u3bh0mTJpl3RYnILBhuiMjikpKS4OPjo1XWqlUrnDhxAoD6TKavvvoKL7/8Mnx8fLBhwwa0bdsWAODo6Ijdu3djypQp6Nq1KxwdHfHkk0/i/fff17QVHR2NkpISLF68GNOmTYOnpyeeeuop860gEZmVTAghLN0JIqLKyGQybNu2DVFRUZbuChFZCY65ISIiIklhuCEiIiJJ4ZgbIqrTeOSciAzFPTdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQp/weZhGTtix0yPQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "\n",
    "display(HTML(\"<style>.output_scroll { height: 300px; }</style>\"))\n",
    "\n",
    "\n",
    "class DisplayCallback(tf.keras.callbacks.Callback):\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        clear_output(wait=True)\n",
    "        show_predictions()\n",
    "        print(\"\\nSample Prediction after epoch {}\\n\".format(epoch + 1))\n",
    "\n",
    "\n",
    "EPOCHS = 20\n",
    "VAL_SUBSPLITS = 5\n",
    "VALIDATION_STEPS = info.splits[\"test\"].num_examples // BATCH_SIZE // VAL_SUBSPLITS\n",
    "\n",
    "model_history = model.fit(\n",
    "    train_batches,\n",
    "    epochs=EPOCHS,\n",
    "    steps_per_epoch=STEPS_PER_EPOCH,\n",
    "    validation_steps=VALIDATION_STEPS,\n",
    "    validation_data=test_batches,\n",
    "    callbacks=[DisplayCallback()],\n",
    "    verbose=1,\n",
    ")\n",
    "\n",
    "loss = model_history.history[\"loss\"]\n",
    "val_loss = model_history.history[\"val_loss\"]\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(model_history.epoch, loss, \"r\", label=\"Training loss\")\n",
    "plt.plot(model_history.epoch, 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, 1])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce73353",
   "metadata": {},
   "source": [
    "### Make predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d0d488",
   "metadata": {},
   "source": [
    "Now, let's make some predictions. In the interest of saving time, the number of epochs was kept small, but you may set this higher to achieve more accurate results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b384977",
   "metadata": {},
   "source": [
    "show_predictions(test_batches, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e83f95d7",
   "metadata": {},
   "source": [
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/02_show_prediction.png\n",
    "---\n",
    "name: '02_display_prediction' \n",
    "width: 90%\n",
    "---\n",
    "An example of the output prediction\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd1c4121",
   "metadata": {},
   "source": [
    "## History & classic models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91455f0f",
   "metadata": {},
   "source": [
    "In the previous part, we learn the U-Net model. Besides, there are still many excellent models, we will introduce them is this part."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90adb67d",
   "metadata": {},
   "source": [
    "### Code for assisting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "12ec8599",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "layers = tf.keras.layers\n",
    "backend = tf.keras.backend\n",
    "\n",
    "\n",
    "class ResNet(object):\n",
    "    def __init__(self, version=\"ResNet50\", dilation=None, **kwargs):\n",
    "        \"\"\"\n",
    "        The implementation of ResNet based on Tensorflow.\n",
    "        :param version: 'ResNet50', 'ResNet101' or 'ResNet152'\n",
    "        :param dilation: Whether to use dilation strategy\n",
    "        :param kwargs: other parameters.\n",
    "        \"\"\"\n",
    "        super(ResNet, self).__init__(**kwargs)\n",
    "        params = {\n",
    "            \"ResNet50\": [2, 3, 5, 2],\n",
    "            \"ResNet101\": [2, 3, 22, 2],\n",
    "            \"ResNet152\": [2, 7, 35, 2],\n",
    "        }\n",
    "        self.version = version\n",
    "        assert version in params\n",
    "        self.params = params[version]\n",
    "\n",
    "        if dilation is None:\n",
    "            self.dilation = [1, 1]\n",
    "        else:\n",
    "            self.dilation = dilation\n",
    "        assert len(self.dilation) == 2\n",
    "\n",
    "    def _identity_block(\n",
    "        self, input_tensor, kernel_size, filters, stage, block, dilation=1\n",
    "    ):\n",
    "        \"\"\"The identity block is the block that has no conv layer at shortcut.\n",
    "        # Arguments\n",
    "            input_tensor: input tensor\n",
    "            kernel_size: default 3, the kernel size of\n",
    "                middle conv layer at main path\n",
    "            filters: list of integers, the filters of 3 conv layer at main path\n",
    "            stage: integer, current stage label, used for generating layer names\n",
    "            block: 'a','b'..., current block label, used for generating layer names\n",
    "        # Returns\n",
    "            Output tensor for the block.\n",
    "        \"\"\"\n",
    "        filters1, filters2, filters3 = filters\n",
    "        if backend.image_data_format() == \"channels_last\":\n",
    "            bn_axis = 3\n",
    "        else:\n",
    "            bn_axis = 1\n",
    "\n",
    "        if block > \"z\":\n",
    "            block = chr(ord(block) - ord(\"z\") + ord(\"A\") - 1)\n",
    "\n",
    "        conv_name_base = \"res\" + str(stage) + block + \"_branch\"\n",
    "        bn_name_base = \"bn\" + str(stage) + block + \"_branch\"\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters1, (1, 1), kernel_initializer=\"he_normal\", name=conv_name_base + \"2a\"\n",
    "        )(input_tensor)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2a\")(x)\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters2,\n",
    "            kernel_size,\n",
    "            padding=\"same\",\n",
    "            kernel_initializer=\"he_normal\",\n",
    "            name=conv_name_base + \"2b\",\n",
    "            dilation_rate=dilation,\n",
    "        )(x)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2b\")(x)\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters3, (1, 1), kernel_initializer=\"he_normal\", name=conv_name_base + \"2c\"\n",
    "        )(x)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2c\")(x)\n",
    "\n",
    "        x = layers.add([x, input_tensor])\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        return x\n",
    "\n",
    "    def _conv_block(\n",
    "        self,\n",
    "        input_tensor,\n",
    "        kernel_size,\n",
    "        filters,\n",
    "        stage,\n",
    "        block,\n",
    "        strides=(2, 2),\n",
    "        dilation=1,\n",
    "    ):\n",
    "        \"\"\"A block that has a conv layer at shortcut.\n",
    "        # Arguments\n",
    "            input_tensor: input tensor\n",
    "            kernel_size: default 3, the kernel size of\n",
    "                middle conv layer at main path\n",
    "            filters: list of integers, the filters of 3 conv layer at main path\n",
    "            stage: integer, current stage label, used for generating layer names\n",
    "            block: 'a','b'..., current block label, used for generating layer names\n",
    "            strides: Strides for the first conv layer in the block.\n",
    "        # Returns\n",
    "            Output tensor for the block.\n",
    "        Note that from stage 3,\n",
    "        the first conv layer at main path is with strides=(2, 2)\n",
    "        And the shortcut should have strides=(2, 2) as well\n",
    "        \"\"\"\n",
    "        filters1, filters2, filters3 = filters\n",
    "        if backend.image_data_format() == \"channels_last\":\n",
    "            bn_axis = 3\n",
    "        else:\n",
    "            bn_axis = 1\n",
    "        conv_name_base = \"res\" + str(stage) + block + \"_branch\"\n",
    "        bn_name_base = \"bn\" + str(stage) + block + \"_branch\"\n",
    "\n",
    "        strides = (1, 1) if dilation > 1 else strides\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters1,\n",
    "            (1, 1),\n",
    "            strides=strides,\n",
    "            kernel_initializer=\"he_normal\",\n",
    "            name=conv_name_base + \"2a\",\n",
    "        )(input_tensor)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2a\")(x)\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters2,\n",
    "            kernel_size,\n",
    "            padding=\"same\",\n",
    "            kernel_initializer=\"he_normal\",\n",
    "            name=conv_name_base + \"2b\",\n",
    "            dilation_rate=dilation,\n",
    "        )(x)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2b\")(x)\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters3, (1, 1), kernel_initializer=\"he_normal\", name=conv_name_base + \"2c\"\n",
    "        )(x)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2c\")(x)\n",
    "\n",
    "        shortcut = layers.Conv2D(\n",
    "            filters3,\n",
    "            (1, 1),\n",
    "            strides=strides,\n",
    "            kernel_initializer=\"he_normal\",\n",
    "            name=conv_name_base + \"1\",\n",
    "        )(input_tensor)\n",
    "        shortcut = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"1\")(\n",
    "            shortcut\n",
    "        )\n",
    "\n",
    "        x = layers.add([x, shortcut])\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        return x\n",
    "\n",
    "    def __call__(self, inputs, output_stages=\"c5\", **kwargs):\n",
    "        \"\"\"\n",
    "        call for ResNet50, ResNet101 or ResNet152.\n",
    "        :param inputs: a 4-D tensor.\n",
    "        :param output_stages: str or a list of str containing the output stages.\n",
    "        :param kwargs: other parameters.\n",
    "        :return: the output of different stages.\n",
    "        \"\"\"\n",
    "        if backend.image_data_format() == \"channels_last\":\n",
    "            bn_axis = 3\n",
    "        else:\n",
    "            bn_axis = 1\n",
    "\n",
    "        dilation = self.dilation\n",
    "\n",
    "        x = layers.ZeroPadding2D(padding=(3, 3), name=\"conv1_pad\")(inputs)\n",
    "        x = layers.Conv2D(\n",
    "            64,\n",
    "            (7, 7),\n",
    "            strides=(2, 2),\n",
    "            padding=\"valid\",\n",
    "            kernel_initializer=\"he_normal\",\n",
    "            name=\"conv1\",\n",
    "        )(x)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=\"bn_conv1\")(x)\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        x = layers.ZeroPadding2D(padding=(1, 1), name=\"pool1_pad\")(x)\n",
    "        x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)\n",
    "        c1 = x\n",
    "\n",
    "        x = self._conv_block(x, 3, [64, 64, 256], stage=2, block=\"a\", strides=(1, 1))\n",
    "        for i in range(self.params[0]):\n",
    "            x = self._identity_block(\n",
    "                x, 3, [64, 64, 256], stage=2, block=chr(ord(\"b\") + i)\n",
    "            )\n",
    "        c2 = x\n",
    "\n",
    "        x = self._conv_block(x, 3, [128, 128, 512], stage=3, block=\"a\")\n",
    "        for i in range(self.params[1]):\n",
    "            x = self._identity_block(\n",
    "                x, 3, [128, 128, 512], stage=3, block=chr(ord(\"b\") + i)\n",
    "            )\n",
    "        c3 = x\n",
    "\n",
    "        x = self._conv_block(\n",
    "            x, 3, [256, 256, 1024], stage=4, block=\"a\", dilation=dilation[0]\n",
    "        )\n",
    "        for i in range(self.params[2]):\n",
    "            x = self._identity_block(\n",
    "                x,\n",
    "                3,\n",
    "                [256, 256, 1024],\n",
    "                stage=4,\n",
    "                block=chr(ord(\"b\") + i),\n",
    "                dilation=dilation[0],\n",
    "            )\n",
    "        c4 = x\n",
    "\n",
    "        x = self._conv_block(\n",
    "            x, 3, [512, 512, 2048], stage=5, block=\"a\", dilation=dilation[1]\n",
    "        )\n",
    "        for i in range(self.params[3]):\n",
    "            x = self._identity_block(\n",
    "                x,\n",
    "                3,\n",
    "                [512, 512, 2048],\n",
    "                stage=5,\n",
    "                block=chr(ord(\"b\") + i),\n",
    "                dilation=dilation[1],\n",
    "            )\n",
    "        c5 = x\n",
    "\n",
    "        self.outputs = {\"c1\": c1, \"c2\": c2, \"c3\": c3, \"c4\": c4, \"c5\": c5}\n",
    "\n",
    "        if type(output_stages) is not list:\n",
    "            return self.outputs[output_stages]\n",
    "        else:\n",
    "            return [self.outputs[ci] for ci in output_stages]\n",
    "\n",
    "\n",
    "class Network(object):\n",
    "    def __init__(\n",
    "        self, num_classes, version=\"PAN\", base_model=\"ResNet50\", dilation=None, **kwargs\n",
    "    ):\n",
    "        super(Network, self).__init__(**kwargs)\n",
    "        if base_model in [\"ResNet50\", \"ResNet101\", \"ResNet152\"]:\n",
    "            self.encoder = ResNet(base_model, dilation=dilation)\n",
    "        else:\n",
    "            raise ValueError(\n",
    "                \"The base model {model} is not in the supported model list!!!\".format(\n",
    "                    model=base_model\n",
    "                )\n",
    "            )\n",
    "\n",
    "        self.num_classes = num_classes\n",
    "        self.version = version\n",
    "        self.base_model = base_model\n",
    "\n",
    "    def __call__(self, inputs, **kwargs):\n",
    "        return inputs\n",
    "\n",
    "    def get_version(self):\n",
    "        return self.version\n",
    "\n",
    "    def get_base_model(self):\n",
    "        return self.base_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17b3fa7",
   "metadata": {},
   "source": [
    "### FCN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df340ed3",
   "metadata": {},
   "source": [
    ":::{note}\n",
    "The link for paper is https://arxiv.org/pdf/1411.4038v2.pdf.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a411a89a",
   "metadata": {},
   "source": [
    "FCN is the first work to train FCNs end-to-end for pixel-wise prediction and from supervised pre-training. Semantic segmentation faces an inherent tension between semantics and location: global information resolves what while local information resolves where. Deep feature hierarchies jointly encode location and semantics in a localto-global pyramid. FCN defines a novel “skip” architecture to combine deep, coarse, semantic information and shallow, fine, appearance information."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f3a097c",
   "metadata": {},
   "source": [
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/03_structure_FCN.png\n",
    "---\n",
    "name: '03_display_structure_of_FCN' \n",
    "width: 90%\n",
    "---\n",
    "[ The structure of FCN ](https://arxiv.org/pdf/1411.4038v2.pdf)\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a569084",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "19b1dec9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "layers = tf.keras.layers\n",
    "models = tf.keras.models\n",
    "backend = tf.keras.backend\n",
    "\n",
    "\n",
    "class FCN(Network):\n",
    "    def __init__(self, num_classes, version=\"FCN-8s\", base_model=\"ResNet50\", **kwargs):\n",
    "        \"\"\"\n",
    "        The initialization of FCN-8s/16s/32s.\n",
    "        :param num_classes: the number of predicted classes.\n",
    "        :param version: 'FCN-8s', 'FCN-16s' or 'FCN-32s'.\n",
    "        :param base_model: the backbone model\n",
    "        :param kwargs: other parameters\n",
    "        \"\"\"\n",
    "        fcn = {\n",
    "            \"FCN-8s\": self._fcn_8s,\n",
    "            \"FCN-16s\": self._fcn_16s,\n",
    "            \"FCN-32s\": self._fcn_32s,\n",
    "        }\n",
    "        base_model = \"ResNet50\" if base_model is None else base_model\n",
    "\n",
    "        assert version in fcn\n",
    "        self.fcn = fcn[version]\n",
    "        super(FCN, self).__init__(num_classes, version, base_model, **kwargs)\n",
    "\n",
    "    def __call__(self, inputs=None, input_size=None, **kwargs):\n",
    "        assert inputs is not None or input_size is not None\n",
    "\n",
    "        if inputs is None:\n",
    "            assert isinstance(input_size, tuple)\n",
    "            inputs = layers.Input(shape=input_size + (3,))\n",
    "        return self.fcn(inputs)\n",
    "\n",
    "    def _conv_relu(self, x, filters, kernel_size=1):\n",
    "        x = layers.Conv2D(\n",
    "            filters, kernel_size, padding=\"same\", kernel_initializer=\"he_normal\"\n",
    "        )(x)\n",
    "        x = layers.ReLU()(x)\n",
    "        return x\n",
    "\n",
    "    def _fcn_32s(self, inputs):\n",
    "        num_classes = self.num_classes\n",
    "\n",
    "        x = self.encoder(inputs)\n",
    "        x = self._conv_relu(x, 4096, 7)\n",
    "        x = layers.Dropout(rate=0.5)(x)\n",
    "        x = self._conv_relu(x, 4096, 1)\n",
    "        x = layers.Dropout(rate=0.5)(x)\n",
    "\n",
    "        x = layers.Conv2D(num_classes, 1, kernel_initializer=\"he_normal\")(x)\n",
    "        x = layers.Conv2DTranspose(\n",
    "            num_classes, 64, strides=32, padding=\"same\", kernel_initializer=\"he_normal\"\n",
    "        )(x)\n",
    "\n",
    "        outputs = x\n",
    "        return models.Model(inputs, outputs, name=self.version)\n",
    "\n",
    "    def _fcn_16s(self, inputs):\n",
    "        num_classes = self.num_classes\n",
    "\n",
    "        if self.base_model in [\n",
    "            \"DenseNet121\",\n",
    "            \"DenseNet169\",\n",
    "            \"DenseNet201\",\n",
    "            \"DenseNet264\",\n",
    "            \"Xception\",\n",
    "            \"Xception-DeepLab\",\n",
    "        ]:\n",
    "            c4, c5 = self.encoder(inputs, output_stages=[\"c3\", \"c5\"])\n",
    "        else:\n",
    "            c4, c5 = self.encoder(inputs, output_stages=[\"c4\", \"c5\"])\n",
    "\n",
    "        x = self._conv_relu(c5, 4096, 7)\n",
    "        x = layers.Dropout(rate=0.5)(x)\n",
    "        x = self._conv_relu(x, 4096, 1)\n",
    "        x = layers.Dropout(rate=0.5)(x)\n",
    "\n",
    "        x = layers.Conv2D(num_classes, 1, kernel_initializer=\"he_normal\")(x)\n",
    "        x = layers.Conv2DTranspose(\n",
    "            num_classes, 4, strides=2, padding=\"same\", kernel_initializer=\"he_normal\"\n",
    "        )(x)\n",
    "        c4 = layers.Conv2D(num_classes, 1, kernel_initializer=\"he_normal\")(c4)\n",
    "        x = layers.Add()([x, c4])\n",
    "\n",
    "        x = layers.Conv2DTranspose(\n",
    "            num_classes, 32, strides=16, padding=\"same\", kernel_initializer=\"he_normal\"\n",
    "        )(x)\n",
    "\n",
    "        outputs = x\n",
    "        return models.Model(inputs, outputs, name=self.version)\n",
    "\n",
    "    def _fcn_8s(self, inputs):\n",
    "        num_classes = self.num_classes\n",
    "\n",
    "        if self.base_model in [\n",
    "            \"VGG16\",\n",
    "            \"VGG19\",\n",
    "            \"ResNet50\",\n",
    "            \"ResNet101\",\n",
    "            \"ResNet152\",\n",
    "            \"MobileNetV1\",\n",
    "            \"MobileNetV2\",\n",
    "        ]:\n",
    "            c3, c4, c5 = self.encoder(inputs, output_stages=[\"c3\", \"c4\", \"c5\"])\n",
    "        else:\n",
    "            c3, c4, c5 = self.encoder(inputs, output_stages=[\"c2\", \"c3\", \"c5\"])\n",
    "\n",
    "        x = self._conv_relu(c5, 4096, 7)\n",
    "        x = layers.Dropout(rate=0.5)(x)\n",
    "        x = self._conv_relu(x, 4096, 1)\n",
    "        x = layers.Dropout(rate=0.5)(x)\n",
    "\n",
    "        x = layers.Conv2D(num_classes, 1, kernel_initializer=\"he_normal\")(x)\n",
    "        x = layers.Conv2DTranspose(\n",
    "            num_classes, 4, strides=2, padding=\"same\", kernel_initializer=\"he_normal\"\n",
    "        )(x)\n",
    "        c4 = layers.Conv2D(num_classes, 1)(c4)\n",
    "        x = layers.Add()([x, c4])\n",
    "\n",
    "        x = layers.Conv2DTranspose(\n",
    "            num_classes, 4, strides=2, padding=\"same\", kernel_initializer=\"he_normal\"\n",
    "        )(x)\n",
    "        c3 = layers.Conv2D(num_classes, 1)(c3)\n",
    "        x = layers.Add()([x, c3])\n",
    "\n",
    "        x = layers.Conv2DTranspose(\n",
    "            num_classes, 16, strides=8, padding=\"same\", kernel_initializer=\"he_normal\"\n",
    "        )(x)\n",
    "\n",
    "        outputs = x\n",
    "        return models.Model(inputs, outputs, name=self.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c7a6e7e",
   "metadata": {},
   "source": [
    "### SegNet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e699947",
   "metadata": {},
   "source": [
    ":::{note}\n",
    "The link for paper is https://arxiv.org/pdf/1511.00561v3.pdf.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "347b45f2",
   "metadata": {},
   "source": [
    "SegNet is a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. Here is the structure of SegNet:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af284e4",
   "metadata": {},
   "source": [
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/04_structure_SegNet.png\n",
    "---\n",
    "name: '04_display_structure_of_SegNet' \n",
    "width: 90%\n",
    "---\n",
    "[ The structure of SegNet ]https://arxiv.org/pdf/1511.00561v3.pdf )\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0daa8b3",
   "metadata": {},
   "source": [
    "As we have learnt U-Net, SegNet is similar to it, the main difference is that U-Net does not reuse pooling indices but instead transfers the entire feature map (at the cost of more memory) to the corresponding decoders and concatenates them to upsampled (via deconvolution) decoder feature maps."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66277c8c",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "294880bc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "layers = tf.keras.layers\n",
    "models = tf.keras.models\n",
    "backend = tf.keras.backend\n",
    "\n",
    "\n",
    "class SegNet(Network):\n",
    "    def __init__(self, num_classes, version=\"SegNet\", base_model=\"ResNet50\", **kwargs):\n",
    "        \"\"\"\n",
    "        The initialization of SegNet or Bayesian-SegNet.\n",
    "        :param num_classes: the number of predicted classes.\n",
    "        :param version: 'SegNet' or 'Bayesian-SegNet'.\n",
    "        :param base_model: the backbone model\n",
    "        :param kwargs: other parameters\n",
    "        \"\"\"\n",
    "        base_model = \"ResNet50\" if base_model is None else base_model\n",
    "        assert version in [\"SegNet\", \"Bayesian-SegNet\"]\n",
    "        assert base_model in [\"ResNet50\", \"ResNet101\", \"ResNet152\"]\n",
    "        super(SegNet, self).__init__(num_classes, version, base_model, **kwargs)\n",
    "\n",
    "    def __call__(self, inputs=None, input_size=None, **kwargs):\n",
    "        assert inputs is not None or input_size is not None\n",
    "\n",
    "        if inputs is None:\n",
    "            assert isinstance(input_size, tuple)\n",
    "            inputs = layers.Input(shape=input_size + (3,))\n",
    "        return self._segnet(inputs)\n",
    "\n",
    "    def _conv_bn_relu(self, x, filters, kernel_size=1, strides=1):\n",
    "        x = layers.Conv2D(\n",
    "            filters,\n",
    "            kernel_size,\n",
    "            strides=strides,\n",
    "            padding=\"same\",\n",
    "            kernel_initializer=\"he_normal\",\n",
    "        )(x)\n",
    "        x = layers.BatchNormalization()(x)\n",
    "        x = layers.ReLU()(x)\n",
    "        return x\n",
    "\n",
    "    def _segnet(self, inputs):\n",
    "        num_classes = self.num_classes\n",
    "        dropout = True if self.version == \"Bayesian-SegNet\" else False\n",
    "\n",
    "        x = self.encoder(inputs)\n",
    "\n",
    "        if dropout:\n",
    "            x = layers.Dropout(rate=0.5)(x)\n",
    "        x = layers.UpSampling2D(size=(2, 2))(x)\n",
    "        x = self._conv_bn_relu(x, 512, 3, strides=1)\n",
    "        x = self._conv_bn_relu(x, 512, 3, strides=1)\n",
    "        x = self._conv_bn_relu(x, 512, 3, strides=1)\n",
    "\n",
    "        if dropout:\n",
    "            x = layers.Dropout(rate=0.5)(x)\n",
    "        x = layers.UpSampling2D(size=(2, 2))(x)\n",
    "        x = self._conv_bn_relu(x, 512, 3, strides=1)\n",
    "        x = self._conv_bn_relu(x, 512, 3, strides=1)\n",
    "        x = self._conv_bn_relu(x, 256, 3, strides=1)\n",
    "\n",
    "        if dropout:\n",
    "            x = layers.Dropout(rate=0.5)(x)\n",
    "        x = layers.UpSampling2D(size=(2, 2))(x)\n",
    "        x = self._conv_bn_relu(x, 256, 3, strides=1)\n",
    "        x = self._conv_bn_relu(x, 256, 3, strides=1)\n",
    "        x = self._conv_bn_relu(x, 128, 3, strides=1)\n",
    "\n",
    "        if dropout:\n",
    "            x = layers.Dropout(rate=0.5)(x)\n",
    "        x = layers.UpSampling2D(size=(2, 2))(x)\n",
    "        x = self._conv_bn_relu(x, 128, 3, strides=1)\n",
    "        x = self._conv_bn_relu(x, 64, 3, strides=1)\n",
    "\n",
    "        if dropout:\n",
    "            x = layers.Dropout(rate=0.5)(x)\n",
    "        x = layers.UpSampling2D(size=(2, 2))(x)\n",
    "        x = self._conv_bn_relu(x, 64, 3, strides=1)\n",
    "        x = layers.Conv2D(num_classes, 1, strides=1, kernel_initializer=\"he_normal\")(x)\n",
    "        x = layers.BatchNormalization()(x)\n",
    "\n",
    "        outputs = x\n",
    "        return models.Model(inputs, outputs, name=self.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e842518",
   "metadata": {},
   "source": [
    "### DeepLab V3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb12271",
   "metadata": {},
   "source": [
    "```{note}\n",
    "The link for paper is https://arxiv.org/pdf/1706.05587v3.pdf.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79bd0c2",
   "metadata": {},
   "source": [
    "DeepLab V3 revisits applying dilated convolution, which allows us to effectively enlarge the field of view of filters to incorporate multi-scale context, in the framework of both cascaded modules and spatial pyramid pooling. It consists of dilated convolution with various rates and batch normalization layers. DeepLab experiments with laying out the modules in cascade or in parallel."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c189e13",
   "metadata": {},
   "source": [
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/05_cascade_DeepLab_structure.png\n",
    "---\n",
    "name: '05_cascade_dilated_conv_structure' \n",
    "width: 90%\n",
    "---\n",
    "[ Cascade dilated convolution for DeepLab ] (https://arxiv.org/pdf/1706.05587v3.pdf)\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaefa24d",
   "metadata": {},
   "source": [
    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/06_parallel_DeepLab_structure.png\n",
    "---\n",
    "name: '06_parallel_DeepLab_structure' \n",
    "width: 90%\n",
    "---\n",
    "[ Parallel dilated convolution for DeepLab ] (https://arxiv.org/pdf/1706.05587v3.pdf)\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73367012",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f48bcc14",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Input, Dropout, BatchNormalization, Activation, Add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f1d963f2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "layers = tf.keras.layers\n",
    "models = tf.keras.models\n",
    "backend = tf.keras.backend\n",
    "\n",
    "\n",
    "class DeepLabV3(Network):\n",
    "    def __init__(\n",
    "        self, num_classes, version=\"DeepLabV3\", base_model=\"ResNet50\", **kwargs\n",
    "    ):\n",
    "        \"\"\"\n",
    "        The initialization of DeepLabV3.\n",
    "        :param num_classes: the number of predicted classes.\n",
    "        :param version: 'DeepLabV3'\n",
    "        :param base_model: the backbone model\n",
    "        :param kwargs: other parameters\n",
    "        \"\"\"\n",
    "        dilation = [1, 2]\n",
    "        base_model = \"ResNet50\" if base_model is None else base_model\n",
    "\n",
    "        assert version == \"DeepLabV3\"\n",
    "        assert base_model in [\"ResNet50\", \"ResNet101\", \"ResNet152\"]\n",
    "        super(DeepLabV3, self).__init__(\n",
    "            num_classes, version, base_model, dilation, **kwargs\n",
    "        )\n",
    "        self.dilation = dilation\n",
    "\n",
    "    def __call__(self, inputs=None, input_size=None, **kwargs):\n",
    "        assert inputs is not None or input_size is not None\n",
    "\n",
    "        if inputs is None:\n",
    "            assert isinstance(input_size, tuple)\n",
    "            inputs = layers.Input(shape=input_size + (3,))\n",
    "        return self._deeplabv3(inputs)\n",
    "\n",
    "    def _deeplabv3(self, inputs):\n",
    "        multi_grid = [1, 2, 4]\n",
    "        num_classes = self.num_classes\n",
    "        dilation = self.dilation\n",
    "\n",
    "        _, h, w, _ = backend.int_shape(inputs)\n",
    "        self.aspp_size = (h // 16, w // 16)\n",
    "\n",
    "        x = self.encoder(inputs, output_stages=\"c4\")\n",
    "\n",
    "        x = self._conv_block(\n",
    "            x, 3, [512, 512, 2048], stage=5, block=\"a\", dilation=dilation[1]\n",
    "        )\n",
    "        for i in range(2):\n",
    "            x = self._identity_block(\n",
    "                x,\n",
    "                3,\n",
    "                [512, 512, 2048],\n",
    "                stage=5,\n",
    "                block=chr(ord(\"b\") + i),\n",
    "                dilation=dilation[1] * multi_grid[i],\n",
    "            )\n",
    "        x = self._aspp(x, 256)\n",
    "        x = layers.Conv2D(num_classes, 1, strides=1, kernel_initializer=\"he_normal\")(x)\n",
    "        x = layers.UpSampling2D(size=(16, 16), interpolation=\"bilinear\")(x)\n",
    "\n",
    "        outputs = x\n",
    "        return models.Model(inputs, outputs, name=self.version)\n",
    "\n",
    "    def _aspp(self, x, out_filters):\n",
    "        xs = list()\n",
    "        x1 = layers.Conv2D(out_filters, 1, strides=1, kernel_initializer=\"he_normal\")(x)\n",
    "        xs.append(x1)\n",
    "\n",
    "        for i in range(3):\n",
    "            xi = layers.Conv2D(\n",
    "                out_filters, 3, strides=1, padding=\"same\", dilation_rate=6 * (i + 1)\n",
    "            )(x)\n",
    "            xs.append(xi)\n",
    "        img_pool = layers.GlobalAveragePooling2D()(x)\n",
    "        img_pool = layers.Conv2D(out_filters, 1, kernel_initializer=\"he_normal\")(\n",
    "            img_pool\n",
    "        )\n",
    "        img_pool = layers.UpSampling2D(size=self.aspp_size, interpolation=\"bilinear\")(\n",
    "            img_pool\n",
    "        )\n",
    "        xs.append(img_pool)\n",
    "\n",
    "        x = layers.Concatenate()(xs)\n",
    "        x = layers.Conv2D(out_filters, 1, strides=1, kernel_initializer=\"he_normal\")(x)\n",
    "        x = layers.BatchNormalization()(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "    def _identity_block(\n",
    "        self, input_tensor, kernel_size, filters, stage, block, dilation=1\n",
    "    ):\n",
    "        \"\"\"The identity block is the block that has no conv layer at shortcut.\n",
    "        # Arguments\n",
    "            input_tensor: input tensor\n",
    "            kernel_size: default 3, the kernel size of\n",
    "                middle conv layer at main path\n",
    "            filters: list of integers, the filters of 3 conv layer at main path\n",
    "            stage: integer, current stage label, used for generating layer names\n",
    "            block: 'a','b'..., current block label, used for generating layer names\n",
    "        # Returns\n",
    "            Output tensor for the block.\n",
    "        \"\"\"\n",
    "        filters1, filters2, filters3 = filters\n",
    "        if backend.image_data_format() == \"channels_last\":\n",
    "            bn_axis = 3\n",
    "        else:\n",
    "            bn_axis = 1\n",
    "        conv_name_base = \"res\" + str(stage) + block + \"_branch\"\n",
    "        bn_name_base = \"bn\" + str(stage) + block + \"_branch\"\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters1, (1, 1), kernel_initializer=\"he_normal\", name=conv_name_base + \"2a\"\n",
    "        )(input_tensor)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2a\")(x)\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters2,\n",
    "            kernel_size,\n",
    "            padding=\"same\",\n",
    "            kernel_initializer=\"he_normal\",\n",
    "            name=conv_name_base + \"2b\",\n",
    "            dilation_rate=dilation,\n",
    "        )(x)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2b\")(x)\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters3, (1, 1), kernel_initializer=\"he_normal\", name=conv_name_base + \"2c\"\n",
    "        )(x)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2c\")(x)\n",
    "\n",
    "        x = layers.add([x, input_tensor])\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        return x\n",
    "\n",
    "    def _conv_block(\n",
    "        self,\n",
    "        input_tensor,\n",
    "        kernel_size,\n",
    "        filters,\n",
    "        stage,\n",
    "        block,\n",
    "        strides=(2, 2),\n",
    "        dilation=1,\n",
    "    ):\n",
    "        \"\"\"A block that has a conv layer at shortcut.\n",
    "        # Arguments\n",
    "            input_tensor: input tensor\n",
    "            kernel_size: default 3, the kernel size of\n",
    "                middle conv layer at main path\n",
    "            filters: list of integers, the filters of 3 conv layer at main path\n",
    "            stage: integer, current stage label, used for generating layer names\n",
    "            block: 'a','b'..., current block label, used for generating layer names\n",
    "            strides: Strides for the first conv layer in the block.\n",
    "        # Returns\n",
    "            Output tensor for the block.\n",
    "        Note that from stage 3,\n",
    "        the first conv layer at main path is with strides=(2, 2)\n",
    "        And the shortcut should have strides=(2, 2) as well\n",
    "        \"\"\"\n",
    "        filters1, filters2, filters3 = filters\n",
    "        if backend.image_data_format() == \"channels_last\":\n",
    "            bn_axis = 3\n",
    "        else:\n",
    "            bn_axis = 1\n",
    "        conv_name_base = \"res\" + str(stage) + block + \"_branch\"\n",
    "        bn_name_base = \"bn\" + str(stage) + block + \"_branch\"\n",
    "\n",
    "        strides = (1, 1) if dilation > 1 else strides\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters1,\n",
    "            (1, 1),\n",
    "            strides=strides,\n",
    "            name=conv_name_base + \"2a\",\n",
    "            kernel_initializer=\"he_normal\",\n",
    "        )(input_tensor)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2a\")(x)\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters2,\n",
    "            kernel_size,\n",
    "            padding=\"same\",\n",
    "            name=conv_name_base + \"2b\",\n",
    "            kernel_initializer=\"he_normal\",\n",
    "            dilation_rate=dilation,\n",
    "        )(x)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2b\")(x)\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "        x = layers.Conv2D(\n",
    "            filters3, (1, 1), name=conv_name_base + \"2c\", kernel_initializer=\"he_normal\"\n",
    "        )(x)\n",
    "        x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"2c\")(x)\n",
    "\n",
    "        shortcut = layers.Conv2D(\n",
    "            filters3,\n",
    "            (1, 1),\n",
    "            strides=strides,\n",
    "            name=conv_name_base + \"1\",\n",
    "            kernel_initializer=\"he_normal\",\n",
    "        )(input_tensor)\n",
    "        shortcut = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + \"1\")(\n",
    "            shortcut\n",
    "        )\n",
    "\n",
    "        x = layers.add([x, shortcut])\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52450f86",
   "metadata": {},
   "source": [
    "## Your turn! 🚀"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0994ed1",
   "metadata": {},
   "source": [
    "<!-- Assignment - [Comparing edge-based and region-based segmentation](../../assignments/deep-learning/image-segmentation/comparing-edge-based-and-region-based-segmentation.ipynb) -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3be7f0a",
   "metadata": {},
   "source": [
    "## Acknowledgments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c3074e6",
   "metadata": {},
   "source": [
    "Thanks to [Yang Lu](https://github.com/luyanger1799) for creating the open-source project [Amazing-Semantic-Segmentation](https://github.com/luyanger1799/Amazing-Semantic-Segmentation), [tensorflow](https://github.com/tensorflow) for creating the open-source course [examples](https://github.com/tensorflow/examples). They inspire the majority of the content in this chapter."
   ]
  }
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