best_cnn_segmentation_connected_components_512x512_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "igEd7obtcxR2"
   },
   "source": [
    "# Approach\n",
    "\n",
    "* Firstly a convolutional neural network is used to segment the image, using the bounding boxes directly as a mask. \n",
    "* Secondly connected components is used to separate multiple areas of predicted pneumonia.\n",
    "* Finally a bounding box is simply drawn around every connected component.\n",
    "\n",
    "# Network\n",
    "\n",
    "* The network consists of a number of residual blocks with convolutions and downsampling blocks with max pooling.\n",
    "* At the end of the network a single upsampling layer converts the output to the same shape as the input.\n",
    "\n",
    "As the input to the network is 256 by 256 (instead of the original 1024 by 1024) and the network downsamples a number of times without any meaningful upsampling (the final upsampling is just to match in 256 by 256 mask) the final prediction is very crude. If the network downsamples 4 times the final bounding boxes can only change with at least 16 pixels.\n",
    "\n",
    "**Edit by EAS** - Change input image size to 320x320."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LHncOor-cxSS"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import random\n",
    "import pydicom\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from skimage import measure\n",
    "from skimage.transform import resize\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hE3jDmgudDDP"
   },
   "outputs": [],
   "source": [
    "# enter your Kaggle credentionals here\n",
    "os.environ['KAGGLE_USERNAME']=\"skooch\"\n",
    "os.environ['KAGGLE_KEY']=\"42f8a02ee92cc773d1dbe66565673ad3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as patches\n",
    "coords = np.arange(0, 1024, 64)\n",
    "\n",
    "fig,ax = plt.subplots(1, figsize=(10,10))\n",
    "\n",
    "rect_1 = patches.Rectangle((195,511),218,271,linewidth=1,edgecolor='r',facecolor='none')\n",
    "plt.plot(304, 646.5, marker=\"o\", color=\"r\")\n",
    "rect_2 = patches.Rectangle((612,214),109,89,linewidth=1,edgecolor='b',facecolor='none')\n",
    "plt.plot(666.5, 258.5, marker=\"o\", color=\"b\")\n",
    "rect_3 = patches.Rectangle((610,599),265,245,linewidth=1,edgecolor='g',facecolor='none')\n",
    "plt.plot(742.5, 721.5, marker=\"o\", color=\"g\")\n",
    "ax.add_patch(rect_1)\n",
    "ax.add_patch(rect_2)\n",
    "ax.add_patch(rect_3)\n",
    "plt.xticks(coords)\n",
    "plt.yticks(coords)\n",
    "for item in coords:\n",
    "    plt.axvline(item, linewidth=0.5)\n",
    "    plt.axhline(item, linewidth=0.5)\n",
    "plt.xlim(0,1024)\n",
    "plt.ylim(0,1024)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(4.0, 10.0), (10.0, 4.0), (11.0, 11.0)]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxes = [\n",
    "    [195,511,218,271],\n",
    "    [612,214,109,89],\n",
    "    [610,599,265,245]\n",
    "]\n",
    "\n",
    "centers = []\n",
    "\n",
    "for box in boxes:\n",
    "    x,y,w,h = box\n",
    "    center_x = x + (w / 2)\n",
    "    center_y = y + (h / 2)\n",
    "    centers.append((center_x,center_y))\n",
    "\n",
    "pixel_locs = []    \n",
    "for center in centers:\n",
    "    x_pixel = center[0] // 64\n",
    "    y_pixel = center[1] // 64\n",
    "    \n",
    "    pixel_locs.append((x_pixel,y_pixel))\n",
    "    \n",
    "pixel_locs    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "304. // 64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sSWY-U2TcxSn"
   },
   "source": [
    "# Load pneumonia locations\n",
    "\n",
    "Table contains [filename : pneumonia location] pairs per row. \n",
    "* If a filename contains multiple pneumonia, the table contains multiple rows with the same filename but different pneumonia locations. \n",
    "* If a filename contains no pneumonia it contains a single row with an empty pneumonia location.\n",
    "\n",
    "The code below loads the table and transforms it into a dictionary. \n",
    "* The dictionary uses the filename as key and a list of pneumonia locations in that filename as value. \n",
    "* If a filename is not present in the dictionary it means that it contains no pneumonia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "m_0ZGQyLdQS8",
    "outputId": "4848c0ce-4d75-4781-d01f-91cbd7626d77"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dir: ./stage_1_train_images\n"
     ]
    }
   ],
   "source": [
    "ROOT_DIR = \"./\"\n",
    "\n",
    "train_dicom_dir = os.path.join(ROOT_DIR, 'stage_1_train_images')\n",
    "test_dicom_dir = os.path.join(ROOT_DIR, 'stage_1_test_images')\n",
    "print(\"Train dir:\", train_dicom_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hopsRmICcxSs"
   },
   "outputs": [],
   "source": [
    "# empty dictionary\n",
    "pneumonia_locations = {}\n",
    "# load table\n",
    "with open(os.path.join('./stage_1_train_labels.csv'), mode='r') as infile:\n",
    "    # open reader\n",
    "    reader = csv.reader(infile)\n",
    "    # skip header\n",
    "    next(reader, None)\n",
    "    # loop through rows\n",
    "    for rows in reader:\n",
    "        # retrieve information\n",
    "        filename = rows[0]\n",
    "        location = rows[1:5]\n",
    "        pneumonia = rows[5]\n",
    "        # if row contains pneumonia add label to dictionary\n",
    "        # which contains a list of pneumonia locations per filename\n",
    "        if pneumonia == '1':\n",
    "            # convert string to float to int\n",
    "            location = [int(float(i)) for i in location]\n",
    "            # save pneumonia location in dictionary\n",
    "            if filename in pneumonia_locations:\n",
    "                pneumonia_locations[filename].append(location)\n",
    "            else:\n",
    "                pneumonia_locations[filename] = [location]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UoB6GBhIcxS6"
   },
   "source": [
    "# Load filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "b_iB-IftcxTE",
    "outputId": "89a05e05-f2d9-45fc-d1ef-1a0cc289f98d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n train samples 23116\n",
      "n valid samples 2568\n"
     ]
    }
   ],
   "source": [
    "random.seed(17)\n",
    "\n",
    "# load and shuffle filenames\n",
    "folder = './stage_1_train_images'\n",
    "filenames = os.listdir(folder)\n",
    "random.shuffle(filenames)\n",
    "# split into train and validation filenames\n",
    "n_valid_samples = int(len(filenames) * 0.1)\n",
    "train_filenames = filenames[n_valid_samples:]\n",
    "valid_filenames = filenames[:n_valid_samples]\n",
    "print('n train samples', len(train_filenames))\n",
    "print('n valid samples', len(valid_filenames))\n",
    "n_train_samples = len(filenames) - n_valid_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lorV7ZmDcxTa"
   },
   "source": [
    " # Data generator\n",
    "\n",
    "The dataset is too large to fit into memory, so we need to create a generator that loads data on the fly.\n",
    "\n",
    "* The generator takes in some filenames, batch_size and other parameters.\n",
    "\n",
    "* The generator outputs a random batch of numpy images and numpy masks.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dFbLdFxVehhB"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 12\n",
    "IMAGE_SIZE = 512\n",
    "CHECKPOINT_PATH = \"model4_512.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "clGBqJ9w-GZ3",
    "outputId": "ed291490-5398-4be7-d4fd-f159fa03aac0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated property [core/project].\n",
      "CommandException: No URLs matched: gs://pneumonia/model8_448.h5\n"
     ]
    }
   ],
   "source": [
    "# upload checkpoint to GCS\n",
    "project_id = 'mammography-198911'\n",
    "bucket_name = 'pneumonia'\n",
    "\n",
    "!gcloud config set project {project_id}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TueY1bVlcxTg"
   },
   "outputs": [],
   "source": [
    "class generator(keras.utils.Sequence):\n",
    "    \n",
    "    def __init__(self, folder, filenames, pneumonia_locations=None, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=True, augment=False, predict=False):\n",
    "        self.folder = folder\n",
    "        self.filenames = filenames\n",
    "        self.pneumonia_locations = pneumonia_locations\n",
    "        self.batch_size = batch_size\n",
    "        self.image_size = image_size\n",
    "        self.shuffle = shuffle\n",
    "        self.augment = augment\n",
    "        self.predict = predict\n",
    "        self.on_epoch_end()\n",
    "        \n",
    "    def __load__(self, filename):\n",
    "        # load dicom file as numpy array\n",
    "        img = pydicom.dcmread(os.path.join(self.folder, filename)).pixel_array\n",
    "        # create empty mask\n",
    "        msk = np.zeros(img.shape)\n",
    "        # get filename without extension\n",
    "        filename = filename.split('.')[0]\n",
    "        # if image contains pneumonia\n",
    "        if filename in pneumonia_locations:\n",
    "            # loop through pneumonia\n",
    "            for location in pneumonia_locations[filename]:\n",
    "                # add 1's at the location of the pneumonia\n",
    "                x, y, w, h = location\n",
    "                msk[y:y+h, x:x+w] = 1\n",
    "        # if augment then horizontal flip half the time\n",
    "        if self.augment and random.random() > 0.5:\n",
    "            img = np.fliplr(img)\n",
    "            msk = np.fliplr(msk)\n",
    "        \n",
    "        # small shifts to images\n",
    "        if self.augment:\n",
    "            h_offset = np.random.randint(low=0, high=10)\n",
    "            v_offset = np.random.randint(low=0, high=10)\n",
    "            # crop the images\n",
    "            if random.random() > 0.5:\n",
    "                img = img[v_offset:,h_offset:]\n",
    "                msk = msk[v_offset:,h_offset:]\n",
    "            else:\n",
    "                img = img[:v_offset,:h_offset]\n",
    "                msk = msk[:v_offset,:h_offset]\n",
    "                \n",
    "        # resize both image and mask\n",
    "        img = resize(img, (self.image_size, self.image_size), mode='reflect')\n",
    "        msk = resize(msk, (self.image_size, self.image_size), mode='reflect') > 0.5\n",
    "        # add trailing channel dimension\n",
    "        img = np.expand_dims(img, -1)\n",
    "        msk = np.expand_dims(msk, -1)\n",
    "        return img, msk\n",
    "    \n",
    "    def __loadpredict__(self, filename):\n",
    "        # load dicom file as numpy array\n",
    "        img = pydicom.dcmread(os.path.join(self.folder, filename)).pixel_array\n",
    "        # resize image\n",
    "        img = resize(img, (self.image_size, self.image_size), mode='reflect')\n",
    "        # add trailing channel dimension\n",
    "        img = np.expand_dims(img, -1)\n",
    "        return img\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        # select batch\n",
    "        filenames = self.filenames[index*self.batch_size:(index+1)*self.batch_size]\n",
    "        # predict mode: return images and filenames\n",
    "        if self.predict:\n",
    "            # load files\n",
    "            imgs = [self.__loadpredict__(filename) for filename in filenames]\n",
    "            # create numpy batch\n",
    "            imgs = np.array(imgs)\n",
    "            return imgs, filenames\n",
    "        # train mode: return images and masks\n",
    "        else:\n",
    "            # load files\n",
    "            items = [self.__load__(filename) for filename in filenames]\n",
    "            # unzip images and masks\n",
    "            imgs, msks = zip(*items)\n",
    "            # create numpy batch\n",
    "            imgs = np.array(imgs)\n",
    "            msks = np.array(msks)\n",
    "            return imgs, msks\n",
    "        \n",
    "    def on_epoch_end(self):\n",
    "        if self.shuffle:\n",
    "            random.shuffle(self.filenames)\n",
    "        \n",
    "    def __len__(self):\n",
    "        if self.predict:\n",
    "            # return everything\n",
    "            return int(np.ceil(len(self.filenames) / self.batch_size))\n",
    "        else:\n",
    "            # return full batches only\n",
    "            return int(len(self.filenames) / self.batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3_GgDL7ncxT3"
   },
   "source": [
    "# Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bqobZQZQcxUE"
   },
   "outputs": [],
   "source": [
    "def create_downsample(channels, inputs):\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(inputs)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    x = keras.layers.Conv2D(channels, 1, padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.MaxPool2D(2)(x)\n",
    "#     x = keras.layers.Conv2D(channels, (3,3), strides=(2,2), padding='same', use_bias=False)(x)\n",
    "    return x\n",
    "\n",
    "def create_resblock(channels, inputs):\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(inputs)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    x_1 = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x_1)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    x_2 = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.add([x_2, inputs])\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.add([x, x_1])\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.add([x, x_2])\n",
    "    return x\n",
    "\n",
    "def create_network(input_size, channels, n_blocks=2, depth=4):\n",
    "    # input\n",
    "    inputs = keras.Input(shape=(input_size, input_size, 1))\n",
    "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(inputs)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    \n",
    "    x = keras.layers.Conv2D(channels, 3, strides=(2,2), padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    \n",
    "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    \n",
    "    x = keras.layers.Conv2D(channels*2, 1, padding='same', use_bias=False)(x)\n",
    "    \n",
    "    # residual blocks\n",
    "    for d in range(depth):\n",
    "        channels = channels * 2\n",
    "        x = create_downsample(channels, x)\n",
    "            \n",
    "        for b in range(n_blocks):\n",
    "            x = create_resblock(channels, x)\n",
    "        \n",
    "    # output\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    \n",
    "    x_2 = keras.layers.Conv2D(512, (3,3), padding='same', dilation_rate=(2,2), activation=None, name=\"dilated_conv_1\")(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x_2)\n",
    "    x = keras.layers.LeakyReLU(0.01)(x)\n",
    "    \n",
    "    x = keras.layers.Conv2D(384, (3,3), padding='same', dilation_rate=(2,2), activation=None, name=\"dilated_conv_2\")(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0.01)(x)\n",
    "    \n",
    "    # transpose convolution to upsize\n",
    "    x = keras.layers.Conv2DTranspose(256, (8,8), (4,4), padding=\"same\", activation=None)(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0.01)(x)\n",
    "    \n",
    "    x = keras.layers.Conv2D(256, 1, padding='same', activation=None, kernel_regularizer=keras.regularizers.l2(l=0.01), name=\"fc_1\")(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0.01)(x)\n",
    "    x = keras.layers.Dropout(0.25)(x)\n",
    "    \n",
    "#     x = keras.layers.Conv2D(1, 1, activation='sigmoid')(x)\n",
    "    x = keras.layers.UpSampling2D(2**(depth-4))(x)\n",
    "    \n",
    "    x = keras.layers.Conv2DTranspose(128, (4,4), (2,2), padding=\"same\", activation=None)(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0.01)(x)\n",
    "    x = keras.layers.Dropout(0.25)(x)\n",
    "    \n",
    "    x = keras.layers.Conv2D(1, 1, padding='same', activation=\"sigmoid\", name=\"fc_2\")(x)\n",
    "    outputs = keras.layers.UpSampling2D(2)(x)\n",
    "    \n",
    "    model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AvdSzGI4cxUL"
   },
   "source": [
    "# Train network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3451
    },
    "colab_type": "code",
    "id": "DCgIz8CwcxUO",
    "outputId": "ce38cdfb-1806-488d-838f-d180e198a099"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_7 (InputLayer)            (None, 512, 512, 1)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_142 (Conv2D)             (None, 512, 512, 24) 216         input_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_164 (BatchN (None, 512, 512, 24) 96          conv2d_142[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_164 (LeakyReLU)     (None, 512, 512, 24) 0           batch_normalization_164[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_143 (Conv2D)             (None, 256, 256, 24) 5184        leaky_re_lu_164[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_165 (BatchN (None, 256, 256, 24) 96          conv2d_143[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_165 (LeakyReLU)     (None, 256, 256, 24) 0           batch_normalization_165[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_144 (Conv2D)             (None, 256, 256, 24) 5184        leaky_re_lu_165[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_166 (BatchN (None, 256, 256, 24) 96          conv2d_144[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_166 (LeakyReLU)     (None, 256, 256, 24) 0           batch_normalization_166[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_145 (Conv2D)             (None, 256, 256, 48) 1152        leaky_re_lu_166[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_167 (BatchN (None, 256, 256, 48) 192         conv2d_145[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_167 (LeakyReLU)     (None, 256, 256, 48) 0           batch_normalization_167[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_146 (Conv2D)             (None, 256, 256, 48) 20736       leaky_re_lu_167[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_168 (BatchN (None, 256, 256, 48) 192         conv2d_146[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_168 (LeakyReLU)     (None, 256, 256, 48) 0           batch_normalization_168[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_147 (Conv2D)             (None, 256, 256, 48) 20736       leaky_re_lu_168[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_73 (Add)                    (None, 256, 256, 48) 0           conv2d_147[0][0]                 \n",
      "                                                                 conv2d_145[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_169 (BatchN (None, 256, 256, 48) 192         add_73[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_169 (LeakyReLU)     (None, 256, 256, 48) 0           batch_normalization_169[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_148 (Conv2D)             (None, 256, 256, 48) 20736       leaky_re_lu_169[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_74 (Add)                    (None, 256, 256, 48) 0           conv2d_148[0][0]                 \n",
      "                                                                 conv2d_146[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_170 (BatchN (None, 256, 256, 48) 192         add_74[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_170 (LeakyReLU)     (None, 256, 256, 48) 0           batch_normalization_170[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_149 (Conv2D)             (None, 256, 256, 48) 20736       leaky_re_lu_170[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_75 (Add)                    (None, 256, 256, 48) 0           conv2d_149[0][0]                 \n",
      "                                                                 conv2d_147[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_171 (BatchN (None, 256, 256, 48) 192         add_75[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_171 (LeakyReLU)     (None, 256, 256, 48) 0           batch_normalization_171[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_150 (Conv2D)             (None, 128, 128, 96) 41472       leaky_re_lu_171[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_172 (BatchN (None, 128, 128, 96) 384         conv2d_150[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_172 (LeakyReLU)     (None, 128, 128, 96) 0           batch_normalization_172[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_151 (Conv2D)             (None, 128, 128, 96) 82944       leaky_re_lu_172[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_173 (BatchN (None, 128, 128, 96) 384         conv2d_151[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_173 (LeakyReLU)     (None, 128, 128, 96) 0           batch_normalization_173[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_152 (Conv2D)             (None, 128, 128, 96) 82944       leaky_re_lu_173[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_76 (Add)                    (None, 128, 128, 96) 0           conv2d_152[0][0]                 \n",
      "                                                                 conv2d_150[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_174 (BatchN (None, 128, 128, 96) 384         add_76[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_174 (LeakyReLU)     (None, 128, 128, 96) 0           batch_normalization_174[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_153 (Conv2D)             (None, 128, 128, 96) 82944       leaky_re_lu_174[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_77 (Add)                    (None, 128, 128, 96) 0           conv2d_153[0][0]                 \n",
      "                                                                 conv2d_151[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_175 (BatchN (None, 128, 128, 96) 384         add_77[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_175 (LeakyReLU)     (None, 128, 128, 96) 0           batch_normalization_175[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_154 (Conv2D)             (None, 128, 128, 96) 82944       leaky_re_lu_175[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_78 (Add)                    (None, 128, 128, 96) 0           conv2d_154[0][0]                 \n",
      "                                                                 conv2d_152[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_176 (BatchN (None, 128, 128, 96) 384         add_78[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_176 (LeakyReLU)     (None, 128, 128, 96) 0           batch_normalization_176[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_155 (Conv2D)             (None, 64, 64, 192)  165888      leaky_re_lu_176[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_177 (BatchN (None, 64, 64, 192)  768         conv2d_155[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_177 (LeakyReLU)     (None, 64, 64, 192)  0           batch_normalization_177[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_156 (Conv2D)             (None, 64, 64, 192)  331776      leaky_re_lu_177[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_178 (BatchN (None, 64, 64, 192)  768         conv2d_156[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_178 (LeakyReLU)     (None, 64, 64, 192)  0           batch_normalization_178[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_157 (Conv2D)             (None, 64, 64, 192)  331776      leaky_re_lu_178[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_79 (Add)                    (None, 64, 64, 192)  0           conv2d_157[0][0]                 \n",
      "                                                                 conv2d_155[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_179 (BatchN (None, 64, 64, 192)  768         add_79[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_179 (LeakyReLU)     (None, 64, 64, 192)  0           batch_normalization_179[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_158 (Conv2D)             (None, 64, 64, 192)  331776      leaky_re_lu_179[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_80 (Add)                    (None, 64, 64, 192)  0           conv2d_158[0][0]                 \n",
      "                                                                 conv2d_156[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_180 (BatchN (None, 64, 64, 192)  768         add_80[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_180 (LeakyReLU)     (None, 64, 64, 192)  0           batch_normalization_180[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_159 (Conv2D)             (None, 64, 64, 192)  331776      leaky_re_lu_180[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_81 (Add)                    (None, 64, 64, 192)  0           conv2d_159[0][0]                 \n",
      "                                                                 conv2d_157[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_181 (BatchN (None, 64, 64, 192)  768         add_81[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_181 (LeakyReLU)     (None, 64, 64, 192)  0           batch_normalization_181[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_160 (Conv2D)             (None, 32, 32, 384)  663552      leaky_re_lu_181[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_182 (BatchN (None, 32, 32, 384)  1536        conv2d_160[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_182 (LeakyReLU)     (None, 32, 32, 384)  0           batch_normalization_182[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_161 (Conv2D)             (None, 32, 32, 384)  1327104     leaky_re_lu_182[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_183 (BatchN (None, 32, 32, 384)  1536        conv2d_161[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_183 (LeakyReLU)     (None, 32, 32, 384)  0           batch_normalization_183[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_162 (Conv2D)             (None, 32, 32, 384)  1327104     leaky_re_lu_183[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_82 (Add)                    (None, 32, 32, 384)  0           conv2d_162[0][0]                 \n",
      "                                                                 conv2d_160[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_184 (BatchN (None, 32, 32, 384)  1536        add_82[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_184 (LeakyReLU)     (None, 32, 32, 384)  0           batch_normalization_184[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_163 (Conv2D)             (None, 32, 32, 384)  1327104     leaky_re_lu_184[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_83 (Add)                    (None, 32, 32, 384)  0           conv2d_163[0][0]                 \n",
      "                                                                 conv2d_161[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_185 (BatchN (None, 32, 32, 384)  1536        add_83[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_185 (LeakyReLU)     (None, 32, 32, 384)  0           batch_normalization_185[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_164 (Conv2D)             (None, 32, 32, 384)  1327104     leaky_re_lu_185[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_84 (Add)                    (None, 32, 32, 384)  0           conv2d_164[0][0]                 \n",
      "                                                                 conv2d_162[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_186 (BatchN (None, 32, 32, 384)  1536        add_84[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_186 (LeakyReLU)     (None, 32, 32, 384)  0           batch_normalization_186[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dilated_conv_1 (Conv2D)         (None, 32, 32, 512)  1769984     leaky_re_lu_186[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_187 (BatchN (None, 32, 32, 512)  2048        dilated_conv_1[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_187 (LeakyReLU)     (None, 32, 32, 512)  0           batch_normalization_187[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dilated_conv_2 (Conv2D)         (None, 32, 32, 384)  1769856     leaky_re_lu_187[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_188 (BatchN (None, 32, 32, 384)  1536        dilated_conv_2[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_188 (LeakyReLU)     (None, 32, 32, 384)  0           batch_normalization_188[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_10 (Conv2DTran (None, 128, 128, 256 6291712     leaky_re_lu_188[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_189 (BatchN (None, 128, 128, 256 1024        conv2d_transpose_10[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_189 (LeakyReLU)     (None, 128, 128, 256 0           batch_normalization_189[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "fc_1 (Conv2D)                   (None, 128, 128, 256 65792       leaky_re_lu_189[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_190 (BatchN (None, 128, 128, 256 1024        fc_1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_190 (LeakyReLU)     (None, 128, 128, 256 0           batch_normalization_190[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 128, 128, 256 0           leaky_re_lu_190[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_7 (UpSampling2D)  (None, 128, 128, 256 0           dropout_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_11 (Conv2DTran (None, 256, 256, 128 524416      up_sampling2d_7[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_191 (BatchN (None, 256, 256, 128 512         conv2d_transpose_11[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_191 (LeakyReLU)     (None, 256, 256, 128 0           batch_normalization_191[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "fc_2 (Conv2D)                   (None, 256, 256, 1)  129         leaky_re_lu_191[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_8 (UpSampling2D)  (None, 512, 512, 1)  0           fc_2[0][0]                       \n",
      "==================================================================================================\n",
      "Total params: 18,375,609\n",
      "Trainable params: 18,365,193\n",
      "Non-trainable params: 10,416\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# define iou or jaccard loss function\n",
    "def iou_loss(y_true, y_pred):\n",
    "    y_true = tf.reshape(y_true, [-1])\n",
    "    y_pred = tf.reshape(y_pred, [-1])\n",
    "    intersection = tf.reduce_sum(y_true * y_pred)\n",
    "    score = (intersection + 1.) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) - intersection + 1.)\n",
    "    return 1 - score\n",
    "\n",
    "# combine bce loss and iou loss\n",
    "def iou_bce_loss(y_true, y_pred):\n",
    "    return 0.4 * keras.losses.binary_crossentropy(y_true, y_pred) + 0.6 * iou_loss(y_true, y_pred)\n",
    "\n",
    "# mean iou as a metric\n",
    "def mean_iou(y_true, y_pred):\n",
    "    y_pred = tf.round(y_pred)\n",
    "    intersect = tf.reduce_sum(y_true * y_pred, axis=[1, 2, 3])\n",
    "    union = tf.reduce_sum(y_true, axis=[1, 2, 3]) + tf.reduce_sum(y_pred, axis=[1, 2, 3])\n",
    "    smooth = tf.ones(tf.shape(intersect))\n",
    "    mean_iou = tf.reduce_mean((intersect + smooth) / (union - intersect + smooth))\n",
    "    return mean_iou\n",
    "\n",
    "def adj_mean_iou(y_true, y_pred):\n",
    "    y_pred = tf.round(y_pred)\n",
    "    y_true_pos = tf.reduce_max(y_true, axis=[1, 2, 3])\n",
    "    \n",
    "    intersect = tf.reduce_sum(y_true * y_pred, axis=[1, 2, 3])\n",
    "    union = tf.reduce_sum(y_true, axis=[1, 2, 3]) + tf.reduce_sum(y_pred, axis=[1, 2, 3])\n",
    "    smooth = tf.ones(tf.shape(intersect))\n",
    "    \n",
    "    return tf.reduce_mean((y_true_pos * (intersect + smooth)) / (union - intersect + smooth))\n",
    "\n",
    "# create network and compiler\n",
    "model = create_network(input_size=IMAGE_SIZE, channels=24, n_blocks=1, depth=4)\n",
    "model.compile(optimizer='adam',\n",
    "              loss=iou_bce_loss,\n",
    "              metrics=['accuracy', mean_iou])\n",
    "\n",
    "# cosine learning rate annealing\n",
    "def cosine_annealing(x):\n",
    "    lr = 0.002\n",
    "    epochs = 20\n",
    "    return lr*(np.cos(np.pi*x/epochs)+1.)/2\n",
    "learning_rate = tf.keras.callbacks.LearningRateScheduler(cosine_annealing)\n",
    "checkpoint = keras.callbacks.ModelCheckpoint(CHECKPOINT_PATH, monitor='val_loss', verbose=1, save_best_only=False, save_weights_only=True, mode='auto', period=1)\n",
    "\n",
    "# create train and validation generators\n",
    "folder = './stage_1_train_images'\n",
    "train_gen = generator(folder, train_filenames, pneumonia_locations, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=True, augment=True, predict=False)\n",
    "valid_gen = generator(folder, valid_filenames, pneumonia_locations, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=False, predict=False)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jSSSpmNE-L1L"
   },
   "outputs": [],
   "source": [
    "model.load_weights(CHECKPOINT_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 527
    },
    "colab_type": "code",
    "id": "fM5VeiuDcxUY",
    "outputId": "c3d66d6d-3c06-4aa6-f58d-46cfa42cdd9f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1925/1926 [============================>.] - ETA: 1s - loss: 0.7316 - acc: 0.9490 - mean_iou: 0.4906\n",
      "Epoch 00001: saving model to model8_448.h5\n",
      "1926/1926 [==============================] - 3080s 2s/step - loss: 0.7317 - acc: 0.9490 - mean_iou: 0.4906 - val_loss: 0.6705 - val_acc: 0.9575 - val_mean_iou: 0.5452\n",
      "Epoch 2/8\n",
      "1925/1926 [============================>.] - ETA: 1s - loss: 0.6641 - acc: 0.9572 - mean_iou: 0.5315\n",
      "Epoch 00002: saving model to model8_448.h5\n",
      "1926/1926 [==============================] - 3059s 2s/step - loss: 0.6641 - acc: 0.9572 - mean_iou: 0.5315 - val_loss: 0.6757 - val_acc: 0.9665 - val_mean_iou: 0.5719\n",
      "Epoch 3/8\n",
      "1925/1926 [============================>.] - ETA: 1s - loss: 0.6397 - acc: 0.9611 - mean_iou: 0.5760\n",
      "Epoch 00003: saving model to model8_448.h5\n",
      "1926/1926 [==============================] - 3071s 2s/step - loss: 0.6396 - acc: 0.9611 - mean_iou: 0.5760 - val_loss: 0.7242 - val_acc: 0.9629 - val_mean_iou: 0.6364\n",
      "Epoch 4/8\n",
      "1925/1926 [============================>.] - ETA: 1s - loss: 0.6313 - acc: 0.9628 - mean_iou: 0.5919\n",
      "Epoch 00004: saving model to model8_448.h5\n",
      "1926/1926 [==============================] - 3082s 2s/step - loss: 0.6313 - acc: 0.9628 - mean_iou: 0.5919 - val_loss: 0.6606 - val_acc: 0.9711 - val_mean_iou: 0.6640\n",
      "Epoch 5/8\n",
      "1925/1926 [============================>.] - ETA: 1s - loss: 0.6195 - acc: 0.9647 - mean_iou: 0.6066\n",
      "Epoch 00005: saving model to model8_448.h5\n",
      "1926/1926 [==============================] - 3083s 2s/step - loss: 0.6195 - acc: 0.9647 - mean_iou: 0.6066 - val_loss: 0.6340 - val_acc: 0.9688 - val_mean_iou: 0.6675\n",
      "Epoch 6/8\n",
      "1925/1926 [============================>.] - ETA: 1s - loss: 0.6122 - acc: 0.9654 - mean_iou: 0.6190\n",
      "Epoch 00006: saving model to model8_448.h5\n",
      "1926/1926 [==============================] - 3074s 2s/step - loss: 0.6122 - acc: 0.9654 - mean_iou: 0.6190 - val_loss: 0.6653 - val_acc: 0.9725 - val_mean_iou: 0.6703\n",
      "Epoch 7/8\n",
      "1925/1926 [============================>.] - ETA: 1s - loss: 0.6063 - acc: 0.9665 - mean_iou: 0.6314\n",
      "Epoch 00007: saving model to model8_448.h5\n",
      "1926/1926 [==============================] - 3084s 2s/step - loss: 0.6063 - acc: 0.9665 - mean_iou: 0.6314 - val_loss: 0.6324 - val_acc: 0.9599 - val_mean_iou: 0.5751\n",
      "Epoch 8/8\n",
      "1102/1926 [================>.............] - ETA: 20:55 - loss: 0.5963 - acc: 0.9673 - mean_iou: 0.6365"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=8, shuffle=True, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7tyG7P79Y42U"
   },
   "outputs": [],
   "source": [
    "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=15, shuffle=True, verbose=1, initial_epoch=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "y_bLpJPBcxUh",
    "outputId": "49c438f6-aabc-41b9-f98e-1bf8519da3cc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TGKjrcp+i2hK+PbuD7efTqDPVI0HC8JAEkmMnMiJkUId5+pdza/8JnN6TxZGq\nIzwYdfl+OcPV9KUn2Ww2/rj7A87X5HBL5CgeHzv3qtOpXSiu5cNvT7HjcL5jW4CfihHxRiKDdEQE\n6YgM1hFh1KH5yQjwyDIdf9j6Ov848RGvzfz/8FM65zaq2WrmP/s/RYKEX4ydT0hA9zNKxIVEQAY0\nUOfyfyfh5mCxWVjTGhT318e1BsU37joHjVzNxPBxTAwfR0FdEXuK9pNedJAf83fzY/5uQjXBjAlp\nmWrho/Dsv0ERGAtCD2qrLBTi5FRtRqORsrIyx/OSkhICAy+Olo4ePZq1a9cCsHLlSsLCLqYJ3Lt3\nLy+88ALQMtJcVVXleK24uBij0djl+1dWNnS5T2Cg7oqLZa02KxnlJ9iRl8bJyjMAaOUaZkRNYXzo\nGALaRgDLuy400cc7DrVMxbZzaUwLnoKXtOuA/Vp11pfeYLfbWXf6M/bnH6G/Po77+txNeVnXv5uS\nqka+2HmetMwi7HaICtYxf9ZAgn29UXlf+vHfUNdEQ11Th20GjNwZexufZm1k5Y/v8eywx52S33jT\n+e/IryliYlgSentgt3+/gYE6pE0thUDyKotd9u8kAvIbg91up8HSSHVzDTWm2os/TTXUNNc6Hlc3\n19BsNdFfH8dTQx65oYPinwrVBjMn7nbu6nMbxytOsafwABllx/k0ayOfn91Mgn9/xgYnMihgADIP\nnGrheS0WBA9S4Bgxdm4p6PHjx7Nq1SpSUlLIzMzEaDSi1V6sqvf444+zYsUKVCoV27Zt49FHHwVa\nAl+NRoNC0fIhLpfL6dOnD/v37ycxMZFvv/2Whx56yKltba+quZpd+XvZVZBOtakGgFjfGCaGjWWo\ncXC35qvJveSMCh7BD3m7OFZ+kqGBCV0f5GG+ydnGjvw0wrQhPDH4oS4vNhU1TXy5O5udRwux2uyE\nB2q4e0IfhvUNwGj0uebgcVrERLKqzpFRdoLN2d8zOyb5erpDUX0x32Rvxc/blztjb7uucwGoZEo0\ncjXlTWIqhXB5NruNWlNdx2C3uZYaUw3Vplpqmlt/mmqx2Cydnksr1xCg8ifaJ4J7+955UwXF7XlJ\nvRgcMJDBAQOpM9ezv/gwewv3k1F2goyyE2jkakYFDWds61QLTyECY0HoQW1TKYKdHBiPGDGChIQE\nUlJSkEgkLFmyhA0bNqDT6UhOTmbu3LksWLAAiUTCwoULHQvxSktLHY/bpKam8uKLL2Kz2Rg6dCjj\nxl15Tnx32Ow2TlVksaNgDxllx7HZbSi9lEwKH8ctoWOdkmZsXMgofsjbRVph+g0XGKcV7ufLc1+j\n9/bjmaELUMmuvIizuq6ZjWnRZTpRAAAgAElEQVQ5bD+cj8VqJ8ig5u4JMSTGG6+rGpxEIuGhAfez\nLP3PbD6/hVjfaOINl8/E05WWss8bsNitzO13FyqZc/Kw+ysNFNQXYbPber1in+A+ShrKOFB8hIbz\ntRTXVDgC3lpTHfZOMmpLJVJ8FDrCNCH4eOvwVejw8fbBV6HD19sHn9afOrm2R+5KeTqtXMPk8PFM\nDh9Pfl0hewr3s6/oENvzdrE9bxdh2hDGhiQy27dnF3abLTZKqxoJDdB0+xwiMBaEHlRYV0SA0oB3\nD4woPP/88x2ex8fHOx7PmDGDGTNmXHLMoEGDWL16dYdtcXFxjmkXzlTbXMeW3B/Ymb+H0sZyACK0\noUwIS2Jk0DCUMm+nvVe4LpRIXTjHyk5S1Vx9w1Ruyiw/xdqT61HLVDw77LEr9quu0czmPTl8fyAP\nk8VGgK+Su26JYWxCEF5dzNG+Whq5mscGPcjrB9/m75kf8rvRv+5W2qa0gn2crT7P0MBBDA0c5JS2\nQcsCvNzaPGpMtTfMv79wdWx2G8fLT/FD3m6OV5zq8JpcKsdXoSPGN6pDsPvToFcjV4svVE4Spg3h\nnr538LPYWWSWn2RPUctUi0/OfElW7VkWDnzEqe9ntlg5dr6C/SdLOJxVRmOzlcUPjqBfRPfS2F5V\nYNxZIYHCwkJ+85vfYDabGThwIC+//LLjtaamJm6//XaeeeaZDqmiBOFmUGuqo9ZcR7TvQFc3pddt\nz9vFZ1kbMdssyKUyxgYnMiF8bI8miR8XOoqPTuWxp/AAtzqhlLGr5dbksfrYv5BKpDw55JHL3nVo\naLLw7b5cvt13gSaTFb3Om5Rx0dwyJASZl/Mv8jG+UdwdO4tPsr7ib5lr+eXwhdcUTFQ31/Dp2Y0o\nvZTM7XeXU9sWoGy5E1LWWOFRgfG1Xl/r6+v57W9/S3V1NWazmV/84hdMmDDBhT1wnXpzA2mF+9iR\nl0ZZ6zSaPr5RTAhLYlhUf6z1UpReyhsmW4Kn8ZJ6MSQwgSGBCdSa6lix7y9kV15wyrnbguF9J0s4\nfKaMJpMVAH8fbyYPDyM6uPtz/rsMjDsrJACwfPlyFixYQHJyMi+99BIFBQWEhoYC8Pbbb+Pr6zkf\nUILgTIU9VPHOE9SZ6gn1CSYxYBhjQhJ7pehCYtAwPjnzFWmF+5gRNdmjR3/KGsv565E1mK1mHh80\njzi/mA6vN5ksfH8gj6/35lLfZMFHLefuCX2YPDwU+VVkC7keUyImkFV1niNlmWw6/x2395l51cd+\nfOYLGi1N3N/vbqcHr/6qlqwW5Y0Vl/y+3FV3rq9bt24lJiaGRYsWUVxczMMPP8zXX3/twl70vgu1\n+fyQt5v9xYccX77HhYxiYvg4x1zWQB8dpc2uWzArdKRTaAnRBnG8/BQN5sZu5fU3W6wcO1fBvlM/\nDYaVTB4WRmK8kZgQ3XV/EeoyMO6skIDNZuPAgQO8/vrrACxZssRx3NmzZ8nKymLy5MnX1UBB8FQF\ndT2z8M4T3N5nBo8G3tOrGQJUMhUjjEPYW3SArKpzTs+521vqTPW8dfh9as113NfvLoYZL1bLNJmt\nbD+Uz8Y9OdQ2mNEoZdw7OZZpI8LxVvTOvEeJRMK8AfeRt6+Ar7O3EusbwwD/rvN0Z5Qd51DJUfr4\nRnFL2Bint8u/dcTYkxbgdef6qtfrOXWqZbpATU0Nen3309x5EovNwqGSDH7M38256pY85wFKAxPC\nk0gKGSUqHnqAIHUgx8tPUdJYSrQ88qqOMVusZJy7OE3ip8HwqAFGooOvPxhur8vAuLNCAhUVFWg0\nGpYtW0ZmZiaJiYksWrQIgBUrVvD73/+ezz77zGmNFQRP0lbC2Nmp2oQrGxc6mr1FB9hdsM8jA2OT\n1cTbR/9GSWMZyZGTmRw+HgCL1caOIwV8uTubqjoTSoUXd90SQ3JiBGpl7y8VUcvVPDZoHisP/JW/\nH2+Zb9zZCHCTpYmPTn2Kl8SLB/rf0yOj+ReLfFQ6/dw9pTvX19mzZ7NhwwaSk5Opqanh3XffdWEP\nel5lUxU7C/ayq2AvtaY6AAb692dS2DgG+vf36DtDN5u2AkzF9aVE+1w5MDaZW4PhUy3BcHNrMBzg\nq2Ty8DBGxTs/GG7vmj9R2xcAsNvtFBcXM3/+fMLCwli4cCHbt2+nqqqKYcOGERERcdXndWbBAE8h\n+uKenNWXsqNlSCVSEiJjkN9A1ZDcWaxvNEZ1AIdKM5hrvgu1B40iWW1W1mR+QHZNLqOCRnBn7K1Y\nbTZ2Hyvii53ZlNc0oZBLmTU2ilvHRF53KebrFeUTwd1xs1l/5ouW+cbDFl5xtf6X576hqrma26Kn\nOSULyeUYlHokSDxqxPinrub6Wl1dTWhoKO+//z4nT54kNTWVDRs2dHpeT7u+2u12jpee4ZszP5Ce\nfxib3YZGruL2ftOYETeRYF3XudbBffrjDDdCX/rZIuEU1EmqL+lPs9nKwZPF7DxcwL4TRTQ2twTD\nQQY1twwNZfzQUOLC/XplvniXgXFnhQT0ej2hoaFERrZE/klJSZw5c4bMzEwuXLjA9u3bKSoqQqFQ\nEBwc3GkaqOstGOBpRF/ck7P6Yrfbya3Kx6gKoKqiCWjq8pjLtUW4NhKJhHEho/ns7CbSiw85Rlw9\nwX/OfE5G2Qni9X35efw9pJ8o4fMd5ymubETmJSU5MYJZSVH4atwnZ+rk8PFkVZ3ncGkGG89/x52x\nt16yz/nqXH7I202QOpCZUT23KFIuleHr7UOZB5WF7s71NS8vj1tuaSkFHh8fT0lJCVarFS+vKwe+\nnnJ9bbI0O6qptaW6DNOGMCl8HKOChrfkC26C0qau2+kO/XGWG6Uv3uaWXPvnywooLa1tHRkuZ9/J\nEo6cLe84Mtw6TSIq6OLIcFlZndPa0tn1tcvAuLNCAjKZjIiICLKzs4mOjiYzM5PZs2fzxBNPOI5f\ntWoVYWFhTs+NKgjurKq5mkZLE/GGrudeCs41JmQkX5z7mt0F6UwKG+cRK9Kzqs6zM38PYZoQEpW3\n8crfD5JfWo+XVMLk4WHcnhSFwcc5+X6dqWW+8b3k1ebzTc5WYv1iSPDv73jdarOy9uR67Nh5oP+c\nHr9z4q80cK46G4vN4hEVt7pzffXy8uLIkSPMnDmT/Px8NBpNp0GxJyiuL+HH/DT2FB6gydqEVCJl\npHEok8LH08c3yiP+hoWu+Sp88PZSkF1RyDufH+NIVjnN5pZgONBPSeKIlmkS7YNhV+jyk6OrQgKp\nqaksXrwYu91Ov379mDrV89MkCcL16qmKd0LXfBQ6BgcM5EjpMS7U5hPpE+7qJl2R2WKjuq6Zj09s\nAqDhbDzv5Z5GIoHxg4O5c3wMgX7Xvnq7N6lkqtb5xm/xj+Mf8rtRv0avbMkf+n3ujxTUFzEuZDR9\n9bE93pYAlYGz1eepaKrCqA7o8fe7Xt25vjY2NpKamsq8efOwWCz84Q9/cHU3usVmt3Gs7AQ/5O12\nlIX3VeiYFjmB8aFjupUjW7h2tQ0mtuzPI+NcOXY70BqPtoWlLfGpxPHYEa469pN0OObi65KfnAPs\ndmjyVdLkXU7BiWKMfmoS442MijcSGaR1my9AV/WVurNCAlFRUXz44YdXPPa5557rZtMEwXO1pWoL\nuQlTtbmDcSGjOFJ6jN2F+1wSGDebrVTXNVNVZ6KqrpnqOhNV9c1U1Zqorm99XtdMfZMFqa4c7wE5\nWKsCKMj1ZvQAI3fdEkOIf/crN/W2SJ9w5vS9g/+c/ow1mWv59fAnKW+qZFP2d+gUWu6Om9Ur7fBX\ntqZsa6rwiMAYrv36qtFoeOONN3qlbT2hzlTP7oJ0dhTsoaKpZaFknF8Mk8LHMzQgQVSV6yUVNU18\nnZ7Lj0cKMJltyLwkeHlJwc7FCoF2HLUCL05/t7d7fHG7vd3OV64vCD5aH8xeNfzXg/0ZFB7mNsFw\ne+5/r0kQPFBBXVsOYzFi7AoDDP3w8/ZlX9Eh5sTNbpmb6ASNzRaq601U1zVT2RrwtgW5VXXNVNe3\nPG5bOHIlGqUMX603EUFaygIOUg9MCZ3K+AnxhAdqndLW3jYxLIkzVec4VHKUL899Q05tHmabhYf6\n9t4iyIuZKTxnnvHNIr+ukK0XdrC/+DAWmwWFVM4toWOYGD6OMG2Iq5t30yiqaGDTnhzSjhVhtdkx\n+Hgzc1IkE4eG4i13/peStgWl9tb/+aHUi/WZecg0DW4ZFIMIjAWhRxTWFyOTyghQ+bu6KTclL6kX\nY0MS+Tr7ew6VZDAmZOQ1HW+22MgtriUrv5qs/GqKKhooq25yLA65Eq1Kjr+PEl+tN34aBX46b3w1\nCvy03vhpvfHVKvDVKFC0XoAyy0/x1yMlDA4YyP1DErvdX3cgkUh4ML5lvvF3udsBGOQfzwjjkM4P\ndKKLuYw9J2Xbjcxut3Oy4gzfX/iRExWnATCqApgYPo4xwSO7VeRB6J6colo27snhwMkS7ECwQc1t\nYyNJSgjukSqZbdqC37YZGaGtGUWKG0oZ4KZrcERgLAhOZrPbKKwvJlhtFLcFXSipNTDeXZjeZWBc\nXW/ibGsQnJVfTXZhLRarzfG6Tq3A6KfCV9sW5Lb89NVcfOyjUSCXXf0Fxm6389W5bwC4PWZG9zrp\nZlQyJY8NmscfD7yFVCLl/v539+qoUIAYMXYLZpuF/cWH2do6xxygr18fpkVOJME/XuQe7iV2u53T\nF6rYmJbDsfMtfxNRwTpmj41iRL9ApNLeH7EN1bXcRS1pKOtiT9cRgbEgOFl5YyVmm1nML3axAJU/\n/fVxnKrMorih1JFc3mazk1da1yEQLq26mE5PKpEQYdQSF+ZLbLgPcWG+xMcGOjVVELRUgsutzWN4\n4GDCdaFOPbcrRejCeH7kL5BKpBiUvVuVzdfbB5nEizIPzmXsyerNDezI38OPebuoNtUilUhJDBrG\n1IgJRPlcfV0D4frY7XaOnC1nU1oOWfnVAMRH+jErKYqEaINLpzCEOALjUpe1oSsiMBYEJ2sbIQnV\nivnFrjYudDSnKrP48viPBDQOJyu/mrMFNR2mRGiUMobE+hMb5ktcmC8xITqUio4fjc6+kNjsNr46\n/y0SJMyKSXbqud1BhC7MJe/bFoyLEePeVdpQztYLO9hTuA+TzYzSy5tpEROZHDG+178c3cysNhv7\nTpawKS2XvNKWL/LD4gKYlRRFXNiVK1P2JpVcia9CR7EIjAXh5nExI4UIjHub3W6nuLKRrLzW0eCC\nBuwRcg6WHaLpiC/YpYT4qx1BcFyYL8H+aqS9PIJyuPQY+XWFJAYN67FKcDcrf5WBkorTNFmaUcq8\nXd2cG9q56my25P7I0dJM7NjRe/sxO2I840NHo5KJ+cO9xWyxsetYIZv35FBa1YREAmMTgpg1Jopw\no/st5jWqA8mqOo/JakbhhlVhRWAsCE5W6MhhLAKentZstpJdWNMyEpzf8rOu0ex4XSGX4meMoVZz\nmrtmaZgWl+jyUso2u42N57+7YUeLXa19yjaR7cD5rDYrR8oy2Zr7I+drcgGI1IUxLWIiw41DxLqK\nXtTYbOGHwwV8sy+X6joTMi8pk4eHceuYSIxunP88SB3ImapzlDaWueXfqAiMBcHJCuqK8PZSOIoc\nCNfOZrdT12CmsrYlDVplXTNVbY9rTY7t7YNgaCklmhBjcIwGhxs1FDX0Z2n6aQpsJ9GqklzUo4sO\nFB+hqL6YscGJjnnPgvO0T9nmjhddT9VkaSatcB/bLuykvHUO9+CAAUyLmEicXx+3Tb11I6prNLNl\n/wW+P5BHfZMFb4UXt42JJHlUBH5a979LYmz93CtuKHXLv1ERGAuCE1ltVoobSonQhYmV11fQZLJQ\nVdca3DqC3Z8GwCastiuniVcqvPDTehNh1F5cKBfmi1536UUhTBtClC6CzPKTVDVX4+fturl2VpuV\nTee/QyqRclvMdJe140YmUrY5V1VzNdsv7GJnwV4aLY3IpTJuCR3D1IgJBGmMrm7eTaWipolv0i/w\nw5F8TGYbWpWcuyfEMHVkOBql+01JuJK2AQF3XYAnAmNBcKKSxjKsdutNX9ijvLqJvadKySuqobL2\nYuDbVfELL6kEX62CqGAdeq03frqWdGh6nXe7596ovK/to2tc6ChyTl1gT+F+bo2edr3d67b04kOU\nNJYxPnSMI7WY4FwiZZtz5NUW8P2FH9lffBib3YZWrmF2TDITwpLQKdxv3uqNrKC0jn9vOsHu1qIc\nep0390xsLcqh8LypK0ZHYOyeKdtEYCwITtRW8e5mX3j30dYzHDjVcTRAo5Th76NsyQPcLtDVa73R\ntwbAOo2iRxbCjQwaxidnviStYB8zoqa4ZDTfarOy+fwWZBIvbnNhcH6ja5tKIVK2XTu73c7xilN8\nn/sjpyqzAAhWG5kaOYHRQSOQu+FCKXdlt9uxWG00m22YzFbMFhvNZismiw2z2Uqz5eJ2k9lKs9mG\n2dLyuslsw2SxYjJbqWkwcyK7ApsdggxqZvVCUY6e5q/U4yXxctvMFCIwFgQncmSkuMkzDdw/NY5p\no6OQ2mwto7ztqr25gkqmZIRxKHuK9nOm8hz9DXG93oa0wn2UN1UwKXycmH/egzQyNUovbzFifA1s\ndhtbz+3ms+PfUtS6eLifPo5pERMY6N9fTAv7iZp6E9sO5XMmr6oliG0NeFuC2ZafZrONK08Guzax\n4b7MTIxwWVEOZ/OSehGg8qe4oRS73e5289NFYCwITiQyUrQI8FUxIM5IaWmtq5vikBQ6ij1F+9ld\nmN7rgbHZZuHr7K3IpTJmRk3t1fe+2UgkEvxVBsoay93youuODpVksCbzA6QSKaOCRjAtcoLLclG7\ns4Kyer7dl8vuY8WOyphSiQSFXIpC7oVCJnVUwPSWtWyTy6R4y71QyKXIZS0/vWVeyOVSFK3Pu/oZ\nE2lweoEhVwtSB1LcUEKdud7tpuaIwFgQnKigvgiNTI2PQufqpgg/EesbTZA6kMOlx6g3N6CRq3vt\nvXcV7KWyuYqpERPw9fbptfe9WfkrDeTXFbrlRdcdJfj3Z2Hiz4lURIu7GT9ht9s5kVPJN+kXyDhX\nDoBRr2LGqAiSEoKveb1Dd9yIX+6M6gCgJTOFu/2NisBYEJzEZDVT2lBOrF/0DflB5ukkEgnjQkfz\nadZG9hUdYnLE+F55X5PVzLfZW1F4KZgRNaVX3vNm56+6mMvY3S667kgpUzI9doJb3eFxNYvVRvqJ\nYr5Nv0BuSctobd9wX2aOjmRYXMANMaXBldpnpojzi3FxazoSgbEgOElxQwl27Df9NAp3NiZ4JJ+f\n3czuwnQmhY/rlS8wO/LTqDbVMiNqigjSeokjZVtjBdE+kS5ujeBJ6pvMbD+Uz/cH8qiqMyGVSBg9\nwMiMUZH0CRV3e5zFnTNTiMBYEJyktzNSLF26lCNHjiCRSEhNTWXIkCGO17Zs2cLbb7+NQqFg9uzZ\nzJs3D4AvvviC1atXI5PJ+OUvf8nkyZPZt28fr7/+OjKZDLVazWuvvYavr+ty/fYknULLkICBHC49\nRm5tHlE+ET36fk2WZr7N2YbSy5tpkRN79L2Eiy6mbBO5jIWrU1LVyHf7LrDzaCHNZitKhRczRkUw\nfWQ4AW5cRc5TBbUr8uFuRGAsCE7StvAupBdGjNPT08nJyWHdunWcPXuW1NRU1q1bB4DNZuOVV17h\n008/xc/PjyeeeILp06fj7e3NW2+9xSeffEJDQwOrVq1i8uTJLFu2jD/+8Y/06dOHd955h3Xr1rFw\n4cIe74OrjAsdzeHSY+wu3NfjgfGPebupM9dzW/R0tHJNj76XcFHbiLFI2SZ0JSu/mm/Sczl4uhS7\nHfQ6b+66JYaJQ0NRK0WI1FO0cg0qmUoExoJwI7uYqq3nR4zT0tKYPr2lclpsbCzV1dXU1dWh1Wqp\nrKzEx8cHg6ElOBg7diy7d+9GqVSSlJSEVqtFq9XyyiuvAKDX66mqqgKgurqaPn369Hj7XWmAoR9+\n3r7sLzrMnLjb8fZS9Mj7NFqa2JL7AyqZiqkRE3rkPYTL8/egIh+d3fkpLCzkN7/5DWazmYEDB/Ly\nyy8Dl7/zI1w9m83OwdOlfJOey9mCGgCignTMHB1BYrzRo3MEewqJREKQOpALtflYbVa8pO5TqEQE\nxoLgJAX1xfgqdL0yMlhWVkZCQoLjucFgoLS0FK1Wi8FgoL6+nuzsbMLCwti7dy+jR48GoKmpiaee\neoqamhqee+45kpKSSE1NZd68efj4+ODr68uiRYu6fH+9Xo1M1vUHWWCge2bnmBY7nk+ObyKr8TST\nY5Ku6phr7cv6zB+ptzSQMvhOokLdq3Suu/67dNfl+uPrraPKXOXWfe3szg/A8uXLWbBgAcnJybz0\n0ksUFBSgUqkue+dH6Fpjs4WdGYV8t+8CZdVNAAyLC2Dm6Aj6RfiJRdO9LEgdSHZNLuVNlY4sFe5A\nBMaC4ASNliYqmiqJ1/d1yfvb7RdTyUskEpYvX05qaio6nY7w8HDHa1VVVbz55psUFBQwf/58tm3b\nxiuvvMKbb77JyJEjWbFiBWvXrmX+/Pmdvl9lZUOXbQoM1LntKvchvkP4hE18c2oHCdpBXe5/rX1p\nMDfw5cktaORqRukT3er34M7/Lt1xpf7ovfVcqM2nuKS6VwpUdCcA7+zOj81m48CBA7z++usALFmy\nBIBNmzZd9s6PcGUVNU18fyCP7YcLaGy2IJdJmTwslORREYT4iylOrtIWDJc0lIrAWBBuNG3Vonpj\nGgWA0WikrOziat6SkhICAwMdz0ePHs3atWsBWLlyJWFhYTQ1NTF8+HBkMhmRkZFoNBoqKio4deoU\nI0eOBGDcuHF8+eWXvdIHVwpQGYjX9+Vk5RmK60sI0jh3RPf73B9ptDTxs9hZKGVKp55buDr+Sj3Z\nNblUNVdjUOpd3ZzL6uzOT0VFBRqNhmXLlpGZmUliYiKLFi0iLy/vsnd+rkdxRQPvfHGc2vpmJBJw\njJtKJC2PWzdIkCBxPG7bRdK2K7Tb1vG4i+eSeUlReXuh8pahVHih9pah9JahUshQeXuhbPvpLUPt\nLbuuaQ25xbV8k55L+okSrDY7Pmo5MyfEMHl4GD7qnplCJVw9Y7uUbTDAtY1pRwTGguAEBa3zi3sr\nVdv48eNZtWoVKSkpZGZmYjQa0WovpgJ7/PHHWbFiBSqVim3btvHoo49iNptZvHgxTzzxBNXV1TQ0\nNKDX6wkICCArK4u4uDgyMjKIiorqlT642rjQUZysPENa4X5+FjfLaeetM9WzLW8nOoWWSeHjnHZe\n4dq0n2fsroHxT7W/82O32ykuLmb+/PmEhYWxcOFCtm/fDlz+zk9n0wC6mvpUUNXEodMlmC02p/XF\nWeQyKWqlDLW3HJVS5nisbnusbH3sLUPV+thssfHn9Uc5mtUyeBARpONnk2KZPCLcpaXpr4c7Twm6\nVm19GSCPhmNQba92q/6JwFgQnKA3M1IAjBgxgoSEBFJSUpBIJCxZsoQNGzag0+lITk5m7ty5LFiw\nAIlEwsKFCx0L8WbOnMncuXMBeOGFF5BKpbz00ku88MILyOVyfH19Wbp0aa/0wdWGBA5CI1Ozp3A/\nd/SZ6bTFH9/lbqfZauKOPrei6KGFfULXAhyZKSpxzQSnrnV250ev1xMaGkpkZEse5qSkJM6cOYO/\nv/9l7/z4+/tf8X26mvoU6qfk42W3U1JS49jWEqPbaYvVW59ib7et/X5t/+vY327/yXEtL5qtNpqa\nrTSaLDQ2W2kyWWhsbnnc2Gyh0WRxvN7UbKHR1Lq92UxFbRPNJmunfWlvQJSemaMjGdzHgEQiobqq\n6ylg7uhGmv7Uvi9eVhUSJOSU5/d6/zoLxEVgLAhOUFjXFhj33iKr559/vsPz+Ph4x+MZM2YwY8aM\nS45JSUkhJSWlw7YRI0bw0Ucf9Uwj3ZhcKmN08Ai25e0ko/wEwwK7nmvclermWn7I242fty+3hI5x\nQiuF7vKEzBSd3fmRyWRERESQnZ1NdHQ0mZmZzJ49m8GDB1/2zs/18pJKPCIbg81mp8nULqB2BM4W\nmlofmy02JiVGolO4f39uZgovOXqln9sV+RCBsSA4QUF9Ef5KvZhP6mGSQkexLW8naQXpTgmMv8vZ\nhtlmZmbUVORecie0UOguR/U7N85l3NWdn9TUVBYvXozdbqdfv35MnToVqVR62Ts/NwupVOKYRtGZ\nG2mU9UYWpA7kRMVpmixNbnP9FIGxIFynOlM9NaZaBvm7z+IB4eqEaUOI8okgs/wUVc3V+Hl3v+Jf\nZVMVOwr2YFDqGRc6yomtFLrDoPRDgsStR4yh8zs/UVFRfPjhh5ccc7k7P4LgiYzqAE5UnKakoYxI\nn/CuD+gFN8/XTEHoIY7CHr1UClpwrvEho7FjZ0/h/us6zzc527DYLNwWPQ2ZVIw5uJqX1Au90o/y\nJlEWWhDcldENS0OLwFgQrlNB68K7UG3vLLwTnGtE0FAUUjm7C/Zhs3dvVX55YyW7C9IJUPkzJnik\nk1sodJe/Uk91cw1mq9nVTREE4TKCOqRscw8iMBaE69TbGSkE51LJlIwIGkp5UwWnK8926xxfZ2/B\narcyK3q6W5U2vdn5qwzYsVMhRo0FwS0ZVWLEWBBuOAV1RUiQEKwO7HpnwS2ND20pmZ1WuO+ajy1p\nKGNP0QGC1EZGBQ93dtOE69A+ZZsgCO5Hr/RFLpWLEWNBuFHY7XYK64swqgNEFgIPFuMTRZDayOGS\nDOrM9dd07ObsLdjsNmbHTO+V0sPC1fOElG2CcDOTSqQY1QEUN5Z1KHDjSlf1Kb506VLuv/9+UlJS\nOHr0aIfXCgsLeeCBB7j33nt58cUXHdtfe+017r//fu655x6+/fZb57ZaENxEtamGBkujWHjn4SQS\nCeNCR2GxW9lXdOiqjyuqL2Ff0SFCNcEMNw7pwRYK3eEJKdsE4WZnVAVgspqoNtV0vXMv6DIwTk9P\nJycnh3Xr1vHqq6/y6st/2roAACAASURBVKuvdnh9+fLlLFiwgPXr1+Pl5UVBQQF79uzhzJkzrFu3\njtWrV980lbSEm8/Fwh5ifrGnGxM8Ei+JF7sL0q965GLT+e+wY2d2nxlitNgN+ataCl+IEWNBcF9t\nC/CK691jOkWXn+RpaWlMnz4dgNjYWKqrq6mrqwPAZrNx4MABpk6dCsCSJUsIDQ1l1KhRvPHGGwD4\n+PjQ2NiI1Xr1ZRwFwVMUtKZqExkpPJ9OoWVwwEAK6ovIrc3rcv/8ukIOlBwhQhfG0ICEXmihcK18\nFDrkUpkYMRYEN9aWsq2k0UMC47Kysg7lJg0GA6WlLY2vqKhAo9GwbNkyHnjgAVauXAmAl5cXarUa\ngPXr1zNx4kS8vMRKbeHG05aRIlRMpbghjGtdhLe7IL3LfTee/w6A22NmIJFIerRdQvdIJVIMSgPl\njWLxnSC4K3fLZXzNWejb32K02+0UFxczf/58wsLCWLhwIdu3b2fy5MkAbNmyhfXr17NmzZouz6vX\nq5HJug6eAwN119pktyX64p6upS+lh0uRSWUMiIxBJtJ0ebwBhr7ovf3YX3yYOX3vwNtLcdn9cmvz\nOFJ6jGifSBL84y+7j+Ae/FV6ihtKaLQ0opKpXN0cQRB+IsjTAmOj0UhZWZnjeUlJCYGBLZ3Q6/WE\nhoYSGRkJQFJSEmfOnGHy5Mns2LGDd955h9WrV6PTdR1oVFY2dLnPjVT7XPTFPV1LX2x2G7nVBQSp\nA6ks7/r/v91pi9C7pBIpY0MS2Zy9hYMlR0kKSbzsfhvPtSwovr2PGC12d46UbY2VROhEYCwI7kYt\nV6GTaylpKOt6517Q5VSK8ePH88033wCQmZmJ0WhEq9UCIJPJiIiIIDs72/F6TEwMtbW1vPbaa7z7\n7rv4+fn1XOsFwYUqmqowWU0iI8UNJikkEQkS0q4wneJ8dQ7Hyk8S6xtDvL5vL7dOuFaOlG1inrEg\nuK3/n707j6uqzh8//roLcFfgXriXTRDEnTLFJcklU9EmqzErwcZlxsqpsfw2k1PGzxnnm3tlU9le\nVjNTGmVOX6dpWtyaFve1cEOURUD2fRO49/cHcoVUQATuBd7Px8NH3HvuOef9uXG47/O5n8/7Y9VZ\nyKvIp9pW4+xQmu8xjoyMJCIigtjYWBQKBUuWLGHTpk0YjUaio6OJi4tj0aJF2O12+vbty/jx4/n4\n448pKCjgsccecxxn9erVBAYGtmtjhOhImRcm3klFiq7FR2umn6k3xwsSOVeWjb/e2mj7Zxd6i++Q\n3uJOwVGyTSpTCOGy/HS+JBWdIbciz+mdTS0aY7xw4cJGj/v3vzimrmfPnmzYsKHR9piYGGJiYtog\nPCFcV32pNpl41/XcFDiC4wWJ7Mzcy129pzieP1V4huMFifQz9aaPKdyJEYqWcpRskx5jIVyWozJF\neY7TE2MpvClEK0mptq5rkCUCvZuO3Zn7qWnw1d5np+uGld3ea7KzQhNXyVfjA0iPsRCuzJUm4Eli\nLEQrZZSdw13phlljav7FolNxU6oZ4R9JSXUpP+UeA+BE/ikSC08z0Kcfvbx6OjlC0VI6Ny1atZbc\nSinZJoSrcqWSbZIYC9EKtbZassqy8df7yYpnXdRNARdqGmfuxW6389mZC73FYZOcGZZoBV+NifyK\n/BavaCiE6Fi+WjNKhdIlKlPIJ7oQrZBTkUeNvZZAmXjXZQUa/An1DOFo3gm2nf6e00UpDPKNoKdn\nsLNDE1fJR2vmvK2akupSZ4cihLgMtVKNr8ZMtvQYC9E51Y8vDjDIxLuu7KbA4dix89b+ugnGt/eS\n3uLOyMdRy9j1xhmvWLGCmJgYYmNjOXLkSKNtmZmZzJgxg3vuuYc///nPjbZVVlYyceJENm3a1JHh\nCtFurDpfSqvLKKtu+3UBroYkxkK0wsWloKXHuCsbar0Bd5U7NruNIdZBBBkCnB2SaAVHLWMXS4z3\n7NlDSkoK8fHxLF++nOXLlzfavmrVKubOncvGjRtRqVRkZGQ4tr322mt4eXl1dMhCtBtXGWcsibEQ\nrZBZKhUpugONWsNI/2G4KdVMCYt2djiilXw0rlmybefOnUycOBGA8PBwioqKKC2tG+5hs9nYv38/\n48ePB2DJkiWOtQCSkpI4deoU48aNc0rcQrQHvwYl25xJEmMhWiGjLAutWouXu6ezQxHt7O4+t/PK\nHcudXltTtJ6vi/YY5+bmYjJdrGpjNpvJyalLCvLz89Hr9axcuZIZM2awZs0ax+tWr17NokWLOjxe\nIdqTq/QYt2iBDyHERdW11eRU5BLqGeLUlc9WrFjB4cOHUSgUxMXFMWjQIMe2LVu28Nprr+Hu7s6U\nKVOYOXMmAJs3b+btt99GrVazYMECxo0bR3V1NYsWLSIlJQW9Xs9LL70kX9E2oFaq8dYYySkpcXYo\nopXM9WOMXbxkW8OqGXa7naysLGbPnk1QUBDz5s1jx44dFBYWMnjwYIKDWz4J1GTSoVarmn2dxWJs\nVdyuqiu1pzu0RW0Ig4NQWFvg1PZKYizEVcoqz8Fmtzl1xbuGYxOTkpKIi4sjPj4eqPsKdunSpfzz\nn//E29ubBx98kIkTJ+Lh4cErr7zCJ598Qnl5OWvXrmXcuHF89NFHmEwm1qxZQ3x8PPv27WPChAlO\na5sQbc1d5YaXu9HleoytViu5uRfLU2VnZ2Ox1PWamUwmAgMDCQkJASAqKorExEQSEhJIS0tjx44d\nnDt3Dnd3d/z9/bnpppuueJ6CguYnM1ksRnJyus7NX1dqT3dpi92uQKPy4GzBuXZvb1OJtyTGQlyl\nixUpnDe++EpjEw0GAwUFBXh6emI21/WSjRw5kh9++AGNRkNUVBQGgwGDwcDSpUsB2L59OwsWLACQ\npdxFl+WjNZNcnEatrRaVsvne044watQo1q5dS2xsLAkJCVitVgwGAwBqtZrg4GCSk5MJDQ0lISGB\nKVOm8OCDDzr2X7t2LUFBQU0mxUJ0FgqFAqvOl4yyLGx2m9PWCJAxxkJcJVeoSNHU2ESz2UxZWRnJ\nyclUV1eze/ducnNzOXv2LJWVlTz00EPcd9997Ny5E4D09HT++9//MmvWLH7/+99TWFjolDYJ0Z58\nNGZsdhuFVUXODsUhMjKSiIgIYmNjWbZsGUuWLGHTpk18/fXXAMTFxfHUU08RGxuL0Wh0TMQToquy\n6izU2GrIr3Te55D0GAtxlTLre4xdaDJWw7GJCoWCVatWERcXh9FopEePHo5thYWFvPzyy2RkZDB7\n9my2b9+O3W4nLCyMRx55hFdffZU33niDJ598ssnzdccxi9IW19WS9gT7+LM3C2o8Klyq/QsXLmz0\nuH///o6fe/bsyYYNG66476OPPtpucQnhDA0rU9RPmu1okhgLcZUySrMwuhkwuhucFkNTYxMBRowY\nwfr16wFYs2YNQUFBVFZWMmTIENRqNSEhIej1evLz8/H19WX48OEAjB49mrVr1zZ7/u42ZlHa4rpa\n2h6tre56TTqXjp8yqF3iEEJcm4aVKQb69HNKDDKUQoirUFlTRV5lvlPHF0Pd2MQvv/wS4JKxiQAP\nPPAAeXl5lJeXs337dqKiohg9ejS7du3CZrNRUFBAeXk5JpOJsWPH8u233zqOFRYW5pQ2CdGefLUX\nahm72AQ8IcRFrlDLWHqMhbgK58rrxxc7dxhFw7GJCoXCMTbRaDQSHR3N9OnTmTt3LgqFgnnz5jkm\n4k2ePJnp06cDsHjxYpRKJbNmzeLJJ59k48aN6HQ6Vq9e7cymCdEuHMtCu9giH0KIiyxaXwCyy3Ob\neWX7kcRYiKuQWer8iXf1mhqbOGnSJCZNmnTJPrGxscTGxjZ6TqvV8tJLL7VPkEK4CG8PL5QKJXkV\nrl3LWIjuTKP2wNvDy6mLfMhQCiGugiuUahNCXD2VUoXZw9vlloUWQjRm1VkoqCqkqva8U84vibEQ\nV6G+VJsrVaQQQrSMj9ZM8fkSztdWOzsUIcQV1I8zznHScApJjIW4Chml5zB5eKNVa5wdihDiKtWP\nM86XXmMhXJZfg8oUziCJsRAtVFZdTtH5YgIM0lssRGfkc6Euaq5UphDCZVl19RPwJDEWwqW5wop3\nQojW89XUlWyTyhRCuK6LPcYylEIIl1a/4p0kxkJ0TvU9xlLLWAjXZdaYUCtU0mMshKvLuFCqTYZS\nCNE5ORLjSinZJoSrUiqU+Op8ySrPwW63d/z5O/yMQnRSmWXnUKDAX2d1dihCiFYwuhlwV7pJj7EQ\nLs5PZ6GytpLi86Udfm5JjIVoAbvdTkbZOXy1ZtxV7s4ORwjRCgqFAh+tWWoZC+HinLk0tCTGQrRA\n8flSyqrLZXyxEJ2cj8ZMRU0l5dXlzg5FCHEFVq3zKlN0iiWha221vPnj30Blx1/jT7AxiGBjEBat\nD0qF5Pai/WXKindCdAmOkm2V+YS46ZwcjRDicvz0zqtl3CkSY4VCQUVNFUl5Z/iJE47nNSoPehgD\nCTYGEWLsQbAxCD+dRZJl0eYulmqTiXdCdGaOkm0V+YQYezg5GiHE5Vjrh1JUSGJ8WUqFkj8MfRi9\nt5pDZ06SVppOanE6aaXpJBUmc6rwjOO17ko3R7IcbOxBiDEIf50VlVLlxBaIzi6j9EKPsQylEKJT\nk5JtQrg+g5sevVonPcbN0blp6WPqRR9TL8dzVbXnSS/NILUknbQLyXJycRqni1Icr1Er1QQZAup6\nlg1BBHsGEaD3x03ZqZovnCiz7BxKhdKxIo8QonOqXxZaSrYJ4dqsOgspJWnU2mo7tHOz02eGHip3\nenmF0ssr1PFcdW016WWZpJWkk1aSTmpJOmdLMkgpTnO8RqVQEaj3I/jCEIwQzyAC9QG4q9yc0Arh\nyuorUvjpLKjlZkqITs3VeoxXrFjB4cOHUSgUxMXFMWjQIMe2zMxM/vCHP1BdXc3AgQN5+umnAXjm\nmWfYv38/NTU1/Pa3v2XSpEnOCl+IduOns3CmOIXcynxHlYqO0KJP+dZcuE3t097cVG6EeoYQ6hni\neK7GVkNmWRapJWdJK8kgteQs6aWZpJVmQGbda5QKJWGeIdx/3Uy8PDw7LF7h2vIrC6mqPS8VKYTo\nArRqDXo3nUuUbNuzZw8pKSnEx8eTlJREXFwc8fHxju2rVq1i7ty5REdH87//+79kZGSQmppKYmIi\n8fHxFBQUcNddd0liLLqk+m9os8tzXCsxbs2Fe/bs2Sb3cQa1Uu2oZlGv1lbLufLsumEYJekkF6WS\nVJTMplOf8ZuI+5wYrXAljooUkhgL0SX4aMxklJ3DZrc5dbL2zp07mThxIgDh4eEUFRVRWlqKwWDA\nZrOxf/9+nn/+eQCWLFkCgJ+fn6OjydPTk4qKCmpra1GpZB6N6Frqk+Gs8hyu78DzNvsX4UoXLuC4\ncMePHw/UXbiBgYFN7uNKVEoVQYYAogKGMb3vL1k4bD49PYPZl3WIkwVJzg5PuAhHRQpZClqILsFH\na6bGVkPx+RKnxpGbm4vJZHI8NpvN5OTUTTbKz89Hr9ezcuVKZsyYwZo1awBQqVTodHVl5jZu3MjY\nsWMlKRZdktVJi3w022Ocm5tLRESE43H9hWswGBpduAkJCQwbNozHH3+8yX1cmVKhJKbvVJ7d9zLx\nJz8lbvhjUs1CkOHoMZbEWIiuwLd+Al5FAd4eXk6O5iK73d7o56ysLGbPnk1QUBDz5s1jx44djBs3\nDoAtW7awceNG3nnnnWaPazLpUKub/yyzWIytjt0VdaX2dMe2eJk1KPYoyK8u6ND2X/VMopZcuE3t\ncyWucuFaLAOZWDCar5O+ZW/BPu7oP7Edz9X9ftE7g5+3JacyBzeVGwOCQ1EqpUa2EJ2dj7a+lnEe\n4d6hTovDarWSm5vreJydnY3FUtdLZjKZCAwMJCSkbq5MVFQUiYmJjBs3jm+//ZbXX3+dt99+G6Ox\n+b+9BQXNr/JnsRjJyXFuD3pb6krt6c5tMWtMnC3KbPP2N5WzNJsYt+bCbWqfK3GlCzc6cAI/pO7n\no5/+RX9D/3bpUejOv+iu7OdtsdltpBVnEqD3Iy+vrMNjEUK0vYsl25w7AW/UqFGsXbuW2NhYEhIS\nsFqtjm9W1Wo1wcHBJCcnExoaSkJCAlOmTKGkpIRnnnmG9957D29vb6fGL0R789NZOJp/goqaCrRq\nbYecs9nur1GjRvHll18CNHnh1m8PCwtrcp/OQO+mY2r4bVTVnmdT4mfODkc4UU5FHjW2GqlIIUQX\ncrFkm3NrGUdGRhIREUFsbCzLli1jyZIlbNq0ia+//hqAuLg4nnrqKWJjYzEajYwfP57PP/+cgoIC\nHnvsMWbNmsWsWbPIyMhwajuEaC8XK1PkNvPKttNsj3HDC1ehUDguXKPRSHR0NHFxcSxatAi73U7f\nvn0ZP348SqXykn06m5EBw/g+Yw/7sw8zKv9G+pl7Ozsk4QT1E+9kfLEQXYdZY0KBwuk9xgALFy5s\n9Lh///6On3v27MmGDRsabY+JiSEmJqZDYhPC2RpWpujpGdwh52zRGOOrvXAvt09no1Qoiek3lWf2\nruWjk5/y1IjHZHGHbijzwlLQgQbpMRaiq3BTqvHy8CTXRRb5EEJcnjMqU8hMoiaEGHswJmgk58qz\n2Z72nbPDEU7gyhUpVqxYQUxMDLGxsRw5cqTRti1btnD33XczY8YM3n//fcfzmzdv5s4772TatGmX\nTJT99ttv6devX0eELoTT+WjMFFYVUWurdXYoQograNhj3FEkMW7GHb0mY3DT83nyFgoqC50djuhg\nGWVZaFQemDxca5JLw4V3li9fzvLlyx3bbDYbS5cu5a233uKDDz5g+/btnDt3joKCAl555RXWr1/P\n66+/ztatWx37VFVV8eabbzY7SVaIrsJXa8aOnXz5uy6Ey/Ly8MRd6SaJsSvRXZiId772PJtOyUS8\n7qTaVkN2eQ4Ben8UCoWzw2mkqUV0CgoK8PT0xGw2o1QqGTlyJD/88AM7d+4kKioKg8GA1Wpl6dKl\njuO9/vrr3Hfffbi7uzulPUJ0NB9NXck2VxhnLIS4PKVCiVVnIbs8F5vd1jHn7JCzdHI3BgwlzLMn\nB7KPcDw/0dnhiA6SXZ6DzW5zyRXvmloxy2w2U1ZWRnJyMtXV1ezevZvc3FzOnj1LZWUlDz30EPfd\ndx87d+4E4MyZMxw/fpxf/OIXTmmLEM5QX5kityLPyZEIIZrip7NQbaumqKq4Q84ns8laoH4i3uq9\nL/HRyU+JG/F7mYjXDVysSOH6E+8aLqKjUChYtWoVcXFxGI1GevTo4dhWWFjIyy+/TEZGBrNnz2b7\n9u2sXLmSxYsXX9X5XGVBno4kbXFdrWlPuL0HHIMKZVmXez+E6ErqS7Zlledg0rT/sEbJ7loo2BjE\n2B5RfHP2B7alfcuknrc4OyTRzuorUrjixLvmFtEZMWIE69evB2DNmjUEBQVRWVnJkCFDUKvVhISE\noNfrycjI4PTp044qMtnZ2cycObPRhL3LcaUFeTqCtMV1tbY96ioNAGl559rk/ZDkWoj2YW0wAa+/\nuU+7n0+GUlyF28PqJuL954xMxOsOMi70GLtiqbbmFtF54IEHyMvLo7y8nO3btxMVFcXo0aPZtWsX\nNpuNgoICysvLCQgIYMuWLXz00Ud89NFHWK3WZpNiIboCLw9P1AoVuTLGWAiX5tfBJdukx/gq6Ny0\n3NV7Cv849hGfJP6LB66f5eyQRDvKKDuHwU2P0c31Vm1sbuGd6dOnM3fuXBQKBfPmzcNsrhtPOXny\nZKZPnw7A4sWLUSrl3lh0T0qFErPGRJ7UMhbCpVk7uGSbJMZXaYR/JN9n7OFgzo8cyzvJAJ++zg5J\ntIOq2vPkVeTT2zvM5SpS1Gtq4Z1JkyYxadKkS/aJjY0lNjb2isfctm1b2wUohIvz0ZrJzj9JZU0V\nGrWHs8MRQlyGVq3B093YYT3G0l10lZQKJTF9p6JAwUeJn1Jtq3F2SKIdnCvLwo7dJYdRCCHaRn3J\ntvzKAidHIoRoip/OQn5lIdW11e1+LkmMW6GHMZCbe9xEdnku21L/6+xwRDvoTBUphBCtU1+yTWoZ\nC+HarDpf7NjJ6YDyipIYt9KUsEkY3Q38J3mr9DZ0Qa68FLQQom34aOprGUtiLIQr68hxxpIYt5LO\nTctd4VOotlXzSeK/nB2OaGOZpRcqUkhiLESX5VvfYyyJsRAurSMrU0hifA1G+EcS7hXKoZyfSMg7\n4exwRBvKKDuHt4cXOjeds0MRQrQTR4+xDKUQwqVJj3EnoVAoiOl3F0qFko9PykS8rqK8uoLCqiIZ\nRiFEF6d306FReUiPsRAuzldjRqlQSo9xZxBkCODmoJvIqchja+o3zg5HtIFz5fXDKGTinRBdmUKh\nwEdrJq8yv9Gy6kII16JSqrBofcguz23+xddIEuM2MKVXNEZ3A18kb5Oehy4gw4WXghZCtC0fjZmq\n2vOUVTe/zLkQwnmsOl/KasopPV/WrueRxLgNaNVapvW+XSbidRGuvBS0EKJt+Wjrahk7q2TbihUr\niImJITY2liNHjjTalpmZyYwZM7jnnnv485//3KJ9hOiqOmqcsSTGbWS43xDCvcI4nJvAT7nHnB2O\nuAaZF3qM/aXHWIguz5kl2/bs2UNKSgrx8fEsX76c5cuXN9q+atUq5s6dy8aNG1GpVGRkZDS7jxBd\nVUdVppDEuI3UTcSbWjcRL3Fzh6zOItpHRtk5fDVmPFTuzg5FCNHOnFmybefOnUycOBGA8PBwioqK\nKC0tBcBms7F//37Gjx8PwJIlSwgMDGxyHyG6MqtWeow7nSBDAON6jCK3Io8tXXgins1uI68inx9z\nj/JVyna+ObMLm93m7LDaRFFlMaXVZQTIMAohugVnlmzLzc3FZDI5HpvNZnJy6j708/Pz0ev1rFy5\nkhkzZrBmzZpm9xGiK/PTd0yPsbpdj94N3RYWzb6sQ3yZso3h/pGO3ojOyG63U1hVRGZZFhll58gs\ny3L8O197/uILk6CvdzizB8Zg0ng7L+A2kFaUCUhFCiG6C7PmwhhjF5g43bAyht1uJysri9mzZxMU\nFMS8efPYsWNHk/tcicmkQ61WNfs6i8V4VfG6uq7UHmkL+NoN6Ny05J7Pa9f3QxLjNqZVa5jW+3be\nO7qBjYmbeWjQr50dUrPsdjsl1aVklNYnvxeT4IqaykavVSlU+OksBOj9CDT446+zcrDgCPvSD7N8\nz1+J7XcXw/wGO6kl1y6tKAOQihRCdBcatQdGN4NTJt9ZrVZycy+Wn8rOzsZiqesVM5lMBAYGEhIS\nAkBUVBSJiYlN7nMlBQXNV9ywWIzk5JS0phkuqSu1R9rSYH+tL2dLMsjKLkKpaP2gh6YS606TGB9P\nKeBccRU+OjVuLbjzdaZhfoP5PmM3P+Ye5cfco1zvO9DZITmUVpeRWXox+a3vCf55qSKlQolV60t/\nUx8C9H4EGPwJ1Pth0fqiUjZ+/ycOjOL/jmxlY+K/eDdhPT/mHiWm713o3LQd2bQ2UZ8YS0UKIboP\nH62ZtJJ0bHbbNX3YXq1Ro0axdu1aYmNjSUhIwGq1YjAYAFCr1QQHB5OcnExoaCgJCQlMmTIFs9l8\nxX2E6OqsWgspxWnkVRRg0fm0yzk6RWJcU2vjuQ8PYbPbUauUhAd60i/Em34hJsIDPXF3c61EWaFQ\nML3vVFbufYGPT26mn6kP7iq3Do3BZreRUpxGRuk5R/KbUXaOkvONJ2koUOCrNdPbK6wuAb6QBFt1\nFtyULfv1UCgUjAq8kT7e4fzt6IfsyzpEUmEyswfG0NcU3h7NazdpRRl1NwW6pntghBBdh4/GRHJx\nKkVVxR06HCwyMpKIiAhiY2NRKBQsWbKETZs2YTQaiY6OJi4ujkWLFmG32+nbty/jx49HqVReso8Q\n3YWfo2RbdvdOjNUqJU/cN4RjaUUcPJHFybRCTqQVwvfJqFUKegV60S/Ym/4h3oQHeblEohxo8OeW\nHqPZmvZfvk7dwZSw6HY/p91u50xxKvuyDnEg+/AlSbCPxsR1Pv0J0Ps7hkL46axtlrRbdb78IfJh\nvkjZxhfJW3np4JtMDLmZKb0mtTjJdia73U5qcQZWrW+niFcI0TZ8tBdLtnX0PImFCxc2ety/f3/H\nzz179mTDhg3N7iNEd+GYgFfRfivgdZpP/77B3oyKDCYnpydlldUkphVxPLWAE2mFJJ4t5GRaIf/6\nAdQqBWEBnvQLMdEvxJveQV54OClRvi1sIvuyDvFVynZu9I/EV9s+dzfppZnsyzrE/qxD5FUWAKB3\n0zEq8EbCPEMIMPjhr/NDo/Zol/M3pFKqmBIWzUBzP/52dANfp+7gaP4Jfj1whssPTyisKqKiupL+\npr7ODkUI0YF8L1SmyKvMpw+9nByNEOJKrFpfoH1LtnWaxLghvcaNwX18Gdyn7g0qr6wh8WwhJ1IL\nOZ5awKn0IhLPFvHZD6BS1ifK3vQPMdUlyu4dkyhr1Bqm9bmddxPW8/HJzTx8w2/a7Ng55Xnszz7E\nvqxDZF5Yqc1D5c4I/0iG+Q2mv6nPJWOBO1KYVwiLhj/GplOf8X3Gblbve4mp4bdxc4+bOnQM39Wo\nX/FOJt4J0b007DEWQrguq64u78suk8S4STqNmht6+3JD74uJ8qn0Qo6n1iXLpzOKOZVexL93pqBS\nKggNMNI/xES/YG969/BC495+b8NQ6w18n76bn/KOXfNEvMKqIg5kH2Ff1iFSitMAUCvV3GC5jmF+\ng7nOZ0CHj2VuikbtwX397+Y6n/58cHwjGxM381PuMWYNnI63h5ezw7tEZlndindSqk2I7sWnQY+x\nEMJ1uavcMXl4y1CKq6XTqBkU7sug8LpEuaKqhsSzRZxIK+BEaiFnMkpISi/m3ztTUCrqEuV+Id70\nCzbRp4cXWo+2e1sUCgXT+01lxZ6/8vHJ/7vqiXhl1eUcyv6RfVmHSCw8jR07SoWSAea+DPUbzGBL\nBFq1a1d/GGSJPE2dWQAAIABJREFUINQrhA+OfcxPecdZvvt5ZvS/m0jrIGeHhs1u41ThaXZl7udg\n9hFAKlII0d2YNd4oULhELWMhRNP8dBaOFyRSWVPVLkNEu2Ri/HNaDzWDwn0YFF43xreiqoak9KK6\nHuW0ApIzSzidUcx/dqWiUECQr56wAE/CAj3pFeBJkEWPStn6r/8D9H6MDx7DltRv+CplO7f3mtTk\n6ytrqvgx9yj7sg5xLP8ktfZaAHp5hTLMbzCR1kEY3TtXeR5PdyMPDfoN32Xs4pPEz1j30/v85D+U\ne/v+Eq1a0+Hx5FbksTtzP7vP7XeMy/bRmPlFv5sdY5iEEN2DSqnCpPF2/C0QQrgu64XEOLsihxBj\njzY/frdIjH9O66Hmul4+XNerLlGuPF9DUnoxx1MLSEwrJDmrhLM5ZXx7pG4VNHe1kp7+RsICPOkV\n6ElYgCe+XhoUCkWLz/mL0AnsyzrE16k7GOEfiYXGxaWrbTUcyzvBvqxD/Jh7lPO2aqBumelhfoMZ\nah2Mj9Z0uUN3GgqFgjFBUfT1Due9ox+y+9x+ThWeZvbAWHp7h7X7+StrqjiY8yO7M/eRWHgaqPta\nZqT/MEYGDCPcOxQ/q1eXKaQuhGg5H42JU4VnqLbVSFUaIVxYfcm27PJc5yXGK1as4PDhwygUCuLi\n4hg06OJX4OPHj8ff3x+Vqm6i13PPPYfBYODJJ5+kqKiI6upq5s+fz5gxY9o8+LaicVcTEWYmIqxu\nnFmtzUZGbjlnMos5nVHsGKOceLbIsY9R51aXKF/oWQ4L8MSgvfIQCc2FFfHeSfiAjxP/j4Eh/4PN\nbuNkQRL7sw5xMOcnKmoqALBofRjmN5hhfoPx74ITwfz0VhYOnc/nyVv4MnkbLxx4neie45gSFo26\njT+QbHYbSYVn2JW5nwM5RxxLWffx7sXIgGEMtlzfIdU6hBCuzUdrJrHwNPmVBY4PXiGE66mfgNde\nlSmazUL27NlDSkoK8fHxJCUlERcXR3x8fKPXvPXWW+j1esfj999/n7CwMB5//HGysrKYM2cOX3zx\nRdtH305USiXBVgPBVgNjbwgEoOp8LSlZdUMu6hPmI0l5HEnKc+xnNWnrEuULyXKI1dCopnKkdRDf\nZ+zmaN4JXti5joSskxSfr+ud9PbwIipgGMP8BhNi7HFVvdGdkUqp4o5ek4nw6cd7CR/yVcp2juWf\n5NcDY9vkZiCvIp9d5/azO3O/Y0KNj8bEjcFjuTFgaLuVzutITd2wbtmyhddeew13d3emTJnCzJkz\nAdi8eTNvv/02arWaBQsWMG7cODIzM3nqqaeoqalBrVbz7LPPNrvErBBdjaNkW0W+JMZCuLCLPcZO\nSox37tzJxIkTAQgPD6eoqIjS0tIml6A0mUycOHECgOLiYkymzj0EAMDDXUXfYG/6Bl8s/l5Udp4z\nmcWcySjm9IX/7jqaxa6jdWW/VEoFPSwGx/CLsEBP7unzS1btfYGdafvRq3WMDryRYX6DCfcOc9ky\nZu2pl1cocSMe4+PEzezK3MeqvS9yV+/bGRsUddU3B1W15zmU/SO7MvdxsjAJAHelGzf6D2VkwDB6\nd6H3uKkbVpvNxtKlS/nnP/+Jt7c3Dz74IBMnTsTDw4NXXnmFTz75hPLyctauXcu4ceN44YUXmD59\nOrfddhsffPAB7777Lk888YSTWyhEx6ov2SaVKYRwbSaNN2ql2nmJcW5uLhEREY7HZrOZnJycRonx\nkiVLSE9PZ+jQoTz++ONMmTKFTZs2ER0dTXFxMW+88Ua7BO9sXnp3Bvf2ZfCFMnF2u53sggpHknw6\ns5jUrBJSskrYfjAdAI27Cv/gcVgtHvjag/Aq0ZBb60ZVQQEGnRsGrRtGrTvubsou32tcT6PWMGvA\ndK73GcD6E5/w0clP+Sn3GDMH3IuXh2eT+9rtdpKKktmVuY8D2YepujBUord3GCP9hzHEej0aJ0zu\na29N3bAWFBTg6emJ2Vz3QT9y5Eh++OEHNBoNUVFRGAwGDAYDS5cuBequXw+PuuEkJpOJhIQE5zRK\nCCeqL9kmtYyFcG1KhRKr1pes8hzsdnub50pXPaDTbrc3erxgwQLGjBmDl5cX8+fP58svv6SqqorA\nwEDWrVvH8ePHiYuLY9OmTU0e12TSoVY3vyCFxWJs9jXOZLV6cl2/i0MBqmtsJGcWcTK1kJOpBZxM\nLSA5qZbkJBuQdsXjuKuVeOrd8dR7XPjvz/95YNS7NdruzKWw2+L/S7TlJob1iuDVPX/n8LmjrNz7\nV347fCYjegy+5LW5ZfnsSN7FN8m7yCqtu2u06MzcHDaSsaEj8Te0/qtQV/8dg6ZvWM1mM2VlZSQn\nJxMUFMTu3bsZMWIEAJWVlTz00EMUFxfz6KOPEhUVhU6nA6C2tpb169czf/58p7RJCGeqn9wsJduE\ncH1WnYWMsnMUnS9u83URmk2MrVYrubkXCylnZ2c3Gn84depUx89jx47l5MmT5OXlMXr0aKBu3ffs\n7Gxqa2sdE/Qup6CgvNlgLRZjp6wY4K1RM6KvLyP6XlyABDcVaemFlJRXU1pRTUlFNaXl1ZRWnG/w\nczUZuaWczqht0Xk83FQYtG4YdG4YtW54GzyIHh5MsLV9S7u17f8XJQ8OmMM3nj/w6al/89z3bxAV\nMJx7+tyBUqHkUM5PdUMlCpKwY8dN6cYI/0hG+g+jj6lX3VCJCsipaF08rvQ7djUJesMbVoVCwapV\nq4iLi8NoNNKjx8VZu4WFhbz88stkZGQwe/Zstm/fjkKhoLa2lieeeIKRI0cSFRXV7Pm6yo3s1ZC2\nuK62aI+PXY+bUk1RTVGXe3+E6GoaVqbo8MR41KhRrF27ltjYWBISErBarY5hFCUlJTz22GOOST57\n9+5l8uTJGAwGDh8+zOTJk0lPT0ev1zeZFHc3Oo0ai8WITtWy7v/qGhulFXWJcmn5hcT5QvJccpnn\nM/PKSKm2AbAz4RxTonpy+02hqFWdY3ytQqFgXI9R9Df15r2EDezM3Mux/JNU1lRSWVsFQLhXKCMD\nhjPEer1T6iA7W3M3rCNGjGD9+vUArFmzhqCgICorKxkyZAhqtZqQkBD0ej35+fn4+Pjw1FNP0bNn\nTx555JEWnb8r38hejrTFdbVle8waM1klua06niTTQnSchpUp+prC2/TYzSbGkZGRREREEBsbi0Kh\nYMmSJWzatAmj0Uh0dDRjx44lJiYGDw8PBg4cyK233kp5eTlxcXHMnDmTmpoa/vKXv7Rp0N2Nm1qJ\nyeiBydjysmJV1bUcSyngH1+eYPP3yRw4mcPcKQMI9W96zK4r8df7sXDYI/z7zNd8nbIDbw8vxgWP\n5kb/oY6Lortq6oYV4IEHHmD16tVotVq2b9/Ob37zG6qrq1m0aBEPPvggRUVFlJeXYzKZ2Lx5M25u\nbixYsMCJLRLC+Xy0JrLKs6moqeyWN9xCdBbtWZmiRWOMFy5c2Ohx//79HT/PmTOHOXPmNNqu1+t5\n8cUX2yA80VoebioG9/alX7A3H28/xY5DGSz7235uvTGEX44Oxa0FX4O7ArVSzS/Df8EvQieiVqq6\nTFWJa9XcDev06dOZO3cuCoWCefPmOSbiTZ48menTpwOwePFilEol69evp6qqilmzZgF1k/nkZlZ0\nRw1LtvUwBjo5GiHElVgvJMbtUctYlvfp4rQeambf2p9h/a2895/jfL4rhYOJOfzmtgH0DmrbcTnt\nyV115cVTuqumblgnTZrEpEmXLj0eGxtLbGxso+c+/PDD9glQiE6mYck2SYyFcF16Nx0GN3279BhL\n91s3MTDUzNP3j2DC0B5k5pWz8h/7+XBrIlXVLZvYJ4QQXZ1Pgx5jIYRrs+os5FUWUGOradPjSmLc\njWjc1fwqui+LfhWJ1aTlq71pLFm3hxOpBc4OTQghnK6+ZFuuLPIhhMvz01mw2W3kVuQ1/+KrIEMp\nuqG+wd78Ze4I/u/bM3y5N5XV6w9yS2QQ99wcjtZDfiWEEN2TrxN6jJta2n38+PH4+/s7qjo999xz\nGAwGnnzySYqKiqiurmb+/PmMGTOmw+IVwlX4OcYZ5+Kv92vm1S0nWVA35eGmYvr43gztb+Hdz4+z\n/UA6R07l8etf9CcizOzs8IQQosPp3HRo1VpyKzvmW7Smlnav99Zbb6HX6x2P33//fcLCwnj88cfJ\nyspizpw5fPHFFx0SrxCupL46VVuPM5ahFN1ceKAXS349nNtv6klBSRVr4g/x3n+O1S1CIoQQ3Yyv\nxkR+Rf4lq7y2hyst7d4Uk8lEYWEhAMXFxZhMpnaPUwhX5NdOlSkkMRa4qZVMGxvOn+YMI9hq4L+H\nM/nTut0cPpXb/M5CCNGF+GjNnLdVU1LddILaFnJzcxsltvVLuze0ZMkSZsyYwXPPPYfdbmfKlClk\nZGQQHR3NzJkzefLJJ9s9TiFcka/WBwWKNu8xlqEUwqGnv5E/zRnG57tS+Nf3yby48QhREX7MmNgX\ng1bKpQkhur6GlSk83Tt2Nbuf91IvWLCAMWPG4OXlxfz58/nyyy+pqqoiMDCQdevWcfz4ceLi4ti0\naVOTx+2OS7hD12qPtOXyrAZfcipz2/SYkhiLRtQqJXeOCiOyr4V3/n2MnQlZJCQXMGtSX4b2szo7\nPCGEaFeOWsYV+YR59WzXczW3tPvUqVMdP48dO5aTJ0+Sl5fH6NGjgbra5dnZ2dTW1jom6F1Od1vC\nHbpWe6QtV+br4UNC6XFSMrLQuemuKo4rkaEU4rJ6WAz8v9lDuXdcOOWVNbzyz5947dOfKC477+zQ\nhBCi3fho6ku2tf8EvFGjRvHll18CXLK0e0lJCffffz/nz9f9zd27dy99+vShZ8+eHD58GID09HT0\nen2TSbEQXVnDyhRtRXqMxRWplEp+MbIng/v48u7nx9l7PJtjKQXcF92HGwf4oVAonB2iEEK0KV9H\nj3Hb1ka9nOaWdh87diwxMTF4eHgwcOBAbr31VsrLy4mLi2PmzJnU1NTI8u2iW2tYmSLMK6RNjimJ\nsWhWgI+eRb+KZOv+s3zyTRJvbj7K3mPZzJrcD2+Dh7PDE0KINmO+MMa4o0q2NbW0+5w5c5gzZ06j\n7Xq9nhdffLFDYhPC1bVHZQpJjEWLKJUKoocHc0NvH977z3EOJuZyIrWQ2Al9mDre4OzwhBCiTbir\n3PByN8qy0EJ0AtYLiXFbVqaQxFhcFatJx8IZQ/jmUAYfbT/FO58f46t9aejcVbi7q/BwU6Fxu/iz\n45+7Cg83ZYOff76t7p9SKcMzROusXftXTpw4Rn5+HpWVlQQGBuHp6cWKFc82u+/nn/8Lvd7AzTff\n0gGRClfnozWTXJxGra0WlVLG77aHjrpeFy36A6tWPd8WIQsX5OXuiYfKXXqMhXMpFQpuGRLE9b3M\nrP86kRNpBaRX1dIW5fDVKiWaC0m0+4VkWeOuQuOu5vpwH266zh8PN/mgEpd69NHfA3UfmqdPJ/HI\nI4+1eN/bbrujvcISnZCPxszpohQKq4ocVSpE2+qo61WS4q5NoVBg1Vk4V5aFzW5Dqbj2mhKSGItW\n8/XSsuCeQVgsRrKzizlfY6Oqupbz52upqq6l0vGzjcrqGs5X26g6f+H56lqqLryu6uc/X3hdSXk1\nudWVVNfYADh0KpdN3yQxbkgQ4yN7YDLK+GbRvAMH9vHhh+9TXl7OI4/8noMH97Njx1ZsNhtRUaOY\nO3ce69a9gbe3N2Fh4Wza9BEKhZKUlDOMGzeBuXPnNTrehg3vX7J/SUkJTz+9mLKyMgwGA3/5ywpq\na2sveW7Dhn/g7e3N3XfHcPr0KZ5//hlefvlNYmPvom/f/owYcSN+fgG8/fbruLm5YTQaefrpVbi5\nufHCC89x9OhPqFQq/vjHp3jvvXXceeddDBs2gvPnzzNz5r2sX/8JarX8Wb9WjpJtlfmSGHewtr5e\np0yZwL//vZWkpFM8//xqFAoFOp2exYv/wqlTiWza9BHLlj3T6LWic/HTWUgrSaegsggf7bWvBCl/\nQUWbUCgUjuEQtLyUYIvYbHYKS6v45lAG2w+m8++dKXyxO5Xh/a1EDw8mLMCzbU8ortlH205xIDGH\n2tq2W1Z3eH8r08f3btW+SUmn2LBhE+7u7hw8uJ9XX30bpVLJ9Om/JCbmvkavPXo0gfXrP8Fms3Hv\nvXdc8kELXLL/hg3/YMSIKO69N5b4+A/Yt28Px48fveS5K8nISGfFiufo1Sucbdu2sGTJMgIDg1i6\n9M/s3r0TDw8PsrOzePPN9zh06ABbt37N5Mm3sXXr1wwbNoL9+/cwcuRNkhS3kfpFPnIrCujbDVZc\n7urXK8CLLz7H7373P0REXMf69f/g448/ZMiQoa2KT7gWq/ZiZQpJjEW3oFQqMHtquGtsL6ZE9WTX\n0Sy+3pvGrqNZ7DqaRe8eXkwaFkxkX4uMURaX1bt3H9zd3QHQaDQ88sg8VCoVhYWFFBcXN3ptv379\n0Wg0VzzW5fY/efI4DzzwMAAxMb8CYPPmTZc8l5h44grH1NKrVzgA3t7erF69jNraWjIy0hk6dDgF\nBflcf/0NAAweHMngwZHU1NTw2msvUVNTw7fffiPDQdqQ74UP17xKmYDnDG15vdZLTj5DRMR1AERG\nDuPdd9+UxLiLaFiZYoBP32s+niTGolNxd1Mx9oZAxgwK4GhyAV/tTePH03mcOluEr5eGiUN7MHpQ\nIDqN/Go70/TxvZkfM8RlVmtyc6tb0vzcuUzi4z/gnXc+QKfTMWvW9Ete29RiCVfaX6lUYbfbGr32\ncs81rP1dU1PTIL6Lv68rVy7l2WdfIDQ0jOefX33FY6nVaoYPH8m+fXs4c+Y01103qMn3QLTcxR7j\n9q9l7Aq66vV6JTU11SiVyktq8Te8JkXnYdW3bck2WflOdEoKhYKIMDO/n34Dyx64kXFDgiguO8+H\n206x8NXvWb/lJNmFFc4OU7iYwsJCTCYTOp2OEyeOc+7cOaqrq695/wEDBrJ//14APv30E/7zn88u\n+5xer3csAXzkyKHLnqOsrBQ/P39KSko4cGC/4/gHDuwD4OTJ46xZU5cwT558G+vWvS49X23M28ML\npUJJXkXH1DIWl3et12tDYWHh/PTTEQAOHjxAv34D0Ov15OXVXY+nTiVSXt780tnC9TQcStEWpFtN\ndHqBvnpmT+7HtLG9+OZQOlv3n2XLvrNs3XeWwX18mTQ8mL7B3rJSn6BPn75otToefngu118/mF/+\nchpr1qxm0KAbrmn/5cufYdmyP/PII/PQ6fT85S/LsNnslzxXXFzMH//4Pxw7lsDgwZGXPce0affy\n8MP3Exwcwq9+NZt33nmT1157h549w/jd7x4A4PHHFwHQv/8AiouLiY6+tW3eIAGASqnC7OEtQymc\n7Fqv14Yee2yhY/Kd0WgkLm4JWq0OjUbLQw/N5frrb8DfP7AdWiHam0atwcvds816jBV2u73tRttf\ng5Z8hWOxGF3mq55rJW1pPzW1NvYdz+arvWkkn6uLq6efkejhPRgxwA+16spflLhSWywWo7NDuCK5\nXl1DamoKa9as5sUXX23xPq7altZqr/a8dPBNThSc4q83L8dd5daiOFxVd7teoWu1R9rSvBcPvMHJ\nwiT+evMy3FXuLYrjSqTHWHQ5apWSkRH+3DjQj1PpRXy1N40DJ3N4+7NjfLwjifGRPRg3OBCjrvmL\nRwhX9emnG9m8+Z/8v//3v84OpUuqH2ecX5mPv97PydEIIZpi1flysjCJnIo8ggwB13QsSYxFl6VQ\nKOjTw5s+PbzJKaxg6/6zfHskg3/+9zSf/ZBMVIQ/0cODCfLVOztUIa7a1Kn3MHXqPc4Oo8uqr1+c\nWyGJsRCurmFlCkmMhWgBi7eW2Al9+OXoML47ksmW/Wn893AG/z2cQUSYmUnDg7kuTAr5CyHq+Grq\nS7bJBDwhXJ21PjEuu/ZxxpIYi25F66EmengwE4b24NCpXL7em0bCmXwSzuQT4KPjrlv6cF2IFxp3\nuTSE6M4cq99VyAQ8IVydn84KQHaFJMZCtIpSqSCyr4XIvhZSzpXw1d409hzL4tWNh9G4q7jpOn/G\nDQmih8Xg7FCvaMWKFRw+fBiFQkFcXByDBl2sY7tlyxZee+013N3dmTJlCjNnzgRg8+bNvP3226jV\nahYsWMC4cePIzMzkiSeeoLa2FovFwrPPPusori9Ed+UYSiGVKYRweWaNNyqFqk0qU0gdY9Ht9fQ3\n8uAdA3n2dzdx3+T+aD3UbDuQzp/X7WHl+/vZlXCO6hpb8wfqQHv27CElJYX4+HiWL1/O8uXLHdts\nNhtLly7lrbfe4oMPPmD79u2cO3eOgoICXnnlFdavX8/rr7/O1q1bAXjppZe47777WL9+PT179mTj\nxo3OapYQLsPoZsBd6SY9xkJ0AiqlCovWh+zyHK612JokxkJc4G3wYMakfjzzcBSPTLueiDAziWeL\nePNfR1n46vd8vOMUOS6yaMjOnTuZOHEiAOHh4RQVFVFaWgpAQUEBnp6emM1mlEolI0eO5IcffmDn\nzp1ERUVhMBiwWq0sXboUgN27dzNhwgQAbrnlFnbu3OmcRl2j3/72Nxw/fqzRc6+//jIbNrx/2dcf\nOLCPxYufAGDRoj9csv2TT+JZt+6Ntg9UdAoKhQIfrVlqGbeTjrheP//8X3zzzfY2ili4Oj+dhYqa\nSkqry67pODKUQoifUSmVjmEWWQXlfHMwg+9+zOQ/u1L5Ylcq1/XyYdyQQG4I90WpdM6iIbm5uURE\nRDgem81mcnJyMBgMmM1mysrKSE5OJigoiN27dzNixAgAKisreeihhyguLubRRx8lKiqKiooKx9AJ\nHx8fcnKa/yrKZNKhVje/FGtH1na9665fsmvXN4wZM8Lx3Hff7eDvf//7ZePw9tbh4eGGxWJk3bq3\nLtluMGiorvZw7OvKdWqvVldqC7RfewK8rGRmZKH1UmJwl+o1bSk6ejLbtn1N//4DHM/t2LGNtWtf\nb3bfVaueb9E5brvtjlbHJzofa4PKFEb31g+DlMRYiCb4mXRMH9+bu8aGse94DtsPpvPj6Tx+PJ2H\n2dODm28IZOwNgXgZPJwaZ8OvjhQKBatWrSIuLg6j0UiPHj0c2woLC3n55ZfJyMhg9uzZbN++/YrH\naUpBQfNLp3Z0UfobbxzLww/fz69//RAAx48fw2TyQanU8fnnW3j77ddxc3PDaDTy9NOrKCwsp6qq\nmpycEqZMmcC//72Vffv28NJLazCbffDx8SUwMIicnBJHW7Kzs1i69M8A1NTUsHjx/xIU1IMvvvg3\nGzfGo1AoiI39FRMmTLrsc/XnAVi8+AmmTZvOwYP7ychIJzMzgxdeeJWVK58mJyebiooK5s6dx6hR\nYxzLQCuVCq677gamTLmTZ55Zzquvvg3A3/62Dp1Oz733xjb7PnWlxQKgfdtjVHoCcOJsKiHGHk2+\ntrXJeVNzBcaPH4+/vz8qVd1N6HPPPYefn99l5wp0NhMmTOLhh+/nd79bANRdrxaLBYvFyt69uy+5\nXhtq6nptaN26N/D29ubuu2N49dUX+fHHw9TU1HL33dO59dYpPPLIPP7whyfo1as3n3wST2FhIfff\n/9sOew9E27qYGGfT2zus1ceRxFiIFnBTq4i6zp+o6/xJzSphx6EMdiac45/fnmHz98kM6ePLLUOC\n6N/T1CFLT1utVnJzcx2Ps7OzsVgsjscjRoxg/fr1AKxZs4agoCAqKysZMmQIarWakJAQ9Ho9+fn5\n6HQ6Kisr0Wg0ZGVlYbVarzm+Tac+48iun6i1td3CmkOs1zOt9+1X3G4ymQkMDOLo0Z8YOPA6tm37\n2rFUcklJCUuWLCMwMIilS//M7t070el0lxzjjTde5k9/WkqfPn1ZuHDBJR+0eXm5/OY3DxIZOYzP\nPvs/Nm36mPvvn8d7773N3/62gfPnq1m+fAlRUaMueW7ChElXjL2mpppXX32bgoJ8RowYyS9+cTvp\n6Wf5058WMWrUGF544Tn++Mc4evfuw9Klf0aj0VBdfZ7s7CysVj9++OE7Vq58rpXvrLgSR8m2ioJm\nE+PWaDhXICkpibi4OOLj4xu95q233kKvv9hbXT9X4JNPPqG8vJy1a9dec2LcVa/XeocOHeD06SRe\ne+0dKioqmDMnlrFjx7VJO4XrqK9lnF2e28wrm9aiMcYrVqwgJiaG2NhYjhw50mjb+PHjue+++5g1\naxazZs0iKysLqJv9fueddzJt2jR27NhxTUEK4UpC/IzMntyP5+ePYtakvgT46Nh3IodnPzzE/3tr\nN1/tTaOssrpdYxg1ahRffvklAAkJCVitVgyGi18dPfDAA+Tl5VFeXs727duJiopi9OjR7Nq1C5vN\nRkFBAeXl5ZhMJm666SbHsb766ivGjBnTrrG3p+joW9m69WsAvv/+v4wbVzd22tvbm9Wrl/HII/M4\neHA/xcVFl90/MzOTPn36AjB4cOQl281mHz7++EPmz3+Qjz5aT3FxEcnJZwgJCcXDQ4PRaGTVqucv\n+1xTBgyoGxZjNHpy7FgCDz88l+XL/+KIMzU1hd69+wDwpz89jb9/AJMm3ca2bV+Tm5uDXm/AbPZp\nxTsmmuIo2dZO44ybmivQ1D6XmyvQGbX39Vrv+PGjju1arZbQ0F6kpaW1ZVOEC7DqfAGuuTJFsz3G\nrnJHK4Sr0XqouSWyB+OGBHEqvYgdB9PZezybD7cmsumbJEYM8OOWyCDCAjzb/NyRkZFEREQQGxuL\nQqFgyZIlbNq0CaPRSHR0NNOnT2fu3LkoFArmzZuH2Vz3AT958mSmT58OwOLFi1EqlTz66KM8+eST\nxMfHExgYyNSpU685vmm9b+e3UTM6/Cv7m2++hb///R2ioycTHByCp2fde79y5VKeffYFQkPDeP75\n1VfcX6m82FdwuWEl69a9wY03jmTq1HvYvn0LP/zwHUqlCru9cdWSyz33czU1NY6f3dzcAPj66y8o\nLi7mlVfO0asyAAAJbElEQVTepri4mAcemHVJXPUmTpzM4sVPoNFoiY6e3OS5ROvULwvdXpUpmpor\nUG/JkiWkp6czdOhQHn/8cc6ePXvZuQJNaW5OwG8tM669Ma0wbdod3H333dx7712Eh/ciPLyux/eZ\nZ5bx5ptvEh4eztNPP43RqGk0J0ChUGCxGFGrVY4hLDqdO2p14yEter0HBoOGmho158+fd2xTKu34\n+Bhwd1djMumxWIxoNGr0eg+XHX/vqnG1Rnu1xdduQO+uI68q75rO0WxifKU72oYX7uX2qb+jNRgM\nnfqOVojmNFx6OnZCH777MZMdB9P57sdMvvsxk57+Rm4ZEsSNA/zwcG9+wlpLLVy4sNHj/v37O36e\nNGkSkyZd+tV9bGwssbGNx6FarVbefffdNovLmXQ6PeHhffj73991fC0LUFZWip+fPyUlJRw4sJ/w\n8D6X3d/X10JqajLBwT05eHA/ERHXN9peWFhIUFAP7HY73333DbW1Nnr2DCU1NYXy8nJUKhVPPvl7\nVq5cc8lzf/3rKygUCiorKwE4efLEJecvLCwkICAQpVLJN99so7q67puH0NAwEhJ+IiLiOlaufJoZ\nM2YRGhqGp6cnX375OWvWvNRWb6FooOGy0B3h5zdjCxYsYMyYMXh5eTF//nzHNzuXmyvQ1BAuV5wT\nUC80NJyXXnqFCROiHecvLi7Bzc3I6dMZfP/9TgIDe6LXX5wTYLfbyckpwWz2Zf/+HwkO7sl33/1A\nRMT1jmNYLEbKyqpwc6ukd+9+/O1v67jrrhmUl5dz5kwyer0P7u4aTp1KwWQKYOfO3YSFhbvk+Puu\nNC+gvdti1fiSUnKWc1mFqJRX/rxtKnFuNjHuqDtaIboCo86dX9zYk8kjQjh6Jp/tB9M5dCqX9/5z\nnPhtpxwLhwT5ygz39hIdfSvLli1hyZKLN+TTpt3Lww/fT3BwCL/61WzeeedN5s373SX7zpv3OxYv\nfhJ//wCsVr9Ltv/yl9P461+fxd8/kHvuieGZZ5bz44+Huf/+h3jssbrjxcTch1arveQ5hULB1Kn3\nMG/eHEJDe9Gv34BLjj9u3HgWLfoDR4/+xJQpd164aXmL//mfhTz33EoAIiKuJzQ07MLrJ/D999+i\n08nvU3vQqjXo1bp2G0rR3FyBht/ejB07lpMnTxIUFHTZuQI+Pp1zKE17Xq/1brhhMP369Wf+/Aep\nqanhoYceQavVcued01iz5hmCg4MJCmr7MeSi41l1Fs4Up5JXme+YjHe1FPZmpqH/6U9/4uabb3b0\nGs+YMYMVK1YQFlb3h/nTTz9tdEd71113kZqayoEDB67qjramprZF5Z+E6GxyCir4ancKX+1OJr+4\nCoC5d0Rw17jeTo6s9Vpyxy+9HO1v2bIl3HbbHURGDmvxPq7altZq7/as3vsSmWXn+OvNy5v8DGvN\nV7cHDhxg7dq1vPvuuyQkJLBs2TI2bNgA1E1Ae+yxxxwrWD722GNMnjyZyMhIFi1axLp16ygqKmLa\ntGls3br1ssNt6nW36xW6VnukLS33RfI2/nX6Cx4e9Buu872086FhHFfSbI9xR93RuvJXPe1B2uKa\n2qstk4YGMX5wAIcSc9l1NAt3ZfMfVl1pTJloW1VVVTz66G8ZMGDgVSXF4uqN6zGK5OK0dqk209xc\ngbFjxxITE4OHhwcDBw7k1ltvRaFQXHaugBACIq3Xk1ycSpAhoNXHaDYxHjVqFGvXriU2NvaS2e8/\nv6Pdu3dvozvaBx98kKKiIsfsdyG6M7VKybD+Vob1v/ZyaKJ78/Dw4M0333N2GN3CjQFDuTFgaLsd\nv6m5AnPmzGHOnDmX7HO5uQJCiLqhFA8N+vU1HaPZxFjuaIUQQgghRHfQogU+5I5WCCGEEEJ0ddKN\nK4QQQgghBJIYCyGEEEIIAUhiLIQQQgghBCCJsRBCCCGEEIAkxkIIIYQQQgCSGAshhBBCCAG0YElo\nIYQQQgghugPpMRZCCCGEEAJJjIUQQgghhAAkMRZCCCGEEAKQxFgIIYQQQghAEmMhhBBCCCEASYyF\nEEIIIYQAOklivGLFCmJiYoiNjeXIkSPODueaPfPMM8TExHD33Xfz1VdfOTuca1ZZWcnEiRPZtGmT\ns0O5Jps3b+bOO+9k2rRp7Nixw9nhdGpd6ZqV69U1yfXaduR6dV1d5XqFznPNqp0dQHP27NlDSkoK\n8fHxJCUlERcXR3x8vLPDarX/397dgsTSxmEYv/asGHRUdPEDQYtREBUMGmSDaLFYZDHIRqPRIBhM\nW/1Ag9iFXTCZBBcMY7HZ1KTIosOOiqhleE4znHTenX3P88xw/9puusNe8GcY2KurK25vbzk5OSEM\nQ5aWlpifn7c9K5aDgwO6urpsz4glDEP29/epVCp8fn6yu7tLPp+3PSuR0tSsenWTem0e9eq2NPQK\nyWrW+cPY933m5uYAGBkZ4e3tjY+PDzzPs7ysMVNTU4yNjQHQ2dnJ19cXURSRzWYtL2vM/f09d3d3\nzv7A/5bv+0xPT+N5Hp7nsb29bXtSYqWpWfXqJvXaPOrVXWnpFZLVrPOvUgRBQHd398/nnp4eXl5e\nLC6KJ5vN0tbWBkC5XGZ2djax0QKUSiU2NjZsz4jt8fGR7+9v1tbWWFlZwfd925MSK03Nqlc3qdfm\nUa/uSkuvkKxmnX9i/Ke0/IP1+fk55XKZ4+Nj21Madnp6yvj4OENDQ7anNMXr6yt7e3s8PT2xurrK\nxcUFmUzG9qzES0Oz6tU96vX/oV7dkLZeITnNOn8Y9/X1EQTBz+fn52d6e3stLorv8vKSw8NDjo6O\n6OjosD2nYdVqlYeHB6rVKrVajdbWVgYGBpiZmbE97T/L5XJMTEzQ0tLC8PAw7e3t1Ot1crmc7WmJ\nk7Zm1at71GvzqFc3palXSFizxnHX19emWCwaY4y5ubkxhULB8qJ43t/fzeLiogmCwPaUptrZ2TGV\nSsX2jIbVajVTLBZNFEWmXq+bfD5voiiyPSuR0tSsenWTem0e9eq+pPdqTLKadf6J8eTkJKOjoxQK\nBTKZDFtbW7YnxXJ2dkYYhqyvr/98VyqVGBwctLhK+vv7WVhYYHl5GYDNzU1+/XL+FXwnpalZ9eom\n9do86lX+hSQ1mzEmBS8UiYiIiIjE5Oa5LiIiIiLyj+kwFhERERFBh7GIiIiICKDDWEREREQE0GEs\nIiIiIgLoMBYRERERAXQYi4iIiIgAOoxFRERERAD4DeSk1zeeZikeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fafdfeb71d0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(131)\n",
    "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
    "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
    "plt.legend()\n",
    "plt.subplot(132)\n",
    "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
    "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
    "plt.legend()\n",
    "plt.subplot(133)\n",
    "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
    "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7dpxfbugzcLT"
   },
   "source": [
    "## Sample Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c-eU6Le2zdtF"
   },
   "outputs": [],
   "source": [
    "THRESHOLD = 0.5\n",
    "# look at some sample predictions\n",
    "samples = np.random.choice(valid_filenames, size=8, replace=False)\n",
    "\n",
    "for filename in samples:\n",
    "    # load the image\n",
    "    img = pydicom.dcmread(os.path.join(train_dicom_dir, filename)).pixel_array\n",
    "    \n",
    "    msk = np.zeros(img.shape)\n",
    "    \n",
    "    filename = filename.split('.')[0]\n",
    "    # if image contains pneumonia\n",
    "    if filename in pneumonia_locations:\n",
    "        # loop through pneumonia\n",
    "        for location in pneumonia_locations[filename]:\n",
    "            # add 1's at the location of the pneumonia\n",
    "            x, y, w, h = location\n",
    "            msk[y:y+h, x:x+w] = 1\n",
    "        \n",
    "    # resize both image and mask\n",
    "    msk = resize(msk, (IMAGE_SIZE, IMAGE_SIZE), mode='reflect') > 0.5\n",
    "    img = resize(img, (IMAGE_SIZE, IMAGE_SIZE), mode='reflect')\n",
    "    \n",
    "    # predict the image\n",
    "    yhat = model.predict(img.reshape(1, IMAGE_SIZE, IMAGE_SIZE, 1))\n",
    "    yhat = yhat > THRESHOLD\n",
    "    # add trailing channel dimension\n",
    "    msk = np.expand_dims(msk, -1)\n",
    "\n",
    "    f, ax = plt.subplots(1, 3, figsize=(10, 4))\n",
    "    ax[0].imshow(img.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
    "    ax[0].set_title(\"Image\")\n",
    "    ax[1].imshow(msk.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
    "    ax[1].set_title(\"Label\")\n",
    "    ax[2].imshow(yhat.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
    "    ax[2].set_title(\"Prediction\")\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3tAntxescxUq"
   },
   "source": [
    "# Predict test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "fnu7ebfScxUu",
    "outputId": "2e91d334-3204-4213-b74c-4c112b1c3512"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n test samples: 1000\n"
     ]
    }
   ],
   "source": [
    "# load and shuffle filenames\n",
    "folder = './stage_1_test_images'\n",
    "test_filenames = os.listdir(folder)\n",
    "print('n test samples:', len(test_filenames))\n",
    "\n",
    "# create test generator with predict flag set to True\n",
    "test_gen = generator(folder, test_filenames, None, batch_size=25, image_size=IMAGE_SIZE, shuffle=False, predict=True)\n",
    "\n",
    "# create submission dictionary\n",
    "submission_dict = {}\n",
    "# loop through testset\n",
    "for imgs, filenames in test_gen:\n",
    "    # predict batch of images\n",
    "    preds = model.predict(imgs)\n",
    "    # loop through batch\n",
    "    for pred, filename in zip(preds, filenames):\n",
    "        # resize predicted mask\n",
    "        pred = resize(pred, (1024, 1024), mode='reflect')\n",
    "        # threshold predicted mask\n",
    "        comp = pred[:, :, 0] > 0.5\n",
    "        # apply connected components\n",
    "        comp = measure.label(comp)\n",
    "        # apply bounding boxes\n",
    "        predictionString = ''\n",
    "        for region in measure.regionprops(comp):\n",
    "            # retrieve x, y, height and width\n",
    "            y, x, y2, x2 = region.bbox\n",
    "            height = y2 - y\n",
    "            width = x2 - x\n",
    "            # proxy for confidence score\n",
    "            conf = np.mean(pred[y:y+height, x:x+width])\n",
    "            # add to predictionString\n",
    "            predictionString += str(conf) + ' ' + str(x) + ' ' + str(y) + ' ' + str(width) + ' ' + str(height) + ' '\n",
    "        # add filename and predictionString to dictionary\n",
    "        filename = filename.split('.')[0]\n",
    "        submission_dict[filename] = predictionString\n",
    "    # stop if we've got them all\n",
    "    if len(submission_dict) >= len(test_filenames):\n",
    "        break\n",
    "\n",
    "# save dictionary as csv file\n",
    "sub = pd.DataFrame.from_dict(submission_dict,orient='index')\n",
    "sub.index.names = ['patientId']\n",
    "sub.columns = ['PredictionString']\n",
    "sub.to_csv('submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "U1NEg9QCcxU4",
    "outputId": "c7e7ccf3-3e94-4996-a07d-a1098b69f3ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully submitted to RSNA Pneumonia Detection Challenge"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit -c rsna-pneumonia-detection-challenge -f submission.csv -m \"Colab segmentation 4 448x448 8 epochs\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "NTQ_vDaVfvZR",
    "outputId": "a9eb81fb-572d-4236-b497-6bc44130aa89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File ID: 1GGJOHEa4uJTtlo-cvF2NLYjCbKp6hq3_\n",
      "File ID: 1C7SXJVn51_rnrx8bU_1v8k-zeFhHybkJ\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'id': '1C7SXJVn51_rnrx8bU_1v8k-zeFhHybkJ'}"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_file_to_drive(\"submission.csv\", \"submission.csv\")\n",
    "save_file_to_drive(CHECKPOINT_PATH, CHECKPOINT_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "yf06y04TVRi2",
    "outputId": "1ca39a9a-1aa6-4e2a-ec86-39a826c676dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated property [core/project].\n",
      "Copying file://./model7_448.h5 [Content-Type=application/octet-stream]...\n",
      "|\n",
      "Operation completed over 1 objects/93.8 MiB.                                     \n"
     ]
    }
   ],
   "source": [
    "# upload checkpoint to GCS\n",
    "project_id = 'mammography-198911'\n",
    "bucket_name = 'pneumonia'\n",
    "\n",
    "!gcloud config set project {project_id}\n",
    "!gsutil cp ./{CHECKPOINT_PATH} gs://{bucket_name}/"
   ]
  }
 ],
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   "version": "0.3.2"
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