APS360_Project.ipynb

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      "source": [
        "# Imports\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import time\n",
        "import torch\n",
        "import torch.utils.data as data_utils\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import matplotlib.pyplot as plt\n",
        "import torchvision\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "from torch.utils.data import WeightedRandomSampler\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.transforms.functional as TF\n",
        "\n",
        "from matplotlib.image import imread\n",
        "\n",
        "from google.colab import drive\n",
        "torch.manual_seed(1000)\n",
        "# drive.mount('/content/gdrive')\n",
        "\n",
        "from PIL import Image\n",
        "import os\n"
      ],
      "metadata": {
        "id": "2Uz-MPyuwcHi"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get install git -y\n",
        "!git clone https://github.com/KevinGe00/Pneumonia-CNN-Classifier.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrjjJV9ewQLk",
        "outputId": "6176009f-0bf4-492c-fb6a-0265d35f3e59"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "git is already the newest version (1:2.25.1-1ubuntu3.10).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 23 not upgraded.\n",
            "Cloning into 'Pneumonia-CNN-Classifier'...\n",
            "remote: Enumerating objects: 7124, done.\u001b[K\n",
            "remote: Counting objects: 100% (715/715), done.\u001b[K\n",
            "remote: Compressing objects: 100% (708/708), done.\u001b[K\n",
            "remote: Total 7124 (delta 6), reused 714 (delta 5), pack-reused 6409\u001b[K\n",
            "Receiving objects: 100% (7124/7124), 139.70 MiB | 39.94 MiB/s, done.\n",
            "Resolving deltas: 100% (6/6), done.\n",
            "Updating files: 100% (7130/7130), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# drive.mount('/content/gdrive')\n",
        "\n",
        "\n",
        "\n",
        "import torchvision.datasets as datasets\n",
        "from torch.utils.data import DataLoader, WeightedRandomSampler\n",
        "\n",
        "# define the parent directory where the train and test subdirectories are located\n",
        "# CHANGE THIS TO MATCH YOUR PARENT DIRECTORY\n",
        "parent_dir = '/content/Pneumonia-CNN-Classifier/Data_new'\n",
        "\n",
        "# define the data transformations to be applied to the images\n",
        "train_transforms = transforms.Compose([\n",
        "    transforms.Resize(size=224),\n",
        "    transforms.RandomResizedCrop(size=224),\n",
        "    transforms.RandomRotation(degrees=15),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "])\n",
        "test_transforms = transforms.Compose([\n",
        "    transforms.Resize(size=224),\n",
        "    transforms.CenterCrop(size=224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9BoIxQzIwa6P"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Apply data augmentation techniques on dataset\n",
        "\n",
        "######## train #############\n",
        "train_data = datasets.ImageFolder(os.path.join(parent_dir, 'train'), transform=train_transforms)\n",
        "# set color_mode to 'RGB'\n",
        "train_data.color_mode = 'RGB'\n",
        "\n",
        "# set class_mode to 'categorical'\n",
        "train_data.class_mode = 'categorical'\n",
        "\n",
        "# Calculate class frequency\n",
        "class_freq = np.array([len(np.where(np.array(train_data.targets) == t)[0]) for t in range(len(train_data.classes))])\n",
        "print(f'Class frequency wrt train: {class_freq}')\n",
        "\n",
        "# Calculate class weights to balance class frequency\n",
        "class_weights = 1.0 / torch.tensor(class_freq, dtype=torch.float)\n",
        "print(f'Class weights wrt train: {class_weights}')\n",
        "\n",
        "# Create sampler to be used in DataLoader\n",
        "train_targets = torch.tensor(train_data.targets)\n",
        "train_sampler = WeightedRandomSampler(weights=class_weights[train_targets], num_samples=len(train_targets), replacement=True)\n",
        "\n",
        "# Confirm balanced class frequency\n",
        "train_freqs_after_sampler = [0] * len(train_data.classes)\n",
        "for idx in train_sampler:\n",
        "    train_freqs_after_sampler[train_data.targets[idx]] += 1\n",
        "print(f'Class frequency after weighting for training set: {train_freqs_after_sampler}')\n",
        "\n",
        "train_loader = DataLoader(train_data, batch_size=32,sampler=train_sampler, num_workers=4)\n",
        "\n",
        "\n",
        "# fix normalization for train data set\n",
        "# Compute the mean and standard deviation values for each channel\n",
        "mean = np.zeros(3)\n",
        "std = np.zeros(3)\n",
        "for images, _ in train_loader:\n",
        "    for i in range(3):  # iterate over each channel\n",
        "        channel_sum = images[:, i, :, :].sum()\n",
        "        num_pixels = images[:, i, :, :].numel()\n",
        "        mean[i] += channel_sum / num_pixels\n",
        "        std[i] += ((images[:, i, :, :] - mean[i]) ** 2).sum() / num_pixels\n",
        "\n",
        "mean /= len(train_loader)\n",
        "std = np.sqrt(std / len(train_loader))\n",
        "\n",
        "print(f\"Mean: {mean}\")\n",
        "print(f\"Standard deviation: {std}\")\n",
        "\n",
        "# Now, update the transformer\n",
        "\n",
        "train_transforms = transforms.Compose([\n",
        "    transforms.Resize(size=224),\n",
        "    transforms.RandomResizedCrop(size=224),\n",
        "    transforms.RandomRotation(degrees=15),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=mean, std=std)\n",
        "])\n",
        "\n",
        "\n",
        "\n",
        "train_data = datasets.ImageFolder(os.path.join(parent_dir, 'train'), transform=train_transforms)\n",
        "train_loader = DataLoader(train_data, batch_size=32,sampler=train_sampler, num_workers=4)\n",
        "\n",
        "\n",
        "###########################\n",
        "\n",
        "####### test ###############\n",
        "test_data = datasets.ImageFolder(os.path.join(parent_dir, 'test'), transform=test_transforms)\n",
        "final_test_data = datasets.ImageFolder(os.path.join(parent_dir, 'test_final'), transform=test_transforms)\n",
        "# set color_mode to 'RGB'\n",
        "test_data.color_mode = 'RGB'\n",
        "final_test_data.color_mode = 'RGB'\n",
        "\n",
        "# set class_mode to 'categorical'\n",
        "test_data.class_mode = 'categorical'\n",
        "final_test_data.class_mode = 'categorical'\n",
        "# Calculate class frequency\n",
        "class_freq = np.array([len(np.where(np.array(test_data.targets) == t)[0]) for t in range(len(train_data.classes))])\n",
        "print(f'Class frequency wrt test: {class_freq}')\n",
        "\n",
        "# Calculate class weights to balance class frequency\n",
        "class_weights = 1.0 / torch.tensor(class_freq, dtype=torch.float)\n",
        "print(f'Class weights wrt test: {class_weights}')\n",
        "\n",
        "# Create sampler to be used in DataLoader\n",
        "test_targets = torch.tensor(test_data.targets)\n",
        "test_sampler = WeightedRandomSampler(weights=class_weights[test_targets], num_samples=len(test_targets), replacement=True)\n",
        "\n",
        "final_test_targets = torch.tensor(final_test_data.targets)\n",
        "final_test_sampler = WeightedRandomSampler(weights=class_weights[final_test_targets], num_samples=len(final_test_targets), replacement=True)\n",
        "# Confirm balanced class frequency\n",
        "freqs = [0] * len(test_data.classes)\n",
        "for idx in test_sampler:\n",
        "    freqs[test_data.targets[idx]] += 1\n",
        "print(f'Class frequency after weighting for test set: {freqs}')\n",
        "test_loader = DataLoader(test_data, batch_size=32, sampler=test_sampler, num_workers=4)\n",
        "final_test_loader = DataLoader(final_test_data, batch_size=32, sampler=final_test_sampler, num_workers=4)\n",
        "\n",
        "\n",
        "#fix normalization for validation data set\n",
        "# Compute the mean and standard deviation values for each channel\n",
        "mean = np.zeros(3)\n",
        "std = np.zeros(3)\n",
        "for images, _ in test_loader:\n",
        "    for i in range(3):  # iterate over each channel\n",
        "        channel_sum = images[:, i, :, :].sum()\n",
        "        num_pixels = images[:, i, :, :].numel()\n",
        "        mean[i] += channel_sum / num_pixels\n",
        "        std[i] += ((images[:, i, :, :] - mean[i]) ** 2).sum() / num_pixels\n",
        "\n",
        "mean /= len(test_loader)\n",
        "std = np.sqrt(std / len(test_loader))\n",
        "\n",
        "print(f\"Mean: {mean}\")\n",
        "print(f\"Standard deviation: {std}\")\n",
        "\n",
        "# Now, update the transformer\n",
        "\n",
        "test_transforms = transforms.Compose([\n",
        "    transforms.Resize(size=224),\n",
        "    transforms.RandomResizedCrop(size=224),\n",
        "    transforms.RandomRotation(degrees=15),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=mean, std=std)\n",
        "])\n",
        "\n",
        "\n",
        "\n",
        "test_data = datasets.ImageFolder(os.path.join(parent_dir, 'test'), transform=test_transforms)\n",
        "test_loader = DataLoader(test_data, batch_size=32,sampler=test_sampler, num_workers=4)\n",
        "\n",
        "############################\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "y9XXjNtFhu30",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6d04718-36da-436e-81d6-9720eed312fa"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class frequency wrt train: [ 460 1266 3419]\n",
            "Class weights wrt train: tensor([0.0022, 0.0008, 0.0003])\n",
            "Class frequency after weighting for training set: [1656, 1700, 1789]\n",
            "Mean: [0.03336268 0.01061289 0.00072629]\n",
            "Standard deviation: [3.08293087 0.98005006 0.06638197]\n",
            "Class frequency wrt test: [116 317 855]\n",
            "Class weights wrt test: tensor([0.0086, 0.0032, 0.0012])\n",
            "Class frequency after weighting for test set: [449, 406, 433]\n",
            "Mean: [0.05905529 0.18609855 0.41793898]\n",
            "Standard deviation: [1.59742853 4.32592539 9.64676909]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory statistical analysis\n",
        "\n"
      ],
      "metadata": {
        "id": "sctAAjAkTQjt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize images from each class\n",
        "# I chose to do this on the training set\n",
        "\n",
        "train_path = os.path.join(parent_dir, 'train')\n",
        "\n",
        "covid_path = os.path.join(train_path, \"COVID19\")\n",
        "pneumonia_path = os.path.join(train_path, \"PNEUMONIA\")\n",
        "normal_path = os.path.join(train_path, \"NORMAL\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "s8U3Sr9vaH6K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "covid_sample = os.listdir(covid_path)[0]\n",
        "plt.imshow(imread(os.path.join(covid_path, covid_sample)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TbRqlWc4aneP",
        "outputId": "3bb96926-0f80-4a2c-ca7a-c5c0a6ef319f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fcf7282bca0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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PFTg8dMvvc/tuNRx2+zQW9cW6ESzkNyzn0dGRut1uVjggJlAJ8/Dezhjwin6/94vHAngf8Q7cKW+juzceDY/+uPv9i4uLNXjsgoEbQB2lUVzDrSzluxA63I7oyi11ybq7u4KSYgxBWi7k7ibx32dxKM8VliMrR4szrwTwsXd2dnTjxg3t7+9rYWFBh4eHev755/Xaa69pZ2cnJ+S88MILOZDzpS99SQsLC+r1erp165YODw919+7dHKUdDof64he/mJNFSCZZXV3VRz7ykVpA6O7duzo6OtK1a9e0u7urtbW12tQfwuTWzL9Tjltfj9IjcPihQGkgM88x8+CKhzq4RYHRo38eLa5D75LAuFXy+1ygHT57co7HGhyxOFOW+galAxpCKPnd5/IROITX4S91cx8aZeezCx44jFOLMYhJ8NHHndgN73c/HaVFAhBUUgSumFD00sgl8byHJlmhjq58IrKILkV0VZxmQglUVaXNzU21222trq5mS7m+vp6t4cLCglZXV3X79u3sr+3v7+e5cPxrsrLw3z2AtL+/r7W1tTwjgNUdDAY5/Rfof3h4qO3t7Qw93d91347OdkTAvR4NhqkJ8HkwDsvvzBHhHhDUfUi3Cl6fpsiwuw0IiU9ronxgKlc4JaZyBINFwup6dp8Lp08N4jbg1jhicXeB8kFnkaHdDfFcCpSBIwxPtfZZBUk1yx+VH+1mtsh/c3ePujmCcHIlR1AZdNLv93Xv3r3i2Ln70jSu0U2AIhKLNBNKQDoekFdffTULhE9/DYfHqb6DwUBra2uZcYgdeCIGkBbGItC1sLCQFQXMR9Dv4OAgJ41I0r1799TpdPTCCy/kOvhUlAfn6GC3gPwxMJ4Our29nacMPbHGg24epHLY6O9DCHneGcFdALdyHqvAuqFkKUdS9jGl0VRmFK7IWN52nyJ15uQa/Y/Q+XOuHFAe/g6UlAc4I4MT5Wfs3Y2jzT7LwDsphz73OoMgfIrXcw1Y+4ExiP3pbkaE/KWsS38+lsV/N0YgSS8TFOT3lmgmlEC73daf//N/Xr/xG7+hmzdvamlpKSfJuK9EZJgUUBoNY2xsbEg6nubDmrr1nJubU7/f1+LiotbX1/O04OrqqhYXF7Mm3t3d1bvf/W7t7Oxoa2sr+7z42WhytyTus7rvyO+4O8wMOBMhYIPBoGaZJNXguqRGuMhAe118XtxdFKzt0tKSlpaWMix3NOPRfepDH/Dd4wrAZqmOOKJvGi2aC8Dh4WEOwLr7ExGPp/m6oCDU9CHloUwIYLqCIYDZbrfzDBVt8L6l/0jw8uAgYwPa9DZGReVo0cfTA7C8k2cjwuM+R0n8IQ8+Rm+amMA73/lO3b59W5/4xCe0s7Ojp59+Wk888YSOjo7yIiKm2BBi17zb29tqtY7n/O/evZsbfenSJV26dElbW1va2trSxsaGrl27pn6/r8uXL2tpaUl7e3t6/vnntb6+rrW1NW1tbenrv/7r1e129alPfao2sA5/PWglqSYErgAIUnoQB/TifzAiK9F4F+gGvxhGcv+zZEGcUXyBDs+7q+JlYCW55gFQCIUAypF0KpnGkYlnxVE3lJNbf6Lr9Cn96c87JEf5gMxQHEzl9nq9rNRQjoxfv9/PbfZVlt4OD8jS3x5L8UQsUB/PeHv5HUWDAnFXEh6Kbpi7nSWY7/fDNyhdvs98TABI74kv3/RN35Rh//7+vp599ln1+311u13dvHlTX/u1X1tLIWWhEHGFw8NDveUtb9HTTz+d5/hfeuklfcM3fINarVa28MQJSDXe29vT6uqqVldX9dRTT+krX/lKDtp4Mo0HmWAQrvuiEvxmn7MH9kfrhkKpqipnPrqVRnAQSul0XMARBgzu8BuGQWjIV8Dy8wzCRuCL8njec/BhUneBpPr8OD49bS25Q1hxV3K01TP3SnEYjyV4sJH3oTiYz4+xFA8UoogRVJ+pWF5ers29O0qURtmJtI3y3QUAUbqFpj2e++AxFx8X+iC6MDy3t7enVquVFSHtaqKZUAJYq5dffjkL1OXLl/Xiiy9qOBzqqaee0tramu7evaunnnoqrzG4deuW3v72t+v27dvqdEZr2a9cuaJ79+7p+vXrWl9f197enq5cuaKUkjY2NvSxj31M3/RN36SbN2/q5s2bunbtmv7YH/tjunPnjn7v935P169f18HBgX73d383ryd3aOdBPzL3CAzBPAR6PALNMx4hBr4RlBwOhzmARUprTPuNwR9nJGk03eUWAiUQoSiCzB9KwefOeYcLj0fInUlpIzA5BjPdPSAgxnWPAbmv7ILvSgE3hOf6/X5OEENIKcdnYAjC0ocIFPVA8Nznpt2MSVVVOajHM3G6GPI4gLtJfo33RATm5MjJn+F+FBLKIyqQJpoJJSBJv/3bv607d+5obW1N8/Pz+vKXv6zl5WW99a1vzQzS6/X08ssvZyZFqx8eHmpubk4rKys5Q3B7ezsv9T08PNTt27e1uLiora0t3bx5U71eTzs7O9rd3dULL7ygd7/73er3+5qfn9fW1lZmNhgR2OmaGBjq/qHDdtY2NAkDU5aS8mKkXq93Kv/ctTiwWFJOQ44EgwA9XdCos5fnzMJ9LhiOOvidNvM77XGoTt/5ykdHMy4MfEYxOfz1+xDi4XCY0YujMupEcJDfHBb7+2iPB13J9OQ+FP38/HyuG25TTCpivB3ye9udovJxwff2QtHqx1iD/8WZjnE0E0pgOBzqtdde0+HhoR5//PHsewFHn3/+eT399NO6cuWKpOM51ZWVFbXb7TzVBwMBdZkpmJub09ramgaDgXZ3d7MSeOWVV7S2tpaZ9Atf+ELOHrxz5452d3f1zd/8zfr0pz9dWyQEzFxYWMjJM9JoybCkLPwwsu8gE60pUBMY7P4niIH/MUBE8CtaTvrUGT4yowufB6ccZruvLo2ClFjZSARLUVSOFig/Ph/vkUYIxWMYjqaWl5ezgMRc+3a7nZcAoyQ8MYjv3o8eTaevvUxcJ+5zRe+JTASAPUofA6I+3cg4RSXk/eJKICIufqOf4DNHcfH9JZoJJdDpdLICWF9f1+uvv541/ac//Wm99NJLkqSNjQ31ej3duHFDq6ur6vf7evXVV2vTWPzvdrvZrx4Oh7p3755efPFFtdvH6xM+9alP6eu//uvzeoOPfOQjeuyxx/IgP//883rsscdqlgSC2ZhyhEnJXdjf389WNyIAhIoEE0k5KBWZwROZYqAIa4w1IUvOd7Shru5LI/TUTarvjhMhvb8vMmrJarnickb1YBr1igztfUU98b+pp6Rs0T0HwNsJImHsYt09cBeVbIzOUw/cgJWVlawsPb7gi70cacQZEy9bGk3vRovv8ZQmt8DdEKZCKdPHteRS1uRvnHCOo5TSdUk/LemapErST1RV9T+klP6BpP9C0s2TW/9edby3wFh64okntLGxoc3NTa2vr+tLX/qSXnnllbxH4HPPPZf3ByCPoN1u686dO1lTY6196u7111/XYDDQSy+9pF6vl1NiX3zxxSwMBP4+/vGP6/HHH9fLL7+slZUV3b17V1tbWxnuogh8SbA0spBYJZ8uQ1gJhLFfHhuU+F4DLuz4vFCMrhOkYhcj4KnvaiPVp+t4zq0x7gHZa0TRPRhI/T1qLtWDUShBj3e4QDlSKgXEHDlRtislRyFMCcfgGc+5hfTyqAv/owvn7oT3H2V71D0KJkiAmR2f8/cAMRRjAa4UHOrTd644vO4IuO/k5P9dcTbR/SCBI0l/t6qq300prUr6eErpV05++6dVVf3jsxT2tre9TXfv3s0Bnm63q3v37uX5+6qqdOfOHW1sbGg4HObkDGnk63ogRzoemO3t7Tz3TDrycDjUlStX1Ov1sja9dOmSbt26lRcg7e3taXNzM8M731eAvHrfFERSjuYjMNSNz/iaJK/AJFVVZcQCs7j7EIUWRefTczA88JXfPJLtzINvTRIVMzMeA3Hh43nm8PnuCMYVUEkJuA+MNXVXxYXG6+tCQKDVlQfvwmo3CQ3k76HP+PNyPJjIM/1+/1SZWFtJOW7gytr5MiqQ2O4I3b0PXaGX+s5nFlz5OcIp0bmVQFVVr0p69eTzdkrpszreavzMRCcsLS1pa2tL0nHlL126pP39/RrUarVaWllZqQVp8Pn4Q1CZXtvZ2ckQnHncw8PDvMcfsLqqqqx4EEY6lk52q15Vx8lLkWnw8dnzbWtrS7u7u9kFcEZA6Dx9FsSA8iMjDcvv2Y0+x+yw3OvtVpfPWGZQFATTIHwINgKIT06gFqHE2lE2zOj1oX/oaw+i+QIqYiWeEelZf76RqysJXDQXAvfzPS7g7XVF7W6bCx0zQQRt4Y/V1dU8E0Q2Kf3mswIonAj7Xcm7+8AYRCTnbkvJjXHl4+156FOEKaVnJL1X0n+Q9Kck/XBK6QckfUzHaOHuuOer6nhNfbvd1pUrV3R4eKh79+5lC+yBMuCgQ0yEHaElnfjevXs5iOdCSDah55FTD+k4sBeDMrghaH0UTavVqvn3nuFHgAkUQuAGS+/WEqZyqxOj5I4UnLHpB2c4h4UIGkxIvzrcjs/xTLSqvMt9VVcyJavDWHm/eL9H4Yx+uCv6VquVDYWnxkrKipd+pyzaFsfZobe7SZArLA+QgkIYV+rPdRCbv8v7N16Lf/4c5DEpR5DuBnm/eoCY35vovpVASmlF0i9I+jtVVW2llH5c0j/ScZzgH0n6MUl/vfBcPnfgsccey/EAOpzdhfkj6CWNAkMeHEEYmBbE6rMKcG9vL+cMYMFj1p4PgGtih94wOjsF4QKggHzxj+89H+dvPW7gLgBWs91uZ4aGUff399Xv9/OOtvzFqao4BRcTeppSimm7pJqgu2JwgYqwP/7un7Hy/g5nXPxat/6UQZvw21mtB7pAufqUIErDpy0py2MKLmxY4ihQKKLoarC9nOc3wJv0r7sZfKd9rrQjIvD3Oy/6PdTLlYLHGqDoYkS6LyWQUprTsQL4maqqPnzywhv2+z+T9C9Lz1Z27sA73vGOiu2x3/nOd+rmzZv6/Oc/r8PDQz355JN64YUX8i69w+EwrysAcrVarTxlxyA89thjOjo60ic/+Ultbm7q8PBQ169f19LSkvb39/XMM89oOBzqox/9aC1vPkZxcVM8gYZ3Ya0lnVIUvoWWQ0qse/RzpVEEHUXiTISQsPow+qHAf7feznwwQxR+G6taYA7GdmsffWlnXsp2RekKJE6NSSPk4NNu9JuknIQlHbsArOpkCbXHgXxnH9/FyMeRsfCtuX0GxLMyvc7RZaDujgCII3kMZW5urpbGyz3enxG9eZ/HwGEU+NI1k69T72mi+5kdSJJ+UtJnq6r6J3b9iZN4gST9JUnPTSrr6Oh4Df+dO3f0yiuv6Mtf/rJ2d3e1sbGR9/9bXl7OG4/CbBHCARVv3bqlr/3ar83Zft1uN68T2NjY0CuvvKKdnR195jOfqW1g4R3m0Wymhba3t9Vut/OW1r5JpUfGSYOODObW3mG1VLeOEAyTB+vE+i0uLmp5eTnvU4CFREFFn9dhKK6Auy3el44ebExPMZELWPzjvf5MLJMyoktBLobPvvAu3DfOnSDo6daevqYPPFbk+wbyTres8X2Sako1Bum8zYeHh3nPC1dQsY9Bn9Fax74s9a0r3TgjEp/xMv1/ie4HCfwpSX9F0qdTSp88ufb3JH1/Suk9OnYHnpf0tyYVRGCs3+/r+vXrWl1d1R/8wR9ki3P16lUtLi7mxT3D4TBDY/YBoEPcj75165ZSOj4Bho1GmW1YXV3Vn/yTf1If+9jHcuDQhaDVGq0ULCEO5qidCfET+XONHqf3EGZgHfcBMx0ORrhIvRYXF2suBWV40Mwhu7s2IAfaCfrgvdGqRCHxuIa3kT6MPnhJydEXzsRk+jGGxFLcPeI4MvIxPHDsc/+OwBDcGG9yl8rb4q5CtNTuJqFMKdszCkuGCnTnfR2FPML3+H7v04jm3J2Ylu5nduD/lFRSL1OdNeCU0vGhHF/+8pf13HPP6dlnn9UzzzyjwWCgW7du6cknn1Sn09Ha2ppu3bqV9xbA3+71ehlSsjpwZ2dHt2/f1ubmplqtlq5du6a3v/3tWl5e1ic+8Qk999xzeuc736n19XWllGrHmbl/7/O+ROgl1ayQ+6a+UahDbBgGK+w+rUM7t45MZ8JQKIidnR31er3a3n4eE/BDNHzXX2diX6UXA1lYtij8CJIzabSQ/i4PasbYiwsBAoky9GlKdyOA1J5dWVJOft1TmplRYXu0mHsR6+1K2/vOg5ERPbly9ClT2u07SZUUNQrF/7yffQyoG3wb0Zv390OLCTwoYjDW1tZ0dHSUV/XB2F/5yle0sLCgt7zlLbp3757W19czIzBlw6nGxANeeeUVHRwc6JlnnskrDF977TVdvXpVy8vLWlhY0JUrV/T888+fisyjAMhF6PV6qqpKq6uredszUpIRHl/tJ9UHwS2GD7grHU/m4B63agy2b5KR0mhPAGAocQryG6hDhPtSeZcaqGSNoqJCIEgV9mAYZfA/wt8myArURYik+gElrqQQJGItJH0hzPzGdxKhfJbFA5LkSqQ0WkTkFpix9vl47w/faDTygiMHD+iNs/IlNOLjA6/ggjri4PkSsog0E0qg3W5n+L+8vKwbN45ji5ubm5nZer2ePv/5z2tu7vjUoNXV1Xz8V0opbxd+eHio3//938/KYXt7W5Ky4Ozs7OS59q985St5OycCf1hQGJBdgHyZc1VVWcvDgAwsLgGWVKpnnXl2oS/Cib400J0MQxfCpaUlra2t1easY9CRE3CpI9FzmMKtEJbXM91cybk1cmXkdWUcpfJW2CVG9NRlBMTL8UVYccqt2+3m+XmCi/T1zs6O1tfX1el08mEtDp2pP3P/KY22kvf8CTJDHVUQs0DoHfazQS7Kh63p/DRkr6cjIerlyjEG/njO8zGcrzAMkacmuQYzoQSqapQnMByOloayE49D7cPDw7wdOM+y2QinFPv88s7OTs1HQmA5XyBaJLfUlM/W35TB6j/eAzLAMnlZ0Uf2tGYG2YOKLux7e3s5mBX9UXxnmNwFGAvgU2HUyacLOU/BoXdK6dS22fSDf46WyYk6lFBGZFLv7+jP+iwEigDhk1RDRR538fMVgd/D4TArDKYS3eVx9OBW3MfME6dcuZViBXE6098ZEQDjEn152t3k55eEPfJQvLdEM6EEILdmrdZo9x007dzcXLbmnBNAhhiogNN16Izo70mj/eKYbvTgkU+hYZ1YLUjwECXAfgHuWzpDYKVhVoSNcnmfNNrYwlfFoRg8Gcj9fqZVUYR8htH4HgUYJDA3d3w2Iv1BfVyA3ZfnuzOmK0tphGScGb3/XTnyPvqAa4wfLoFbT9wxLN/+/n5e5ekuBH3v6Ic27e3t1VK9pXqGHeNJWxcXF7M19/hHRDHUE6J+KH0CzNSVZ70/Ir+6oogBS+6jXtGNcKUw80oASBr9VUk1ZiYo5CcHLS4uZmvg8+10QIRfUv0QSun0QhO3TCgAhJ9nYuIPz/L+OFPgFiMqJKyYL4Zh0MmCI8AH6iBNFbhJ7oSn13qwUFLeMAS/16FsDES5MPt4ICTRP3XGc4F3QRlnAf0eZ3YPWkrK6cqScgwpRtwHg4G2t7dzPUEDvJepV/qcfnC3BkVQstwgA97nQuztYLxIXIuIJyon72fnJ8hnZVw+4ucYT/C6lWgmlIA0WoThlsYDTM4UwD2so2tu14gOo9yauQD4fCv1QKvjW1MPdqThFGLgGmW6ZUwpZQajPIJ4KKnt7e1aQAtGRdngo0aI7EpxcXFRKysrtZgG7osfwOFK1P31wWCQrS3ZlQSvoAhDS1CfMaKO0V0ooYf4fLTAUUD4DT7gHArqi/Jw14zYCKs2UXqOtmi/54u4EkShu3B7/oAr0ugGwgcsEKM+Jf5z9ORKkzrEqeZITRb/TYEEIjwuWQ+UAtadTvfMPJ5xS0KHehCKTvWIvENSBM23P4fcZ2QgvR1eT2emGOHHTYgRand/YADfzw9o2ukcb6dGPSk3pZTPPaQtMWvR613qN77HMYo+qt8XFSx//ObK0e/3fuWvhCA8k5B3oASrqsr5BPACwd+VlZWau4aCATG5C+K/+3uoH3Eg+CSlUY6IB0F93GmrpzFjFByJ8pwrhOgelWZYopHzOpT+l2imlIBDcYSdxkdh898inHLUEKGcNOp0F1bKB346gxFgiyflYEWdGASvlwftiA+Qbkx7gKAO+ZjSom7smLS8vJwVAkwQg5LSKJXW+8MJZRNXTDYxmwsyEBxlHJVdtD5R6KOVjwrfx53+iQoCHiE+QL8jbH4WQKvVypF7jzvBR65gIgLlOoaDJCU3SI6eYhyEa0zdcppR7KcoyFEJRD72+nq9J1n+SDOhBJqCV26x8Qs9YObQ0C2nC3b0kyAYnqw/Z2KYgxWKrEkg8MgAHh4e5uw2F34YzINetAMr5FtrA9095ZfBx/LjBvkpxaXotqRTqEIaMRTtipCUPu50OnmjTsaC+rgS8PgH5TvaKQl9abzdUpWQC9d8LYPDdt5NX6McSYba2dnR9vZ2DgDS36A8pmF9uhBFT10jMmi3R+cz+swNytCNUwz4wT9u6b0/MBaxb1wJxL51eYCaEFeJZkIJMPAwKceASfX5e2mUTcZnoBaNdcGLjBYVCx3L4AO5SSYaDI6PJyO44+8EKZQQAoxG5NqtjFQ/wgrfFD+eNjiTEshDgTDT4EIRUQ51gyGBz0SoneFRDEDkGCxEoSL4BCn9Hs/f53q0ahEi+5hEVOazB66sPACI4HnsBWtfVcdTrOw+TLsYu1arlcvDMseFR4yRH0qCYfG+7/f7OUC7sLCQlQ57HaaUMvojdwH0EK06fcV3jwNERRpnC2ifI6mSGxFpJpSAdHoxhwfdStosugFuSUqWyGEm32MgC4Hz/ek8EcgFgf8oB383jMnAw8QMumcZkmnn6MG3CZNU812Brp5TD2z2XAa3GB5h92CYT2M6Q0X3izGIiMoFwmMAbrH8fofzJVeB6zGe4+XEegLzebbdbuct4bx/PRDqltoFkVV+LDePlhp0FuuEewCi8iSrfr9/atdnBBtlHHm2qW9in3rMxesYZcb5vkQzowTcUpR8w5KfFa/7oEXy5yKscr+TKTQsg0/v+aIblBSC6HUFaYBmPBiEH4nAe6DO/9MnMLP72rzHzzhwJqCdMCKCQtnsJdhqjXY7HtfPznge33Al4NbbmZGy3PLHsqGIDqTRGg2ug3Tcavu7hsPjwzd8bwIUYEwPd76DD7gWlRB97BvauJLu9Xo5uWtjYyOflA1q80Ag7WBmAz5A8Xg/+P9oyJrQVOnecTGCmVECUImZvSEwW/SNonA2aVbK8N9hZuAkTOOa3xnd/Txf1uqDwIASOOSdbpWBrp7uiWKBebyOCC87GmH5fYmy/9GPLIRCES0vL+ft2eJCFlfEcVzi+Ej1M/SiRSspFB+PiC7imDmK4D08x7Qd/cdmKxxAgrKILogHA33GhHczLez1YnwJBKMAsOLSKOaTUqrBfX6Lfcj9MQvRhXeSLJSUrfdRk7KNNBNKIHaOQ2OpzmiuVR0eRkgamVU6jQYcOuMXxxx1LzdGc/EDcQk8SOTRYq7RNkcCPBOn6hxJ8E4EnUzJu3fvajgc5qXQ5CGwIs+hK+X4lKJPrbrw0++xv5xif7uwSfVprxL897HzPy8L5BHrEgV3MBjk3H8PutJ2z3mIdfbcBB9zxpqx8A1WvQxvD/9Z7t7tdvN+lWxZF9GFIxP4xPmXz9S1RE3GrjRuJZoJJSCdDpp5ANA7PGZd8btrd58C9M6NnexlurX0aLBn15UCW/jkHvyL73Oo75F5Vify58jB/X0Ck+xUvLe3l3fHqarR2XtxlgSBdl/VhcuDmbTJ0VRpfFxRRMTh5NdKSgDBcwH0eyIs9rJccLmOQqNv3XXhOXfBomtYQjKuEB2VUb4HZCHiANLoyPLIM44evB2xj2JdYpzMxzkqghJSaKKZUAI00BuNtUwpZdjswhmtGw12JeCBKhdMSafeBdSW6sduoQT8z5mzBKX5DSaKCIM/h5PAe3ITpJEPi68J4x0cHGh7ezufzMx7UJzUiyAn/eJ9hcXk/TEYFy2JK1xgc5yCje5ZRAHRTfCxjPd5X/vMgaRTu0EdHBzUDiFh4xjcHcbGE8Tcp6ddLujwgSt72ku9fYaJ35lhOjo6yitYl5aWartju8Lx/Qdor/Nt7M+mmIH3k99fQgmRHsRGo89L2pY0kHRUVdX7U0obkv53Sc/oeHeh760m7DgMOeR3JorpvdJpv0eq70DrTB3hqneQZwcCw0EXUZE4NI9TNNEnRwC9HFADyUDS6BzCWDa7JcdsMyzC1tZWXlSFsiEAiHvjuxB733gswPsTJeoCElEXSqAk6Nxfii2MUxZ8Lt0X3QCguU8F8jccDrW+vp4XkkU3i7r7e+ABHyvGKc6E+DZmLnwudPQ5sQGUsaMr7vcpzthfkVdLRsjr4tOn/qzfW6IHhQT+b1VV3bLvH5T0q1VV/WhK6YMn3/+rpocZSEm1LbPQnPh8VVXVps5cmyJ4MDrMHC10nI8GAZBg4prZN6REQxOMi7kGXicGnEF2y+UWdzgc1lYp9vt9bW1t1dJfI+SGGbFy9Bt1pQ7859lOp5NP0SV6HqGmK0+fAYg+cPzMc5BPh0VhiZYKd8R/d1/dV0f2+/2cy0CZtAO+oK9dYJ0YAwwLyWLuXhFTAQlGxEN/+Uai/BFvQSkfHBxoc3NTq6urOYXZlTZIBsTrfesUUVJEBBFBcJ+jmSZ6WO7AByR928nnn5L06xqjBKTRph9YvcXFRUn14A5MA3mHuIWKq8pKVgr4RqTck1HcYrqAOzx1gfTfIPfXPUXYYxp+D4ktPiXp/iKW3RNxJNVg8dLSUp4bl+rr8XkvyILfPXZRgp+R+eJvTW4Dz7gCiErAfy9ZVu8nUm6x8D4Vy3ee8WXRnoTmwhr7kbGPy38dHaB8/V2+jsUDsS58GLFWq1U7SRrXwWcRoksSyXna/5d+87F62EigkvRvU0qVpP+lOt5K/Fo12nH4NR2fVxgbk88duHbtWm2Q3YqTOINW9yCWz7HTWDqVzvCB5nm0MMt0CcZ5lqKndsIA0cXwnHWHc9zLpiPc664Nls7z0bHuPv3kigimjEoxLmmmjj5bgaJjNR0MHKF7iYEiRD3r9SjkpftQVm6pES52V2IKkHtcCfjYgwLcaKAQ6K840xNnmhgzlJLP5sS2uhvggu/uFoqX05u4RxrlMQyHp5O0SkJcUgSl3+L1JnoQSuBPV1X1ckrpLZJ+JaX0B/5jVVXViYJQuJ7PHfi6r/u66uRabYDpRDb3LA1Y9JUiPC3N3eM7+xpvzwoElrkQxekkt1KljqctXi5M7ZaNdkqnLTOpyz4FClEfZxrq4huQ0h5gKsrGs+zo26gASoFUv8/7NZbjbY0UUZT3F/+5D3QYhcqVipcJWnKjQXku5N6nXqb3JVbfYT7vQUG7IONO+nUPQA6HQ/V6vbxlfQkdlQS+JOz+e+lzLHMc3bcSqKrq5ZP/r6eU/rmkb5F0I52cP5BSekLS69OUFbUtHbm/v1/byGGcJfKgD/8js3jQzKGbxxVKFisiCvf/fZBg5LjMF6vlGW0xGQk4z3oC3Am3ctx7cHBQ3ELMg1wkF3U6nQyluc//oi/q16MVd380ktevyVJ5X8ZIeBR0F37iAyAZ7otZl6AgTxH2WYE4gxTfnVKqKV9Pq/bdoj046VParvydV9rttvr9fo4/laYkY19NsuhRcUe+LSmWSPd7AlFXUqs6PpC0K+k/kfQPJf2SpB+U9KMn///FNOX5NA0VJyWTE38cqnkgiU5whnY4KI0sFgPmfiED6Qzu03ouaD6o0SpKoxkAX2HI/D5CDlP7tJFb03b7+JATNjCJFqWqRpuEYC19Wau3lc03OObd8xb4iy4P7WxSgN7ffHZB9MSWqIj5TF97YNBhPoey+m5JEUVExSuNUorjzsPt9ujcCQTc6yipxhfRUkdU6agLdFGqXzQkMZXYXaDY302WvIQ+/dn43nFo4H6RwDVJ//zkBR1J/1tVVf86pfRRST+fUvobkv5Q0vdOKsgZiIFw3zYyoTfYmSlatJIS8EFiek7SqQDN/Px8npUowbaSVXRm8ngA1tl/9/x92gVCWVlZyVurs0BFGgUCqQcKB4hPm7GOMOrOzk4+IQcr5ErA+8TjCV5nt+rutvDdx9D7I/ZfdB9cSfh4MlvC1F8cX/rW3+1BVnLz6TeQUdwyzAXYc/k90OeHj6Lgydx0V5H2x8xTxoP3c3COxwei69SEdr0Pm5CWf36o7kBVVV+W9O7C9duSvv2MZRU1LoPL/DDC5FNyDGKErA4PY3k+mxBjC9JIwHxgnHGceV0w3IL4WQRYoZTqEWbcHxTd/Px8PnbNpw/JGnRmxOWg/oeHhzkLEaQB05Fa7O1wIXBl5mjM+9JRV8lvjVbMrTufHcL7u+Lnw8Pjwz6ZMfHgHM8S26FPU0r5u/ODu35uLNyYuPA6gvRg63A4rG1k64FrFBD94KsTo6GCLxyRuuvjihO+alIGHvNww+nIYpz7Js1IxqAzi0NCOg1G39vbqyW/lAI7rgRcQLyjHP5K9aOhnNE8DyAqgBLccwZHWH1LbIQeq+HWR6onSlXV8R6EXqa7FO7CoMjcLSD/gW3J8aNLhELzyLhbeL/H0UEJksYZiqgIKIt7ojvBOHP4KDkgCAkCT/9hDLjmLhH9AuryLcB9zEBqbmw8pkCfuJAR26EMks3ckDnkp688ZRk+8NkBVwKR96LVh+gfH0eXg3EKQJpBJRD963gfjI6Ql6ZUvFNcqzOQDlm9kyL8jVQSfL4zwNTPz0nwKbwYmCsFKIGfjnhou/u6TG8Oh8N8PwzNVmQwtdfTLbV02mVy1BSVgDNX7Ad/3oU+/sbYRQRC/7ASEJfE+wD04/3NGMfEmJKxYOaANhPoc94CaTny8bGGf4hXScr5JiQQRUuOAnFjI6mmzCKvlgzMtNSEzko0E0pAGnWGdwL/vQHAbBjdYVFJMJ0RJNW0MxQZzZVSaTDinz+DBfEkFq8/73brT138EBPSiqkX9fapTQR+MBhtToFVIkONdQe0s9TH0a2JDNNkuUsMWoKtcYaBdjvj85u33zcFQYlGxeGWGoVHmT5NiNKQjk+ZZsxBgf1+PwcMPQAb2xlnAXARQB1+mrLf48qI+kZeJrYQ0dgk4xTprMpjJpSAW45olb1j3H/3QEzJMkORgRlEH4hYDxdeBrxJAfBcRDDU3Z+PdYvCyWeP7jv8lEZrIwgYOgJACNrtds5MwyXxtjiTuY9fcq0irPf6RjTgsNfb7/0SIa6kmuXEEu/t7eXDXVIaJVi5BcV9INaCInSkyDs91Rjh5/6jo6OsYOG3mHlZihl4n4HSQANxxaaXQx+h2FBWKLJxLuckOYr8J9WRXolmQglIOqUAHK77XDqE1fUod7RkjhIYMB9QOp17XQHwnLsbcRBceUUl5v69a/GIHGLgKQbq8D13d3e1u7tbY3B+d4vmwrm9va2dnZ2cT8BzJNS4kvV6u5Ut+ailvnAlQDmMU/RZfYziO4h9AMkjdMYV8pwL5t1pk68Z8D5BWYAgeRftB3m5O0BbXGH6uHEd9Le1tZUPh/Wdon0WhrY4D3icyBW2K9RJwuxjAT9PgyJmSglIpyPwbr0i0YHO0FEbOpUgWUmweZ8PWpN/GwOapVkK9/fdVWDxjw+av4dTdLD4vgjJkRDlLi4uam1tTd1uV4PBQJubm6c2SC1B/iiIJXQUFXQTKmrqe4e27svH+6Pg+UyOjzcWHxfK4wxxDT9Iyff+I0joU67U0V01V4Duknj/uPX2tQdMM8e8AQySo4I4TdyEBM7rEoxDETOnBKAosM6Mbk2j8ojQOlLsFB9op8iAXs9oxZr+4n1AxibmdYEHvvIMaAghghlJSDo4ONDi4qKuXbumS5cuZR/XrQjKwyPu3gb/Xtr/wPvPFS/Xvb3xj3tw6ZrGxS0o7QKyYyFRIH7IJyiBfvbdmr1cSdrd3a31qaMqxgP3inIJ9Lr/jjKhLr7wDPQFSnHkSlvdDaB9HlOYBP8juQL1Pp1U1kwogaqqsoaO0zVEziNCQNhJqWVgnPH83jgrQIf5smPIUYArlMjkHpGOkBptjmXw+1AAkENg1hNIygzELrjEEAhGpZS0srKShQYU0O/3dffu3ZoLEKEo70QROXSNbkKEwZHJIvSk/wjyOSLa29s7tSaDKU+2SKN98XRgxtEPKeWkYfp9ODzeh2F9fb0mUPjp7u8j9A71XUmiQKgDfIoS54CTlFIeT5QXa0PoU8bR+9eNjOeJeKzLZaT02Q1ivN+vvyncAWcKOgeGcwTQBG+iD+fWZhI0ciF2YSlZrGg947p8t5A+pUf5cc2A/87zMCJtgfmok6OdXq+ntbU1PfHEE9rY2FCv18vnNqysrNRcH4+FROZxQeC3OE1W6gdvexOjllwnR1j+LHP67N3o48KcOicwISySakrD24ywM/1Hn4IAIiz3MyxoO1ae9GtfJOS5JLgoKF3Gm2AtSsAVEPWnfnF2gf6ZhsbdN/NIQDq9EYZnqMH4kAtz9Bc9/z/6svGP3xgUaTRv7AMVqeQOuAKgTBjE4bgrNDL8er2eUkq1lF4UiOcX+Hul0d6IV65c0bVr15RS0ubmZg6WORT2QKXP0bvA0E+8Iwp6jCnE+nifelDPg14+1tFaUTaZgEtLS7ndjAnz/ChClAYCjKLA8oLEfL0AAcJSwM/n+n2BEO9lPP2gFcbe60iZvrcBZWA4PEeEe7nfDZkbRe/vyNdu9eP/cTRTSsAhO0ogWu4mq+5WwX28pjlayvJNPKKPy/+SQPBbTGRhmqvf7+dDLBxZcB/548B895dBBW7FUFKUf3h4qMXFRb31rW/V448/rr29Pd2+fTvHBhAKdwX8c4TuJUUAuWC71eO36CrFMWUspJHQOEPzPhdGfG9vuwcEgdA+uwMf+Bii8Jh6jIjH2+LvoG6uvFJKWlxczCdUMZbeVsrw/vITrNfX12vojs+uNJzvohvcRFFGoiIYRzOjBJxJm6yT/+d3Z5zSVJd3okNhCNjtud4MrKcdR2TBO/Dh3Of3gBLle1AOy8T8PYEjfHQXWpjQk6mwaJcvX9bq6qr29/f1h3/4h7p586ZWV1dr+fRugdwHpW+8v0t97G1GoRKrmTSeJYvPu3yBEn8oQT574CzWyxW8W0KuHRwcaHd3V5JyqjV12dzcrI0h6M+tNGViUOgH71PvF8Z8aWlJUn3DkJRGgb+VlZXcNtrHePp4R+H1dkele143AJoZJeBRcidvZHQDnDncR/M52ZLPG8v330rMKp0+7cg7l3s9ag/sdusf4bQ0yiAk0Se6FlhND14SkHrmmWe0t7enV155RTdv3qw9X4L8tIM2lNribYSiZSnByyjQ3nZHaR4LiIHbGBdgHNwaS8oIy9+NQgbqewYgiAHovbm5WasnO0xFJOKopYQsebcjOFfS/Dlfg+B870SfpQAlxkB3dBFK/d90/U3jDsSEH6meQCOdZjRHC3QWSsDhWXQd/P64KQf3S/WkkBLTSaNTZFEYrggo2xekwOA+x+++qr/X309b5+fndenSJV26dEntdlt3797V66+/rsPDw+wrY5VgYN+ItQQro2LyOkQlDFPHZ2PfgESoeyzH028jSnP/GyFxy+cbhHAtpZSFmcAorgGChRvhboGjOGIMHlNyhIFS8nZ4opZvDxeNFPzIu7w9LuidzmgvRIKEtLc0Vo6CXOCjOzGOZkYJRFjnELwkzCXm434EMa4Wg7x8lukCT+OqvFIcwOvp78aaw2zUhxiBR65hHt8dF2vEenjucQu5srKixx9/XFeuXNFnP/tZbW9vZ3+XOuzt7anT6WSlIh0LNRDUt8128vaUFKNbtmgNYx9H5eyCQ1uiy1YSGhSlM/nGxkZeHu0BWOrsCpeVfSxN9v0F6ReUtCsH3u1pwMQoXDG5O+XuAklCKAf6zfnTFYEre589oo68IwbJI/97v8drTXRuJZBS+jodny0AvV3SfyPpkqT/QtLNk+t/r6qqXx5XVlVV6na7NcsRhfjknTXmk+r+exwIP9zDO8WtnAevKMthXXy//6fcCLuPjo60u7ubyyHw12q1ssKJbspgMFCv16sFjQ4PD7WxsaHB4HhXoEuXLultb3ub9vf39ZnPfEavv/56dgF8JZyk2vZVpblvZ3QXPPIHsHJNCVMo5qbxHOc++TWECcXp2XfRHfI+pM0rKytaXFzU/v5+3jkJBUp/MK4rKyuZP1ywHMHx/pWVFUnHU7Bzc3NaXl7Oitr5p6qONz9B8FdWVvJvvmks7iEpxfA6sRoUEQrH3SPPV3C3kL52hBLHaRo6txKoqupzkt5zMqBtSS9L+ueS/pqkf1pV1T8+a5lNVnbc/6bn3DdruvekHZJOT1s1+Xfc40rErb3PbYMKPGJdVVXeVZkglA/s3Nycut2uut2u2u3j3YE7nY7e+ta3am1tTZubm3rttdd0+/btfNyVK5PIdJ58VYqLuAVyyx7hbFTEJXTUFHfx++iDJvTgikFSDQ0wHnF/fhQrU67dbrdWtucJxNWdCKvnB7Tb7YymfL6fcYyZgL4zFcgV5VXqY1cQfr2p7xwxxP4cd60kRyV6UO7At0v6UlVVfzgJejTROKjv95SeiQTDsDqsiZGl+pFjfPcEIEcDHjhyBIIPxzyvW0p3NWAm32+AMmCquLmFJF26dElvectbJEmvv/667t69q/39/dqOwe62uC/vFr008+IIyNvpgUnv44gMSi4A5TZRHOuI1rjHU5c9RuJZgu4yxdRdbyN9yXdHCTHgF4OU1GMwGGh3dzdb/Hg0PEqCYCPXHGm5kvNcFF9oFuMEvDv6+CUlUOrrSePxoJTA90n6Wfv+wymlH5D0MUl/t5riCLKSgJeuc63kJjiVIGl8XlINLTj0hKJF5HlXCHE+P2p+FIsH6Tg9GOi/srKSoeTdu3e1ubmpxcVFPfnkk3rLW96iXq+nO3fu6N69ezU/k/pRtie4+BoBmDtCbe8vrsVYivczDOupvyVFRJmRmb2cpnd44NH9bu5zq42vzlZqxABiBii/kVnI7AFC6grYp2h9/4eUUp5uZNzJAvT2OkqMsQ03KiBIym+36yc3RV6eBim72zstPYizCOcl/T8k/cjJpR+X9I8kVSf/f0zSXy88lw8feeyxx06VGxmnxEhNCgJyOB9/KymZ2HExEu4WE+Ei19+jvggkZVA2jMH0j+81SEyAdywuLurpp5/OfXPjxg3dvHmzpnBcEdA/KBeYMzJzqZ3Ui/rCRE1KICoK7x93r6JSmIa8b+PmIpS3uLhYC9z6tCjLhHHDvM6Mpwf7iKlEC+wBRjZx9f0e2cux2+1mBOLRfxYxLSws1OIzXldptKdEt9vNu0TFfiv1P9+b3IGoFMbRg0AC3ynpd6uqunHy0htWoX8m6V+WHqrs8JG3v/3tVcliTLLiJQaNz0xixGjRpdHUk5fpfhn1dNgNc/m7cQ1cAN1a8/7h8PhwUZJNrl69qo2NDT322GO6d++ebt68qV6vlxOTYGaSYWCupmkpt6QuqJ5H0USxvHg99rMrymjN4lg6ef96lJ+FVXHLL58GJQ6ytramqqry/b4aM6LCePbk/v5+3q8hUrvdzmOJkmGBEH1CmrZvXQZf+GxMFG7ayhSvK+HoHvE3zh2bJm4Q6UEoge+XuQLp5NCRk69/SdJz0xTiWpjvJ+UVGbVJMUSL5mWV3ge0dTfAI9+RqZ2hImP5FBoBopi4xDOeBgxqWFxc1KVLl3TlyhVdvnxZ29vbunPnTu28AM81cL/Sfc/9/X31er1srUEC3k+gCfrKLRV1jFOzse+8r0v3+b2l50uf6RNvF4rTfWlXOKAd0qV3dnZym0nL5nliLY4OYlIQ9zsS5LmYGsyqSOpCUNJnp7wvUfqscWB2gj0ISmig5DZxPVITz49T9A/i8JHvkPS37PJ/n1J6j47dgefDbxNpkhsQ3l9UBiUL5fdEH0tSzcrEAGBJ2wJRsTTsd+/X8TUd0sbEFxiu2+3q6tWrtSSgV155JUejDw8P8x78MKcvO4bxEBI2LJFUS8xxhvfpOBdAGLoJknr9SyghKvRx41sif64UxIzW0qcXfU6eax4A9IArdVlcXMx7E7RarYywULaeAo3wooR9+3DcPD9C3vuBcWAHakdp1DMeD0efRzcg9pN/b+r3Jrrfcwd2JV0J1/7KOco5JYBQU6JQk/BTHkKB5pVUO3gU39L9creWMWJOHZ2peO7g4KB2WKY0mnWAWdhB160LDHvp0iVdvXpVa2tr2t/f16uvvqp79+7lJbNYORjUrZv3A0wOCmF+GwvJFtkwqSMBmNZdHLfiMQAYpxwjnPdnHLaX3Afe4W2hj7HoCHjcKMSXZnPP7u5uDvo5/xAcXFpaqiXkgNykEaxHobMDEdOCTB2SnUn/MWvR6XT02GOPaXl5OU89UgdP+/aFTe6usaeCx6Ji8locpyhHUYm64SnRzGQMjqOS5ShZKf8eFYIjAEk17euzAd6RMJpD+Mh0WJZSFhi/kdWHpfb7L1++rCeeeELz8/O6deuWXn755QzlYSSEvd1u56AYDOwxCZQFDOa7FXvfOKSO1sqDjJMs9lnHq8SI/h5HZf6bB/zcwnrZw+Ewb8qyublZUwL8zswByijuRVhVo2AkQT2svbseLvwx2Mg+gymlrIAJ4vp5mqVgdcklji4B1/3Z6DqPG4cSzaQSmAYullyGEtTEmngwybVu3PkmMiCfHQX4VKDnscdFMww+DIJCkKS1tTVtbGxkRnvttdf0wgsv6MaNG2q1WjlZyHec8ag421d5euzCwkJWAkBWt4RYl6isokUu+fixj8f5nuOsjr+zhOb4jK8ujXYFkuqnPDnyIUtzZ2enlojlCoLgKunZbqGjC0R/tlqtUxufMtZu0UE8VVVpa2srj9nCwkLml1Iau8N8jwW44eK+6MY2KdVprjnNjBIYZyma7vd7mpjKO9SnDKM1dItYQgGeVEL5Pn8MRPRDRxBODhU9OjrS8vKyut2u1tfXtbKyotu3b+uFF17Q7du38woz0oQ9E406MbeMcnGmdMvkQSmprqR844qIklwBOIPS5+PGg7LcBYuIIjK5KyQfu4hQiNB7MpdvukK03rdhQ3mgjPkNNED/8Tt9TT/EQK+303+XlAXdXa+trS2trKycWpHImKGE3LV0HowoIKK0yPfjlO84RTAzSqBEk9wAvzaukdHqAaVjAoZrZL67tUFhSCMNjv/t0Np9N6K/BwcHWllZ0ZUrV9TpdLSzs6PPfe5zunPnju7cuaPBYJDniklkwXI5s/pMADnr/EZ9yBPAB6UuMb7ikXHuK001nmW8ogKIeQxunV0xR6UQvyPoMZvT8+l5X3SVPGfCZxm4n3KJo0CeOu55AJ5DgHLlWXczNjc3dXR0pI2NjawcmHXw54h50N8RscYxc353RPCmdwdK1uAs98dno7ZEICTVPrsQSPXknhgEpOzSMx5X4B5Opnn88cdrLsmtW7f00ksv6c6dO5JU2wWXY60WFhZqB1zCeL4Aqd/v15g25qI7bI5tcEtWQlAlCx7vi33uguWKt8S4/BZ9YFdcEYH5VJorFPqj0+nUNvXwGRBf2efjX1KABOKigDkkd0ElIQgU54lAKHRHas5b7ha4WzEYDGrBRx+bqFTjOESapNBnRglMohJDRuvS9JwHA6uqqkHCkq8l1XcCirECJx9QXyzEOYEE8yTp3r17euWVV3Tjxg1tb29nqy0pT+thuTywFwORvNdnUnzhEALjPm/cZJMyaBN/DlNL7fU+Kv0W73P3y8fOp2H9OtdirMIFnvbiEsQ0YtCXp1D7WHm7Pcff6+GbfriL5xF+5wsUNELsuRwcLLu6unpqy7SoBBgvfo+zZVFJT+MKTKKZUAJRmKOvM+nZ+Dkyd0lJxOBLdBlQAn5/U0ejAKRRLjsWZW9vL68FuHfvnu7evau9vb0MUXlPVVW19QQsEHLGcGWGwLp/S1AQJeB57REyQ3x3pir59E1jUbJGMRbhCUiOQqLi9feVYjB+H4rNt03398Z2EuGPU8LS6Gg3dxd92pT6ML9PUJC2IbT9fj+v/+AelEO/368FJak/ioj3OP9520sK26kkL286d0Cqp/C6VYjTXE5ROGMHOuPBiG7tojLwQcXaRivh7yVQJynvH+dwnZ1/bt++nU8Dmp+fzz4obWYXGRcOkIEvaIk+PD4mkWiPQHtgyxfHICz+W0QIcY7d+9eRQJxVKQmjNJr3d4sXEZqk2thg9Ut9wNFsWF93+9x9wuo6UkNIvQ58x+XgOfqUbcuokwuonxjkc/6uTKTRiVKrq6uZT0AdzuOMsed8eN+jfOLYeD/4eE2imVIC0DgNFn0h/zwtenDCikRGh4lgXGeamBPg03KDwXEe+tbWlnZ2dnTv3j3t7OxkeI6ldibxAXffH+sDSrh8+bKWl5ez4mFVnMcxUAg+fYXgeDtdoPy3GPWm3DgGcZxKLoJDXVdeJQjs5AaAOuFrdzod9fv9WoDNp2rddYiohnfGdrvL4ciLbcoWFha0sLCglZWVmmvmSopEpDjLRB0R5J2dHaVU316eP1+A9CBg/rQ0M0rALbI0WRE0PX+/7/doNIMYISrIAEGSlOepUQB37typzVmX/Lfo6/o2VwQVYVp3DXybMEcODrmbMi2hqByi1S/5nPRHjEeM6/foZri18lmV0jhyD4KBYuScBj8jwv1131nY6+AW1vcA8P/cQx0oG/fBzwrwqUV4Iy4iox6OUPb39zU/P6/l5eVa8hPvcKXlCq3Uz9PIyySaCSUQhfosDXJB4vtZnncLH3ediQIQIXRKKaeV3rt3T5ubmzmItL29rV6vV0vNPTo6ygtOXJEQA2BXIn7rdDrqdrv5qDHKwTK5Dxsj5vHPrXyErZGR/P5IJaXS5KOWnqMNCJzfG+uLkkIogMaOlOg/jjJHiXK/uxkgCp9ujOsIsNokeCG4nj6M60XwNyoSnpVUWwbdao02k/X6O5JjHL0fm3g6IuLz0kwoAamOBMZBoaZr3llngVBu4d2aSKd3wfWOhjHu3r2bN/zY3t7OFoYtxBho3kOUObYHa87pO5KysLNFNcjDZwGoe2lTDIeaPjcd/f/oWjkCKvVX9Pu5XhqjOB7ely78XhfP4mQcIOoPVPeTgKqqym5SzAWgj1EExAsYL+5lfz8UgSdYoXy4lzFBUfF+j8E4MWPgB9IuLy/nmQZHTLS7xHve3yUFflaaKSUw7T1NTBa14jTaEUGKSSoMgOfqo+0JSvV6PW1vb+fPBJ4YYJ+Ooj4gD+AtFtH9d2A+zMU++6urq1paWsoM71DaA2fxRF5vk8clqEdUwDHnwYWJ9zQJd0mJRIXhz8TpwyZj4PP8Dq9dAKn38vLyqSO9iNWAzOL740yEJzGhjIfDYe0sg6qqausBqCvPlnITPOCKYllcXKy1I6K2Ur/FsTmr8XOaGSUAnbVB0R04K/l8rUNuD1odHBxkaA+k397ezkt5/YQbt/Zxnpqy8UuBlqwB8MQW6uNLkH2OOqWUNyLxJBcYz6cHqZcrOydXEiUU4MLsSiDCUf/uiiKOUYmJfTx9Wi66M46WeI64C4K3uLiYLTJ1xt3yPAPP8sPK+8wESIN7cdt8XPmdMXU3kfLb7XaNJ4jvEB/gZGNXoP7XFA8o9eV5aOaUQMkquPZ28o5xyOhW1BnaGcmtiA882p8lqfjw7kPif/pec9yD9Xeo7pYmui3UFRjsSS6XLl3KlgYBZeGQZ5MBV2FED2pK9R17YVSfPoyR+0m+vgtlCZ2VpgjjtSamjcqIMfQMPlZYdjqdnCvAtCtj4LMfEIe+xP0CCC5i3b1fmcvHosMbBAF5tt1u51WfruhdAbpb6H1S2i/S+dbv95kQEE7MsjyrMphKCaSUPiTpP5P0elVV33RybUPH5w48o+PNQ763qqq76bjm/4Ok75LUk/RXq6r63UnvcAhq7230HyEXuDjNhaB74KXEiAwYwZ+jo6N8WrBP44EIdnZ2ajvKMJB7e3s1aNpqtWoZaUT/qS8xA5/Hpm6S8toA4CjBRdYUREXmU1HUmzq4C+Dz9TE+4O8f97n03ZWbW7ZSOZOsmytJH/OqqmquDsK4tLSUx895gLFnrp++IQjLeLth8PohlIybK1RHELhxnknIDkaOnqiPfyYByY2X/+7uBd/5PSqupr5s+l2aHgn8r5L+J0k/bdc+KOlXq6r60ZTSB0++/1c63nPwHSd/f1zHG4/+8XGFO7PQQK94CcJCCInPucasvfgOhJIsL5+b7/V6GQEMh8Pa0V57e3va2dnJe9FFGI5lkkYHXfpxWhySwaYXMSCJS0F5bGCBEpCU64XwupC5VYdJSrDc3YImqO5jE5FUnB5sevZBko+d79bkwk2uPnVyRCApBwxZ2YcCIObi/r3PAvjMS0opKxxyCRDiqqp09erVUwbGeSQG+xwBlvI2mtASBDoo0bTjMZUSqKrqN1JKz4TLH5D0bSeff0rSr+tYCXxA0k9Xx7X/SErpUqrvO1gq/xTscxjnwY8Y5eb8uQhpXRhiLjZaGsvuO86yMYX7d5xu4+4B76FOKB3gYQxwcU9M63WkgqD74LmA8ZwLO8wuqdZ3jgh8bX0MOnoArMR00TUoKZ+HKfzeByVXkbYw706+RlSOIDnGqNvtamlpSfv7+zmfw3MFPFCLEgFdEciDd3jP/v6+bt++rfX1da2treVYBCjC4wuumBkjDEZJ0dLuOE7RLTsP3U9M4JoJ9muSrp18flLSi3bfSyfXGpWANBJcPjvkQ9P7VJd3VGQOtCv3YHEpGxiIsHsswK3nwsKC+v1+3vCTE39cwDygxCo2t/DANYJEKC1PPELhoMwol7ThaLEdHjqjRuUAsqA8mMmhciy7Cba7sJesf5PVelAU2047JNWEbHV1NcducLGia+ICWdorgH7FZSNr0/vc0aQrco49d4TCO322wnmXNrlrUVICfC4pgZhgdBZ6IIHBqqqqlNKZ3pzs3IGNjQ1dvnw5R0kRQDTwyTtqMBbG9owuFwC3iDwL+doAnwem87CSZP/5TAC/+dp26utJPSAbFBJCjzJgnzvKdUaEeXd3d9XtdnP93Q/d3t7OEDRGuf3obhCK57c7fI1uARQVbbT2tM9/exiKoOSyUD++e1Cv3T4+tJXDR31TTxQ94+fK1N02R1KeO9Jujw4HcXTpSpJ9CG/cuKGVlRWtra1peXk59xltgjzu4OtJolvc5AJ43MDHwftvEt2PErgBzE8pPSHp9ZPrL0u6bvc9dXKtRpWdO/Dss89WKysr6na7WTidqXd3d09ZTo8ToA3x6aT6mnpHCo4KEFI6zeftmQUg+yyuHXArTzn8xqA5vKNc6ukRcJQagUgsuOfD+yGhfPeddaURYyKMnvDicN6Dl9EVg6kmTU1RL+hBKoCmsiJaAeqjeBkPgnOtVuvUFKtnDNIftDcu8YX/UJrEfBh7SbVZGsZ8f38/H5XG6k4PSsYYC+SzSd7+Eiqgj3wmYlL/NdH9KIFfkvSDkn705P+/sOs/nFL6OR0HBO+NiwdIx43nRBdP5UQgImx1cljr1sGhnz/jKKDVauVTfxzWE0DiRFrKwwd05MFnaTS3LCkn7fjzEQY6c2G1aC8bVMDQbEzBQhXfuDROEzkioQ+ok7tT3k9QjL+UlEBJGB8mxTo6eiEvoNMZ7RCcToJ3y8vLqqqqltdBQhfuoscLfB4f5YgxQEkQt/HYgRsjNhnFreN7SkkrKyunFADfPTbjPD2JmgTe4wilPnSadorwZ3UcBHwspfSSpP+vjoX/51NKf0PSH0r63pPbf1nH04Nf1PEU4V+bVP5wONTt27czU8d5bZ/KKk1pSaPcAMpzS8dvMIAriKOjo6y12eKb04U7nU6GlW71W61W9jmhXq+XmYXpO1b44X9KI0UBwqH8xcXFfHQ2Zezs7Kiqqtq21Ww97lOXpdiIpNrctveTr513BEM/xdiA8UFtTLyv+d3/l2Av1923999Lvq0jGH73ZCy/t9Vq1Q7yACl42cQEmPFx1EYehqSMAtkx2A9CdevMgSE868E6ZitwFxcXF/Oq0uXl5bwTEnyfUqrFmOBRd1FifzeNk4+tI4VI084OfH/DT99euLeS9EPTlOsUc/ahpuCUo4Ko5UrPeDyBZ9C8DDr30WnsC8i0IJakSWvjTgCrPegEwxJL8DIkZQvGwpSlpaVaHMJnNbxcDxTBqFtbW2q1js8zaLIS8c8VbcnCNymFaamkGEplRMtG3dzNcmjvCguFy3Me1+h2u5JGaIjAMIqbKUFXmq5g4njDJwgpytERGG1hBmJubk5XrlzJ8S6eBcl4/7igN42h33M/7tjMZAw6xIwWwJVCyS2I16KvxwC5EnDrxyaQ7lOzKwwHlHiWHb6oNPLbEVBQgzSy2iiHqJF5D6gBq8GuxZ6gwvOuaHzwS33A/6gEvV/jbEspFjBOAZzXFYiM60oi+r2Mowf2HOlRVlxJ6YI7Nzenbreb3S82AaVfJWVXkHH1WSDGxdGo8xV15pnItxxQs7y8nPcSYBdqzkX0tnm7vW98fKKyhJquN9FMKIESbIxM7PeWYgP+WxwABN4Dbe4q4JOTwOOJIjs7O2q1WnnmorSpRGQ4YCXlO1P6lF6/38/Q3zcw4XlJtd1nfPCZvXCmpJ7MU6O4QCA+S4Dwe5zAy5oGDTT9VhLi+JsLb/y9xA/eN66s3Hhg/YnpRHeD39bW1pRSqsF8oD7vYmxxnVAyPpuAgvf8DG9P9O0PDw+1vb2tdrtdW4bsZ1Mwi8EYOwqkHTGI69dKrtwkRTATSkA6HSjhWoT/TRbP73FhcYjv9/GsbxBBGq/7k2wH7nnmHgDkWal+QATZiF4vaTTLgEJxC0yuwOrqai5rd3dXrVYrL5hxV2BnZycrK19q7MzhKckIgc+TOxKIYzBJyKf5rTSOfm0cg0aBciUqjebeo+UkqOoW2nkAVMDUHatBfUbI4z2ucDyN29GfC6DPPkGUR4B3fn5e3W43u4dsIuNxLQxAk3KJqJL/44xqiWZCCdBQ17pcdyohgKZGRlgYA04IFAFD99Ox4v48GYYwF8LlawGAh3ExUQli+7Qcx2gjpOwhcOXKlQxPfaWhzxEvLCxobW0tuzQEo5xxQC8olqOjo1PQdhz6arLy0/w2yZWYVgnQXgSRPqQ9kW/iDIjHEbC67hphle/du5eRl+dT8P7FxcW8BwBuAgJJv/LemLdC/QhqUg9QiM/weMyoxOOlvonBv2lcAWlGlIBUX+km1f16nzst+bxx6s21v0N1rvGdlWcE+/y4793d3SxwXg5/HhRCkAg2wZTUD+GmjfyR2IMb0mq18lbkS0tLunLlSm1NBIEs+gP/klN1PWKOIqJfWGTDNubTMFeTxZ/GVWgiF4pS2R4XKt3jQUL4hbFpqhPP+cwHSnU4HGbF60iBaUXuR+idJ312IsJ0z1/gOV92zA7EjKEnPPlMiBvI+J5SO73fpo0NzIQScEGO8/pSGQHwXMll8OdKU1h0dLvdzouBJOXtoDlhOKb9ssGITzUy2MwmgA48SkyGoPt9KSW99tprebqIhBLcBLICUU7D4TBPKXq6MJDYU4PJEoQhWX/PFJVUDzjFvp5kvZt+K/0en4sWuwnBNcFayqANHs/gPg8iIrQkkjF2KGJfxosriDtRVaMkLWC7J155PIB3cZis96/Xkz+2oJubm9PKykpex+J94MYm9gN/JbloGpsmmgklgPV131RSFipP/Y0+pVs1P0UW5icLjzJ9/tV9MWA0zy0tLWUN7st8fdEPCSG+vwDwkRwBoGNK9elHFAywnBx1t+67u7t5l5zNzc2ceMJimY2NjWzFhsOhdnZ28jQVCkVSzQrRv7Tf+xAGdV+2CTHE3/nMf7doERXFKVZ+jyggWnqvewzGurC4BUYxoqwZS5898jRx1nGw0pC0cUeBrCXwZDDe7ceNMduDG8FmL54z0uv1cr8Tr6Fe3j8eGKU98Dr9EFGAu8E+ppFmQgk4jYv6l3535RAZEkuN8AEDUxotImqyOM4cDLRra/xx/G8ChjHAxrvd/+Q6q9FQGESNGWDPSUBZES+gbv7O0vJX/gh8SqqtZouuQYlK/TqNq1AK3pbuZRybyo73uqUvWcZonaXTGXkO57kW9yuMKcEs40awSvsIIsguwG7VUdLEekCQngTmfUE9MSTRJY4orOQCTEIMM6kEosB7PKCk7SLDxWgxQu9Rc59ig5yZYIput5v9cO7xMrB4nv7rwi6NUpXdx/NIMO905oDhfPMS/Hr8Q5CLW7ZOp5OhqzMOzEZdfVai5BqUBLUk6KVnpqGSwEdFzrXIB17fyBtS/Th62u5jBSJwuE3EviTg/iw7GjP+Pn7+OSoBrzPvgn9ZRjw3N3cKOcVYQakfS98njZXTzCiBcYIdB9qZJQaFfED8Nx+EJuTgUJiIsUPtlFJe1dfr9XKyR2Raf58zizMYCCWl0bLpuBCICDX1800rHfoyXw2UjYomogKsvwc9o0U967iVxtHHyMfHP0fLVYrfRD7wAHKJP3yKDlTlMwgetfe9IRl/jxMQS0kp1TZ2ofySQqL+LrSMgydmMdYoIxQB7oajGOoUkUdTn8W+HkczowQgF1y++/UIa6LCiGX5yrKoNND8xCNcaGEWmIPtvVJK2a90q8D7vPPjjAWMgf9HnIF2gExABK5A3I3wqDT3+loGF3xPbHGE4oqpBDGbgnIlC15SHCXmHHe//zbpulvYqBRiO1zgvK/pH0mngoUuwBgD3AOeZUo3Wn3q0DRj5e6pR/2ZxmVL8og8WQ8Rpyyb4ilRsb+p3AGpvjZaal5TLtXhf+k3YgFxtRjC7dYBLeun3DoC2N3d1fb2tra3t3MQyafhEGAXMurvltnPCPC8AmYCWFbtU4fUFb+eyHa0iJRP0MoP0aCdzpRR6KLgnscdiExYQgLOwP6M1yGWFevmygDh8gAaz8UgG+QzC75k2AONKF2eW11drRmTqOAR0lJMiOuU6bMauGueIeixBY9FlJBbk2KeBt3NnBJwgXZN2iToUQicIrM6XPQFKXQ0mj6+0zuavAJPKHKt7kk9lENwkYAdzCKNLDxKYXl5Wd1uN9/rWYyUB3P53vq+PyF19cw6n9FAKcTZAG9P0xRipCZFMA1FRe/j5NcnleFKmHozPo6+HAF6IpdvBss9ngjmv/mMgOcr+LStVI9F+T1u4FwB+Bj62MMblOl90oTAxv0v0UwpgZJViowRNZ4/hxVwf9yhoPvA/C9lzjmxOajvUIPfhgC6FfCpG68X03bOsL6AiWlJP3OQ+ANBQk8ykkYCT1t8CtAFmjpSP3cPfEsy72fv2xIDlRBCk8BOUgyTno9UclXc+tNHHhvgdxQC7pPP6eOLQ64IXZDpLxSJNFo45M+74vWpaU/thidBhYPBIM/+uNFz9yX2vc+WxL6ZhmZGCUTYQmMiDHKU4PPbbsXR1tGfjvPGUXg8Eotw+dROaflqSqOAHYkmROilUeBOGgWBHHlQf4ST/942hJ5dlYlHRKTiqww90BhjKa4sUYjOsLS9aUzGQcwSpHeKrl7kAcaLMlzJQTC9+8bcz3WUW1VVNaXJONPX9Bljwh/97gu7eJYj6G/duqWdnZ08znGMCfQ5onAl7SiBNg0Gg7ylPDzshot2eDu9DW4A+B4DqZFmRgmU4P5ZLEN8zjvJBdo1p1SPKcQOZ0BgBl9g4hDdIWeM4Prg+vthBlcADuU9eMRmI/iuMBiZgZ6tWFVV3lHHpyC9fv4OfnehnuRDvhHkyiSOrV9rimGUkArkSsRRnJfncZioiBhHEsrcePjUn6/XgHwrNL7HcYJXHKF5jClSSVH7b5PkaKISSOWDR/5/kv7vkg4kfUnSX6uqajMdb0v+WUmfO3n8I1VV/e1J76Dy51UE45QAWrMkhLGMqADQsB688xNuYCYEmb/oZ/uqQ4d5zgheJ4/YAx1ZZ7Czs5O3yIZxXbhhaE9bPhmzqfr2fhTAOHeu6Z4SlRRAkzKYti60vwSlvf99sRWEcYB/yPRcWVlRq9XS7u5utsQlYY3vjAHMWB9PKqJOjojHxQLom7OM4TRI4H/V6YNHfkXSj1RVdZRS+u8k/YiOzxyQpC9VVfWeqWtwQnHOWpo80JDfFzUng+8JF2hej7rGaD7XgFsO5zzazj1uzR1qxwGkfJiNuIQrQG8D9+7v7+fTj7a2tmqzBAQTgZ/OPNzj0W73lekjVyRnpXECNy2aOwtFYxGFrGQNo5Bw3f17qe5+xuCex41wKelneAPDgzHwFOHhsH4qkTTaUcunG91gRYgf2+ZoZtwqwnHjOlEJVIWDR6qq+rf29SOSvmdSOVO8p5gHMEmreSfRGf4cvpcPpMMvn3ePlsenEd3Pc+Xh+9c7fHNF5tFpVwz87m31drD7jQegWIfuEeO4mamkU4wbkVYpT2BapTtuLPxzkzBO+44miFt6ProHLuj+50G06CIx4xLr6u6AuxIocF/GLalmEKKLt7+/nwN/Dv0pyw0AaID4RGyTf6esqPCm6fMHERP46zo+kxB6NqX0CUlbkv7rqqp+s/RQsnMHLl++XLO+ULSMTRQHOWr92HnOBD7AUUh8g1GsBp/dN3dLitLxjgei+9RhSqmW5y+pthik1Wrp5s2bGTYuLi7mjTA4YIP98agrjOR+J0qBd9Ankal5ryuPaagpjlCy0tNSCVlEvzcqrNK7/Td3lxijOD6guiYXRFLOyqSMdrudNwvFyvsOQZTJd1cyTW129BDjON4fpT46T7/flxJIKf19SUeSfubk0quSnq6q6nZK6Y9K+sWU0jdWVbUVn63s3IHr169Xbp3OWIeaAohKwH+LueAeDHIl5H9MG5V8bBeWCOF9oBxNODOyDwAMSP6BdMyYrELzY8a9fUdHR3kdfK/XqwW4/JwCSTUlFKH/tLBx2vHwzyVBnWSZSgxdYvwmikFg/++W2f1/fouumY9lSimnibdardpGMO12Oy8NdoFnzOEBR5L+Xh8PX+VKABgl3yTkpb4pjW+Jzq0EUkp/VccBw2+vTmpWVdW+pP2Tzx9PKX1J0jslfWxcWVFAIA+Ucd/Ju/N3vxY/Y+W9LC8HRuH9RNnjAROU6YPJrENM76R8tLgrAFc6WAxHAWxbnlKqBRgd4rOy0P19aQRBq6rKsxeenOJ7Izqy8RkNt3Dev+OYqYQAohBHNDfJ7YjvcxcvKpXoTjGu8VpUCB638aXm0XXzNrrijwYEVOf3UzbC3G63a0u8q6p+2pFnEfrzjK+j1pISHxcXeODuQErpL0r6/0j6c1VV9ez6VUl3qqoapJTeruOTib88TZlAV0+kiP6YN8otr1s8aTSP6mUw4O4387x/ZiqQWYBWq5W3/3LhsTbXAkY+ayCpNmXkEWivB8LNe2CASdA0KhjmlVl8xGeUCpYrZri5wolxmWgNnVxBxWdowzjF7VRSMA7XfazieEb3oKSIooFxJR7b7IjR6xZ5yi0+isA3LKU/HU3GXYW5jxmoOH3NzBIKxmcOHPHQRkfU7u6Oo2mmCEsHj/yIpAVJv3JSAaYC/6ykf5hSOpQ0lPS3q6q6M+kddCyN89Vavty3BIWiEPq93tFxYGHgUtDQI7ck5nCwhOeWS6Oz/mL9KB8l4PsOSsrbfNFuVxIwVkxHLqEDRz0el3Cr6HPZoASQAoegUkcURpNQjoOhJUV9XvdinKLw4F6E7dIo0j6uLMpzoaNsf6ak3CLKiUIWFQuzM1L9FCgPzjJ+EZl4TKg0pdj0eZLL5TTN7EDp4JGfbLj3FyT9wlRvNvIGu4WkQ9wiRnJo54rAIbPDeMp1Te5l+TryEirwmYWURstLqSv3efYfswg+wFhqhDQy23A4zBmC7EoT3Qksma9k5HdJeX+DUj8T9OR6VI4uVCU3ywV8nD96P+SIwsuNSCAqG29DRAn8Hinygd/vAu3ojTo6+X4OKFru852nolFC2VM27/GxRZlEN4XPEQlMSzOTMSiNBIngGFowUvTP3I/yshz+N1mGyCSuRX22AkHxLbnYehph9HXgDCoDgz/oyMPPtvd2gkZcCTjTxGg+LgTrDhzWU39fHOP94WV520tW3BVDtPZNKKBJGYxTEiU/38e7NJvj74qZdlEwYn+X2sd9jqjoO78/GiAUL+90uF9Vx1vYkRLsioox9/qCzjy2E9sQ+2dSv5doJpSAC6Ef903nROZzuO1KIGrlJmvGdwSyNC3p90ZtThSZE2cpS2qObXh9/b0ekPN8AxBGRB7eRpAKR5P5XDJWHoXj1oydbTym4G6QW7BSX3p9SmigpCDOAk/9nZTTpJgildxCF3r/LSqCpjiSX4t/3OMLlko8Rxt8+TduV5Oicz7xqeoYSyi1PbZ/HM2EEpBOp4i6No8aLyoAF7YYcIla3wfOFUBECkB631sAwtd3bS6pdgx4RB+Uy1LkqMBAF55+jP/OajffJYcYA7MFvAv46MoIxOKZjV5/pxLDxPZHIY9UQgtN9zRRyRfnuRgo9DL9PsYxKvaSQokCFusekaW7BYwJY+U86de87k1KxRWxJ/+48nDXpKRESvUf198zpQQgbxyR7xLMjEJemtbxJBnKlkZRVu6NHcr8r0N2TwVlygcmYFBjUo4PCEHGfr+fDx5FYD1+4fXztFJyFXwNg0f/mXkgToDF9xOKvO88y5G+dmgbx8VpkmCfBY5OS1FIXQE7LHfFXkIwscxJ1tMDkTzjyMJzL+LJRIwHv6+uruYyXVn4u6Q6IsbiR/e4SdgdeUxDM6MEPKnCXQDfH0+qJ/ZIo+h8VVU5GBefleqCzuA4fKYOCDUCQlaedHrgPLor1Q9QYfB5/3A4VL/fr52CK40sNxYA4ayq4ymnXq+X60MZ7GokKZ+FMBwOa0lFcR2BE24EyMRTXD3gWFIEHnSjrY563Fr5c4xh9G2byGEx/VeK8URFECkixdgeL9vbiBCBGGN9vJ4YDMr2vSR8/GMMI840+btJ7vKZhP39/bzRLHyKIZq2X0s0M0qgCWKWIHWEQ1Hjl5hiEpOWoJakfJZBrBsd72v2Pc8fhUKZWIaUUi2W4JbNlRu/sSz48PBQOzs7tYNOJWXmA1lIqp1SRD3c56e+HviMsyy0Nyo8r2spfhL7No5njLxPohKcjwog1qFk7SPcp56x3n7dhT6iBX+Px3VQALhdJXeC/iwpUHcL3ABiWHzsx7Ultn8czYQSKEEypyiw/kzJ9/Z7IJ8+AXq7Ri1ZrqqqTi0OkU4HkLjGBpEMovuKuBUxKOer16iXMwpKoNfr1TabTCnlc/GGw+MzDJaWlvKhF3t7e7X4hH/2unscIiqBmLdesuxx3FyZxfu9bePIy3IlFZ+NSqA07qVYQKwn95WUjY9ldO/cJeA59pz0cUYR+ywMlp34Uny3T4tTfnQP4RFXJudBAzOhBKQ6bPJOjYPHNRi61PgmPyl2pKfJOkM4RQbwOpIS6nsN4NZQnh906n64TwU5iohWmHIlaWVlJU9JHh4eamVlRUtLS5kJPC2YuECcWy71uwuET2t5zMV/l8afaOPl+jj4eE7zLO1yAfGxiIIbUVy8rwkp+DVXwG5kfAx9fGI79/b2MuqKqeXcA9/5HoK+NiXOJFF/R5feLyX5GDcGkWZCCbgARiUQLYArAN8fzhnX7/X/3mHeyVH4PRDk0zU+GFzb29urZQISaGP+3jcT8dkDFAQIwQeXMkglTilpfX29ZlVwA1AmrCp0H9+F1vs69oErSPqLGETsS8rwcWsS9iYGnGStSkojCn+sR0kZlFDCpHqWlIv3VUmhOM+x0awHYSMCoxz2iEAJoAh85ggDExVBFPySnEzrds2EEpB0SglIzQ3xAKIHEb2zfaoQAtr54JDJF6fsXPN7INGj9iABrL0HIrHuZImhtBx9MMfPO316EKvCKUhLS0v5pNx2u107GYn6ohzcP/WAlSOC6AaU4HuMuZRokiKI18aNa6QSCoy/U4cmRRHrWqpbFOToIji/NNXD+YmZGUeRlON8mVLKO0ZxYK0HBqmDZ326axFne2LSV/xroplRAlGLSyOhdQvFHwrABzNGgiOEl+pBnKoa7QmHBXUF4FqXiLrvFOQWlXd6kg7+XkwJdQUyHA5rVsBXA5Lbz3JhMgOJ+FN32kWSUUpJOzs72tvbO7XnHYotCrj3l/drySpOogit47Vpymgqs/TdrWS8p+TixXsiX0n1DUNd2LxNJfThiWT0NUgRvvElwriTjBVrBCJSc5Tr047ehvPSzCgB6XTkl78IS33A3FrQWSV4GBkcy+ibSXiQjj+f7/WsMOqCMHp8AGThJ9BSLr66uyGeA+BTQktLS6d2F+a0XBAG24x52/v9vra2tmrW3xWX+/gxo9Bhr7sTPsUVUYIzaknYz8ugTWhhWjejCRXE7yW47u0tlR9jB7FvWDNC33o9CB5yyhSnYx8cHOQZnkkzNRFR/EehBJqsD7DHfeYYnOG6nzjsewM6eRZWuz06xcfzCzhj4PDwMB85jmX3rD3m5hnAmPDjm05Ip5ctHx4e5rMF9vf3tbq6qqOjI+3s7OQsP6L9R0dHtRWLrtBo02Aw0O7ubrZAvV5Pd+/e1XA41MbGRm0Rk09hRVcAQYjQPqIuJ8aqabYmIg5/zv/Hd/l9EY1EYXYh8ff58/H9KF2uU/84a+SKzpO7Yj3oYz/j0H9n7UAUXoyIpFrgUDqe/iVdPboUsR28M+YNjHO/ZkYJxIH371HjxTlt/92fl06nlsaILh2HJXbtDeSKrgEdWopNnKWtEcmgZDywJ41OyOU6gnzv3r2sxHwdAC5Dt9utRbddIGDkJoGO1r3Uz2dxDc5C4xBAdPPie0rW3+sunc4fcfTD725cHCVEAaQ/GCvP4HTeabePN4QFBTCGkmpxIOIJXq63iedKK09L/D8NzYwSgMZBmyh0xAA8IBIVRoS5UQP7Z58CArZ7ea4IqA//PWnIrUfUztF39LYhvDAUiScgCd4HfOTgC5/lANWwAjG6A7QBZoyQvjQWsa/uB3pOSyVkcJZ4QrzPhbwkLBExOJxn7N09ioqQPkTgsd7U23k28giKn/iBBwi9nq444tZoTW7LNP01cTfJlNKHUkqvp5Ses2v/IKX0ckrpkyd/32W//UhK6Ysppc+llP7TiTVofm9t8KWRsPnmGiUm9ud8JZYzQRxcF3aEBpfAYwJ+oCmwL6YfQx4h9nq0Wq2c4RezwmgndaC+WO9+v69er5fTiUtBRxd2lEAMLOHiuLLy/1DsszeCInQ/i//bpLSaUCRt9nUUpWuSakqBerqR8ak+An+eMARqc2OCwvaDZDzhzOspjYwT49eEgP3zpD4777kDkvRPq6r6x2EA3iXp+yR9o6S3Svo/UkrvrKpqoDNQafB8cBwF0Dk+WF6GM5An45RgcukdrOSLKw55fm9vr2ZpvSzq6It9qDN+IX5fq9XKS4JRLg7/mE7kMwdgwKi+/RltZSUhcQnPU/B+KkWh3cePCu6NVAbxvZMQQdNv0VpGSI8g0+cgOPrfFTTjHDetcTQIIuh2uxoOh9re3j61DoV3EpvhHRG9uosalQhl+c7StGlaOte5A2PoA5J+rjrecPQrKaUvSvoWSb89dY2MosC6dh6HBCJE8nlfSTXr64zlsI35Xp+dSGm06MN98FK9vQ4wXLQ0BCapg88s+CwEDMfCoRib4J1ed55zBvW+K6Gps1rah0VuaaPgTvusf4/PRXQRffRSOSXy+rkB8AQfzonwPSR9sxne5coNBe6H3LjSitO9rrBj3V3RNdH9xAR+OKX0AzreSfjvVlV1V9KTOj6MBHrp5NopSnbuwKVLl2qVLjGkC06T/xq/+7UYSHHlEQfT7ykFH92N8JwDaWRhmHpEsD0ph/toEwPo6wuceR318Jz3Cb/5XoQxbkF2Ijntnm1Jffx/01h4nz0sihbaxyYGdkvkAhORQRO6aCrT34/FdaXueSSSTqEB3oHiJRbj8N4zDCXV4jVeVmwX372OZ1UA0hQxgQb6cUlfI+k9Oj5r4MfOWkBVVT9RVdX7q6p6f7fbbbzPGS/6R/Ge2AFxwGN8wH937er3UU4puj4YDLL/55bVBdaZg/vQ9hDPebDIMyE9FuKbguBL+n6GvhDIZxn29/dzWrF0epYk+uAlwS8p32kpKspx1CSM4571+vu1yBsR+fk7x72XeyKCcGEmd8MVvi9Zj4as5GZSlq9IdcTZZOzuRymfCwlUVXXDKvHPJP3Lk68vS7putz51cm0iudX1DqazsGAeA3CL6am3Lnxx3lYaaVbglmcBSvV4gaeBMiCUjUX1HAEXdLcglAlCqKpKW1tbtfl6YOP29raq6jgxiJmA6Be6QiT45EuG+Y1rHoEGEfhuQzCX7/bsdaffXdialIILn9/vDB8VeXy+aSam6f4ma+j85fXxtFzq69mj1MGtcBxPeAG3cW5uTltbW7m/QXYo7H6/r7W1tVofLS0t5ZWquIbM6rD5DDxDG9yVPDg4qM0mxFiX5xY00XnPHXiiqqpXT77+JUnMHPySpP8tpfRPdBwYfIek35myzEZrEyPWTa5ASSuWrEAJKrrycd867iMf6+oLh7DYrrj8GX+HNNoQxP18bytxAF+j7ijCTzBq6osYQKWccbEAglpNYxKRTBT22Ofus05jsZqsdNPYnZXGPYcydbfN6xJjT3HTG/fNvb1MG4Ie6RefrnV04HGKEvp1JeVuQbzH/zfRec8d+LaU0nskVZKel/S3Tl72mZTSz0v6fR0fT/ZD1ZQzA+7fxkGKQayTehXhHhSDLkR7Sz6lCz9WorSmwJUMA8bsQISH3OP+ub+PugD9YB7KBcL3+/1Tbgflra6uFrPPPBhIvUABtM13E3Jl1aQcmvq95Jq5pYzjMM6ix/GLzzZB9rMqg3H3Ox94PUqIxF0udyWj4peUd43u9/uZn3mG3AD61hOGcCXiITaSasajZPyo/yR6oOcOnNz/30r6bye+OVCEnc40fuS3Qx6/xxkmWqQYT4jQludceBgUXAJyA/w5oDbvcisOfItKzX08pgd9CzCfB8YNQHhdaDudjrrdblZu/EZfMi0YXRH6M5YXLVzD2Ob/JaH09pas0rR+a4Su4xSA3xPrOY5cmfkz43jEn2N8YyovrkHsg06no6WlpVMum7t5pXf64jnvi+gy+ftimyahppnJGIwCw3cPtPgARUXAM1C0zvx5FJaOLEFY3xqKJCHWFPgAAOkdypeCOVHIsQIeEyDxyF0DTzyhfHYRir68R65dyCFnzpIAe9/55/jdr8fv8T0Rtp6VJ0puQKxvk0CPo3FQeRK6jBl/8XfSsSOS6XQ6+ewJV+7eR65Q4l80aO5KjmvnfbsDbxTFgaaDQAHR8peEGoqa212NmONf6mwIIQSiE+gBBXiChiulEvx3tOK+HnCQSDKLezhwxBUBzy8tLeVtxzxgRJ1c6Tg097UKztBNmWfjLLALuLfZhaPUp9NQqR5e9jiX4CwKILYltsHHDUGLyqOUIOb3e/0J4rIPBPxdciV8wZykUzMPHj+grqWMxgfiDrwRVNL0Ul0I/d5x8QMXxIgqoqUuWbl4jXczAB7gcosdF4E4Ezl8i5bMs8uWl5fztF98nwswkX1nHOILvhGpP+vxgFJKbByHqARKgujKJo6PQ9uI1CYxZxNCieX573HMx1FJCfA/zu/Tj1G5+5jynf4kxiOdTlxzRBuNmzRagISFZ18C348QRRORbGy/9/PMuwNeUSoeE4MiM41zBfy7M2sJUpbgbdT2DJgvvaVcBNjzv1n/7wdGRAjpQojlX1lZ0fLyclZ8RI5dmCFHHi50nnxE2bwLJeC5BR50Lc3CNAlkjCOU6jUOVp9XCTRdc0Eo0bjrZ6mPNEod9nJdCUSlEY1ADE47cuJgGsrnGXchGGN/vqRgx/WH00woAWkU2AIGwahSPV4QtScdgIC4r+UCJI00vVSHyZ60Qad5Jhcd7nv8Hx0d5e8ecZdGWWNY8lg2wra6ulqL0kchRFD9/EPq40tWoVarlaeh/HnucRfLFUFMH3ZLA9KIsRT6O/q9XIurF/2eEvpwZo2w1u/lvSUB8PuaaBJacKgdLT+/exmOLCXV0n37/b6Gw2GO7GPNPeXcjUJcE0K/sJeALzqKvMo4E1fiHaw3aQr2SjOkBKCmWEATRYXgjFcaxCZ/0N/vTB4hr7/LgzmuVObm5vKecf4ckXkf5FgHRzC+1Ji6eV1cWN3CRKH3fvTIdOyDEhqKQT7qFtFSaVxKn0v3TGOt3ijyNtLXJSQjnd4wJPaJI6y4irQUPGXM/X2unFH+1CkijBKKnTRO0gwpARrgue0exGtyAWJAzi2vQ6ZoqUoQkN9dAfgASacFsVQ/fPYI4amvLyCJVtPLcSZ0iA/FmYiS6xMzCD120aQI3EJ5/V3hRTjM59IY+W/eD/H5OA7TUGzDNPdPus8VIN9je5rq6AjJlbDzphu4OHZ+ZH3kAw8ExuCgBy5Lhm5cn86MEoCpSkGrKMjxr9QhcerP/2Kk18v2716m17HE0J6ZF4WxZFmpBwPqVEIIwH/eHZVR7B/e5YEoV0DRL/X3xfp62/3e+L3EaE3XxgnjWZRApEkMf1bU4WMR/fDSvUzVlpAoY860YDRgGBBS3EvW3APJ8Ke7XE1TsW8KJCApd4LndXOd/yVEAI1zBehwD9rE8kufXYikumA5eiGY1wQdvTzu29/frykB6ud1c0sMo0T0UYL3POt9FYOtTX15v9Z53HOxz0to7DzuwYN0KSYpqab+wY1kdgh+5BmPWzWh1Ii0PKbFPWQUuiGISuAsCGlmlAAKIE4JNvllTUwJvI4dG2EVgxSFfpw1iwE8VwIxcyyWV7IKBIwiZI7TPo50Yp2ikmpSII5WSuilqc3uw0bGnkRu4Up9AJWU/TTU5O9Oen6cUNBOv9djLG5o+L30Rz3cUsPjkmqrSwn28owvR+Z/5F8C0lDJPZu2zTOjBDwpJga/mqLLrrGjPyvVF3s0WT1n7gjNXAgdpbhwuSD5QERUEBlLGq1w9D8sNvdhQUouTpNf7e30vojxAf8fn3WGK82kNCmOkrIeZzlLNK0Qnxc1jCPvf68Hew76ATTUxfmudGioLyfudDq1LcR8qtUVRUqptsU99Wq1RvtJci0qJO+nafpnZpSAK4AYeIvCzH9nOmdSt6Lj3IfIRLHT/F2OABydNE2VxbKbXI5YH//v6aAxSOXWtaT44tRVtPxxliDWtQTbm1yocVTqhxIMbiqzpDzHocBJdSspz9I4lZQcvIgibLdHy3kZB6alm+rQarVqMQPnl1K9PEjrv3tswPuYMRo3JRhpJpRAVVW11NiSFYudWhJwXAGsV7wnUrT8kRliHKDkU1MXnonlx/+R8Zrq5vdIdWgfBbPkgjhC8utNQcHSu2Mbows1zfP+veQaxPKbyh1n0WL/TlJQTQq6STn5vXFWx31z30cyKmY/EyPWIwb6EGLyAfjdyV0CDxBGd412jHPhZkIJOCwqBUxKwhwHuikg6ExW0rolwfRnGeAYaY/lToKoJUUwiVlL+wTEz24VJkG/ku9assxxHGKfnAWCx/fFa/FeSafe3aTAm36ftn485/kWpTaOq6ev70DAfZdqykeo+/1+TZH46lTe63zmiWGuNHzMI3omWciR3ptCCURo2iTsTRDSgzWle6OrMC4Y5srC59SjWxLdjlj3UrnTIACoFBT1z9FP93yDpjZB3BdRQRNyimMyqe7eN02KII5x0+9OJYQT+2UaJdD0XAnhNZVZVdUpJZDS8WnSe3t7pyw/AhvzBrDy0dg4Aogusu+6FHmT8Y/uQxNNs6nIhyT9Z5Jer6rqm06u/e+Svu7klkuSNquqek863pX4s5I+d/LbR6qq+tuT3kFDprlnHJNK9bROvy8ijPicv8MFw4NEJQU0Lfxsaue4Z33gS3X3SLZD1NIzpf5oKjc+06SUm+reJDDjyqc9jmiiki0pgJIiHtenJTcgfp/mee9LUCxBY46fIxXYN47xlF9PauN5P2UKpUHfRIMW0V8pbhDdxRKd69yBqqr+n9ZxPybpnt3/paqq3jNFuadokoWJzBy1dhRaq2+N0Zq0vSuAJgtZqpO/J5Y7jdUcRyWha6pH7BNHEk3Kc1y7muo9zTiV6tzEiFGxNSmCSFEBnAUBTLrWRE1Ihr72aWTOqWST16qqtLi4eKquEcl5P/h6En8uoqeU6ntIoCR8q7Qmuq9zB9JxTb5X0p+fVM40FLVeqfLjBsxX+kU/P0baI+NRdoRkJWvq5ULTugSl+89D0SJG1OBWpEkJxCzHprqW2nFW9BMZuCkuENtSEuxJke9pkUDTtWnHpUnRw4NYdl8I5s8y9cv0IUeULy8v5+lo5984Ve5lDQaDfPRcp9PJyMMVSxPdb0zgz0i6UVXVF+zasymlT0jakvRfV1X1m9MU5NY3JrlMgq7u5wOlXOg9WcMzsCifMr2MkmCU4LlrYq9LUxubBLfp/tjeJsvq5cX3lBQA7fT9BSdR9JmnqXtTO0r+v49BVOTeVl9TEv+fpV6lZ2O9SwpiGgXuiABlcHh4WNs9ihmFlEY5AZxa7OsLYhZpVKYuH7yvhC6a6H6VwPdL+ln7/qqkp6uqup1S+qOSfjGl9I1VVW3FB5MdPnL58uXaiT80lI6LiStuWZpy2vmd1FwP4ESfq0lQojUdB2fHIYPIMNMm2sTfx11zReiWNqKZWGdnoFKORqmO00DvCNGbrH6pbZPgazwotlTGeWmSyzJJATich+9QuAsLC9rb28ubju7s7GhnZyfzOm3b399XSsfbkfuOUu12W3t7e5KU4w3D4VArKyt5LEtGrBQ0dzq3EkgpdSR9t6Q/ah2wL2n/5PPHU0pfkvROHZ9SFDvrJyT9hCRdv369ahKM2OmlQSpZPj5HLVnKOfDnJsH4s1JTeePqfNaym4JjJQtXUgSxj89q6c9D9/N8ySJOW+a0iOFBkNcH3vPTn7DW0iiV3RGEt4tMQw98e9DPxztOZ09q9/0ggb8g6Q+qqnrJGn1V0p2qqgYppbfr+NyBL09b4CQhnEZIo7XCmnvHutUsWez7YVAvv0mwmt73IARrHAKJ95b+SuXFZ/5jo2mQzVnKKiln3FHWizhCaLfb2t3dzUjY9xKkDFzkmNPAe+L1Jre2ROc6d6Cqqp/U8enDPxtu/7OS/mFK6VDSUNLfrqrqzlk6zxs2iaFLQuR/cVFGXJ3oSTHTZNE11bsJIk5SaE2fz+Lfjnu39yX/J8UKzvLe+6E30iI3UURj09Rp0n0R1Tmv8d8tvltqP+imaayI/HsdPEOWdxAbiKcVl+i85w6oqqq/Wrj2C5J+YVKZJYrQpQnKRsaN3z1+wHefOin5wZOs4TR1n+aZpvvGWeBJ75+mzBKzNyGByMDTtsVpFgS8iaJiPEtfxzLOco/3p0N/Ti1mOpCpRGYSIqrFLSAbkUVNUR5imvs4RTATGYPSaag5jb/XBLm90aWViRGu3Y9VLNXJaZy/3fS+szJlU9njfh+n/EqWbFx9S+8ZpwhKQcJxdX0YdB4UcL/vigpIUt6FSlIOWnO+ha8W9OCgz3ZVVZWVgL/HZ9r4a6KZUQIILgxSQgL+fRpoXVVVPqsPDVx69rxKoKmOkwQwKrazKr9pyvbPPssBlVKSJ5U97prTLCMBpxIKuF/l5K5XFMxSEFaq5xW02231+331+/3aEuSYueougJ89UOJtUEETzYwSeBBU6oCYdOH3jVtrIJ2dIUoQfFoUMM5aTPP+cUooQsVYhyY08GYR5rPSw0YBMVpfej+/+x4CHtGvqkq9Xq+2CMnHyI1l6aATpzeFO1Bi/CaN61FPnnFL57nYTVt+UX7cIOS89fbPTYI06d5x95/n3dLpQKDf71aiNKccma7pXW7h/H3EX7gPXzaumBvXriaKfBLLGVdGrH8U2Chkk2gaQ1D6LcJ2t+S4sO12O+cReDyBcyh7vZ4Gg4GWlpbyb2QK4go4UmiimVACTk2MMc5Cu0JwTerZcNNYytJ7J9E4YZyEBOK9Z3nvWe5retaZsEmJld7TZHFK1DSeJWt5nvaU4PVZyikhtvMgg2l4qKS8SgrIpxOXl5fzeQQ+zY2QS5PTqCfRTCiBaEVKwagmRUD0H43njM3zJToLIzfROMVSujbpfq5Py4Ql6zVJILzd0yjIcWX5eJ3HfRmnIM5Dk2D4g3jHg6CoAEr9mtIoY3B3d1fb29s1npdGLkIpyDspruY0E0pAGs9IJSXAM5JyDrZTzD/nflcO51ECJUY+C8qYRvjPyqQxljCtQppWaOJz/kxTIHfcWN6v9W+y3k1CFelRKwD+j0O9LuAHBwe1rcwhF/64Jiby5JtCCdwPRUZsQg5RAZyVAaeFzOdBACVBHses41BAU1yh9O6mHYQijVPEpaj3uLqPg8jTKqVSv59HqcUy3giKysrr68gWdMtZlfv7+7VpQ3/ejz8rve9NoQSiNZkmUl5i+qgIxj0XP4+jaRFEU9lNn5vq7r+No2lQQKxfE6I6y7NxrPz98bdxKOF+0VikSX1QCiJO028PgprcofhuMlmx+iw+qqrjJCKfDYDiStuzBEtnSgmc5bcmQYpLg/m9ZO3uZ+BLFucsCKEEx89jxaTzCX9T+8ddL32OCGxSHSJsv1+F8B8TlYyaJwctLCwopZRPu6YPS7zmZU7q1zeVEoiBrKYOiH7XJGabhDT8e5M2H/ecvycO9LTWftz1knBNqk8JejdB/nHUBOEflDI7j5I477tnhXxlIBuPSKNNc3zz0SYDUFXlg0pLNBNKoFRBGuFbLMVgH599nwHXkJ6BGJXFOFjrv5eCLZNg/rRwtCSA/tskaO00CdbG6zCRv3scshm3Ht0Xr8R6xnbFe/0Z/k9CRPeLGKZFTU2Keprnx1FpHCPvxljL/Px8ThwiWAgikFRTDATK/cCaGDh3mgklUKJxzNwkhI+S7te1cDqvVXoj/domJn6j6/MfG41zxdzdLZ3MdV6aCSXQBEPP0rgmBpzk3z4IehhKaRKEa3rfwxS86GY1fX7Ywn8/0P0sKOONas+0FA8oqaqqmOtxFhdVmhEl4BQh6iS/HyrByXgyz8Os76T6naWcs973RiiCOA5nUQSz5G+fVagfZTsiP5WUsKcENwn/pPZOzDdMKV1PKf1aSun3U0qfSSn9lyfXN1JKv5JS+sLJ/8sn11NK6X9MKX0xpfSplNL7pm1wZLL4+7j7EHq/HqOnD5POowCiknM/sBTQGVfuo25niWFLivxBUFM/TfM3zfPTtP1hUFN/lT6nNNpU1DNkzxPHmCbp+EjS362q6l2S/oSkH0opvUvSByX9alVV75D0qyffJek7dbyt2Dt0vJHoj0/xjkbGaap8ExJospQPS0gmDdhZ2zXt+5rKeZiIZxoGfViC/6DoPNZ8Vtrj6Dal+vb48T6n+0YCVVW9WlXV75583tbxCUNPSvqApJ86ue2nJP3nJ58/IOmnq2P6iKRLKaUnJr2nicGi5S/9t7oWo++lMh4GnQcNnPXeaZ5/oyzWG/W+R0nj0Okb8W5/v8+YuSLgt/OimDPFBNLxISTvlfQfJF2rqurVk59ek3Tt5POTkl60x146ufaqzkCx0k0rpZosv/+fNCU1TZlnofjepvIiPI2fJ9F5g1YeN3FLctayIvONq8+D9KcnlTVu3JvuLZXvLuW09Z/UhyUj5Z+bplr9e+znqAzOSlMrgZTSio73D/w7VVVteSWqqqpSSmd6ewrnDkQqQRpnOh/oqA09aso94wY7voPPU7bj1OfQN43PTjojblwdphWE0nPO3A4rS35xZDx/Jiaj+O9+Dl48INV/b6qv1zG296xtnxYel/hrWmM0DU1S+g73oXiKEH3PvVxj74ymg3nH1XuqFqWU5nSsAH6mqqoPn1y+Acw/+f/6yfWXJV23x586uVajqqp+oqqq91dV9f5ut1t656nP46x+ofyZgKlRIfA3zVHiZyl7ElwdZ4HGHVc1zkWb5i/WM77/rPV9EDROCGedYh2n6af7jgmk4xJ+UtJnq6r6J/bTL0n6wZPPPyjpX9j1H0jH9Cck3TO3YSpqYqgmyOTPla6N0/wPk6IwULcHpQDGta3Jqkfy46p8h+ZpFK7D0LhDUVM9J6GNWM9x8Ph+yd9b+ot1eNDvPy+VFEFTHadBttO4A39K0l+R9OmU0idPrv09ST8q6edTSn9D0h/q+GBSSfplSd8l6YuSepL+2hTvOFVpPjcpAqfot43TiE0Q82FSibnuF6k4PCz9j/d6m5vclVjmOLju12NqdYSuTUqvJFTjFMCkutwPjTMm5/W1HzSdRSGepb7TnDvwf0pq6vVvL9xfSfqhqWtQoHHQctJzLmxx2zG/542ghwkzp9HwTcJfUkAlZTsJDZSUahScWOYkwS+Nz8NwDZreMS2vPSpqqndT30qTlebMZQxCzjylU2q5Zxpt3aQA3giFcN6BOStNExspfS7186R3jBPUUv+WBGtaaP0w0MA0boe/w5HmLCiISX1/1n66vx0K3wA6CxqIMHcchH0jBrPk/58F2UyiactqEjb6yP35pjLHCXHJGrnCmIRWpq3rw6ImxFZq46zQWeozaQxmBgkcHh5qbm4un7yaUn3fNBjL0yRLvqsvVfWts84yoOOgdhSS0vZcJWj2MF2CcZZg3LOROVBa07hOk94Rf/fYAX1DINIt7bh2TKM4m1DXWfp/3L3RHZr0zDgUOAl9TNPHkfc5jchjMSmlvAy5RDOhBBB4abRuvdPp5D3WvYPinDbUBDuneXfT9XHWMA5WiYnv5/3TPjutJZ30bq/7WdFKCSrHZz0hyY/kPk/9pxGO88SA4hi64vLfm4zDtK5p5NuzGqdpyvYYzKQ+mAklUFVVPmSxqqpT0PR+tWy83vTZ7y0xkd/jVmxcvWbBhxwXL5h0b9P32EcuIIyhvye6DPH9Ufia6jutUJ/l3nHP+qaetKspRvAgxrqEACYJf3z+rDQTSkBSPoDRz01DMfiBC+MsVJPSKAV4/Pq0VEIepf/++8OkaWF/6bPXc9rzB6KbNM5tKlm7+Of3TRM3cOs2LZ13DEpjHct1hfBG0CSldl5UOBNKIAopkNG3U4owtcTQZ4WxsQ4lCzXOf5sFmtZvbhLwaM2b+q8kBBEtNSnDcUI/CXmctd+b0M15UUEsN6KDcS7Ced8TFd15kEDT/xLNhBIYDofa2trS0dGRlpaWamers7FitOBRCUQmLsUMSp+jb1+qW9Mzj5pKCmASxG8S8JISmEYZNFFUEAiPB26ndVPOKrznRXrTELxI4G3StOqjpDcVEhgMBrpz5462trby2WuLi4v5UMa4+2qTLy+VA3rjgi/OpCVFMa1PHH9/VDTu/bGepb9xq9FKgt2EoKAmN2ASYjuv1QZFxjacZVymqVfToS33ixKbnp+m/ud990wogZSOpzD6/b56vZ729va0uLio+fl5ra2taW1tTZJq2neaoJZUhkPj/NVJ9Wy6VkIms0QlBeCfS0hgnBBNC/+51rTisERNyqRU/2me9eemGeNJLklVVTlu9UaM9XmUy5vOHZCUj2MeDAba399XVR2fttLv97Wzs6P5+XktLi5qeXlZ3W43b8FMYKvT6dQsFHOmcRlm/D+tMpnkM59XC48TtFLc436pCQ3EHWomBaB8mtaRWslVK93ThMioiz/XVIemsfDj5uM98XNUYpPaHvsobmk3iTfOozB4Pp49GF1VlBN93el0dHh4WEQtTjOhBCIMJSbAtcPDw3xPp9PR/Py8Ukp59ZukrAS8DJREE4QvQf4mf3gcM01qWxNNA/2aGPVBklv+JgVZElquT2Ntpq1HtKzTKNyIXKYZT+6P75ymDfHeBxkvOs84l/gl/jbzSkAaKQIyAjudTv7swRefNkTjxaxCt24lReDvjHWYpp5vJD0IwZ9UZ4fo/s5xzD7OjWp6XxMKeJB0XiGa9pmSq+B9V0Iu98Mz45RT6bfz9O1MKAEXXGYFmBngc6fTyZ/9XhSEKw1pdHa7r5MfZyHOohzeaEXg771fKzGOxgXtSj5mKdjn9/h9sU6T6jUOAY1DayUffZxS8rqfF3GV0FDJPQDGT0OT2l36/bzKdSaUQFVV+QglhBkB57MrBN9l1TdejD5aU7BvGgXwoNyBB03nfe95IPYkgWj6rQTLvdwoePHekmA2ve+8aK6pDtME+py3Ip811fEs41ZSSOOUwqT3Tnr/zKwidN/e3QDPZivtXhPjB5G8nFJnTOs/lu5/2NQEt89KZ1Ve0cpPE7s4y3UfhyZF8SCuj6OmPjlrOREZTXPvtPXi+1mu8ed8X9qW3GlmkACar9PpaG5urtYQjxqXUogRcJ+24TliB+O04nmZ+Y2kprjGWaiJ2UvWtsmyTUJRXs6kgNk0lves7kCkcYJXQiLTIIFx74jPnido2oR8xiEi/z3KxqR3z4QSkEadD+SX6kzlWYTuFvjz/uf+17S58X59EoNNgsoPix62OzAJep9FCTQJ8CRYPynQGIUu1vksdXgY41h6/7TjNslVmeZ9JSM59p2PgpFPVSKlm5J2Jd161HW5D3pMb+76S2/+NrzZ6y893Da8raqqq/HiTCgBSUopfayqqvc/6nqcl97s9Zfe/G14s9dfejRtmJnA4AVd0AU9GrpQAhd0QV/lNEtK4CcedQXuk97s9Zfe/G14s9dfegRtmJmYwAVd0AU9GpolJHBBF3RBj4AeuRJIKf3FlNLnUkpfTCl98FHXZ1pKKT2fUvp0SumTKaWPnVzbSCn9SkrpCyf/Lz/qejqllD6UUno9pfScXSvWOR3T/3gyLp9KKb3v0dU817VU/3+QUnr5ZBw+mVL6LvvtR07q/7mU0n/6aGo9opTS9ZTSr6WUfj+l9JmU0n95cv3RjkEpRfSN+pPUlvQlSW+XNC/p9yS961HW6Qx1f17SY+Hafy/pgyefPyjpv3vU9Qz1+7OS3ifpuUl11vF5kv9KUpL0JyT9hxmt/z+Q9P8u3PuuE35akPTsCZ+1H3H9n5D0vpPPq5I+f1LPRzoGjxoJfIukL1ZV9eWqqg4k/ZykDzziOt0PfUDST518/ilJ//mjq8ppqqrqNyTdCZeb6vwBST9dHdNHJF1KJ0fRPypqqH8TfUDSz1VVtV9V1Vd0fEDutzy0yk1BVVW9WlXV75583pb0WUlP6hGPwaNWAk9KetG+v3Ry7c1AlaR/m1L6eErpb55cu1aNjmF/TdK1R1O1M1FTnd9MY/PDJ3D5Q+aCzXT9U0rPSHqvpP+gRzwGj1oJvJnpT1dV9T5J3ynph1JKf9Z/rI7x3Jtq6uXNWGdJPy7payS9R9Krkn7skdZmCkoprUj6BUl/p6qqLf/tUYzBo1YCL0u6bt+fOrk281RV1csn/1+X9M91DDVvANdO/r/+6Go4NTXV+U0xNlVV3aiqalBV1VDSP9MI8s9k/VNKczpWAD9TVdWHTy4/0jF41Ergo5LekVJ6NqU0L+n7JP3SI67TREopdVNKq3yW9J9Iek7Hdf/Bk9t+UNK/eDQ1PBM11fmXJP3ASYT6T0i6Z5B1Zij4yH9Jx+MgHdf/+1JKCymlZyW9Q9LvvNH1c0rHy/1+UtJnq6r6J/bTox2DRxkttQjo53Ucvf37j7o+U9b57TqOPP+epM9Qb0lXJP2qpC9I+j8kbTzquoZ6/6yOIfOhjv3Lv9FUZx1HpP/nk3H5tKT3z2j9//8n9fvUidA8Yff//ZP6f07Sd85A/f+0jqH+pyR98uTvux71GFxkDF7QBX2V06N2By7ogi7oEdOFErigC/oqpwslcEEX9FVOF0rggi7oq5wulMAFXdBXOV0ogQu6oK9yulACF3RBX+V0oQQu6IK+yun/AkSEY7oY7DZnAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pneumonia_sample = os.listdir(pneumonia_path)[0]\n",
        "plt.imshow(imread(os.path.join(pneumonia_path, pneumonia_sample)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJHJ8-ASbifw",
        "outputId": "4bb765d8-ba67-4215-ded6-a412955adad3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fcf6ff1e940>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_sample = os.listdir(normal_path)[0]\n",
        "plt.imshow(imread(os.path.join(normal_path, normal_sample)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0_2OrYqbowS",
        "outputId": "b6d24837-1f2f-4b55-87db-15622efe1c86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fcf6fea07c0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create figure with three subplots in a row\n",
        "fig, axs = plt.subplots(1, 3, figsize=(10, 5))\n",
        "\n",
        "# Plot each image in a separate subplot\n",
        "axs[0].imshow(imread(os.path.join(pneumonia_path, pneumonia_sample)))\n",
        "axs[1].imshow(imread(os.path.join(covid_path, covid_sample)))\n",
        "axs[2].imshow(imread(os.path.join(normal_path, normal_sample)))\n",
        "\n",
        "# Set title for each subplot\n",
        "axs[0].set_title('Pneumonia')\n",
        "axs[1].set_title('COVID-19')\n",
        "axs[2].set_title('Normal')\n",
        "\n",
        "# Display the figure\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZQYL6YAt59-",
        "outputId": "7035addd-20e0-4eab-f779-1c59298edf7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exploratory statistical analysis\n",
        "\n",
        "# Get class names and counts\n",
        "class_names = train_data.classes\n",
        "class_counts = np.bincount(train_data.targets)\n",
        "print(class_counts)\n",
        "\n",
        "# Create bar chart for training data distrib b4 apply weighted sampler\n",
        "fig, ax = plt.subplots()\n",
        "ax.bar(class_names, class_counts)\n",
        "ax.set_xlabel('Class')\n",
        "ax.set_ylabel('Count')\n",
        "ax.set_title('Distribution of Classes in Train Data')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "from torchvision.transforms import ToPILImage\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "SnOWV23jbgyF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec424bc9-f17d-4a3c-b16e-3c257316e5d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 460 1266 3419]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  Compute mean and standard deviation of ALL images( no sampler applied)\n",
        "mean = 0.0\n",
        "std = 0.0\n",
        "to_pil = ToPILImage()\n",
        "for img, _ in train_data:\n",
        "    img = to_pil(img)  # Convert tensor to PIL Image\n",
        "    mean += torch.mean(TF.to_tensor(img))\n",
        "    std += torch.std(TF.to_tensor(img))\n",
        "\n",
        "mean /= len(train_data)\n",
        "std /= len(train_data)\n",
        "\n",
        "print(f'Mean: {mean:.4f}, Std: {std:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iTPS0myDRx-5",
        "outputId": "cda07137-6935-49a7-93be-332cb0cfae13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: 0.5046, Std: 0.2972\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exploratory statistical analysis\n",
        "\n",
        "class_names = train_data.classes\n",
        "\n",
        "\n",
        "# Create bar chart for training data distrib after apply weighted sampler\n",
        "fig, ax = plt.subplots()\n",
        "ax.bar(class_names, train_freqs_after_sampler)\n",
        "ax.set_xlabel('Class')\n",
        "ax.set_ylabel('Count')\n",
        "ax.set_title('Distribution of Classes in Train Data After Weighted Re-Sampling')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMAxeqpdQQBm",
        "outputId": "f85ebde5-dac4-426a-f1b6-ef532fa2663d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Separate code and import for baseline model"
      ],
      "metadata": {
        "id": "vvAwEdSuaTh4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get install git -y\n",
        "!git clone https://github.com/KevinGe00/Pneumonia-CNN-Classifier.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6qxqLm8aNym",
        "outputId": "0b04bd8e-0a5d-459f-b71d-b1d57970f7e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "git is already the newest version (1:2.25.1-1ubuntu3.10).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 23 not upgraded.\n",
            "Cloning into 'Pneumonia-CNN-Classifier'...\n",
            "remote: Enumerating objects: 6430, done.\u001b[K\n",
            "remote: Counting objects: 100% (6430/6430), done.\u001b[K\n",
            "remote: Compressing objects: 100% (6426/6426), done.\u001b[K\n",
            "remote: Total 6430 (delta 6), reused 6417 (delta 1), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (6430/6430), 40.96 MiB | 21.41 MiB/s, done.\n",
            "Resolving deltas: 100% (6/6), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from sklearn import svm, metrics\n",
        "\n",
        "# Define the paths to the train and test datasets\n",
        "train_path = '/content/Pneumonia-CNN-Classifier/Data_new/train'\n",
        "test_path = '/content/Pneumonia-CNN-Classifier/Data_new/test'\n",
        "\n",
        "# Load the training images and their labels\n",
        "train_data = []\n",
        "train_labels = []\n",
        "for class_label in os.listdir(train_path):\n",
        "    if os.path.isdir(os.path.join(train_path, class_label)):\n",
        "        for filename in os.listdir(os.path.join(train_path, class_label)):\n",
        "            if filename.endswith('.jpg'):\n",
        "                img_path = os.path.join(train_path, class_label, filename)\n",
        "                img = Image.open(img_path)\n",
        "                train_data.append(np.array(img).reshape(-1))\n",
        "                train_labels.append(class_label)\n",
        "                \n",
        "# Convert the training data and labels to numpy arrays\n",
        "train_data = np.array(train_data)\n",
        "train_labels = np.array(train_labels)\n",
        "\n",
        "# Load the testing images and their labels\n",
        "test_data = []\n",
        "test_labels = []\n",
        "for class_label in os.listdir(test_path): # COVID19, NORMAL, PNEUMONIA\n",
        "    if os.path.isdir(os.path.join(test_path, class_label)):\n",
        "        for filename in os.listdir(os.path.join(test_path, class_label)):\n",
        "            if filename.endswith('.jpg'):\n",
        "                img_path = os.path.join(test_path, class_label, filename)\n",
        "                img = Image.open(img_path)\n",
        "                test_data.append(np.array(img).reshape(-1))\n",
        "                test_labels.append(class_label)\n",
        "                \n",
        "# Convert the testing data and labels to numpy arrays\n",
        "test_data = np.array(test_data)\n",
        "test_labels = np.array(test_labels)\n",
        "\n",
        "# Define the SVM classifier\n",
        "clf = svm.SVC(kernel='linear', C=1, decision_function_shape='ovr')\n",
        "\n",
        "# Train the classifier using the training data\n",
        "clf.fit(train_data, train_labels)\n",
        "\n",
        "# Predict the classes of the test data\n",
        "y_pred = clf.predict(test_data)\n",
        "\n",
        "# Print the classification report\n",
        "print(metrics.classification_report(test_labels, y_pred))"
      ],
      "metadata": {
        "id": "hdgwZJ1GaStZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Primary Model\n",
        "Run Kai's data augmentation cells first, to obtain train_loader and test_loader. Test loader is actually our valid_loader, we'll talk about changing the variable names later\n"
      ],
      "metadata": {
        "id": "q4wAM7U4vgHu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# add imports needed for primary model and training\n",
        "# doesnt matter if duplicated from above, this is more\n",
        "# transparent\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchvision.models as models\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "jlKM3B5tviYo"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Artifical Neural Network Architecture\n",
        "class ANNClassifier(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ANNClassifier, self).__init__()\n",
        "        self.fc1 = nn.Linear(1024 * 7 * 7, 10)\n",
        "        self.fc2 = nn.Linear(10, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(-1, 1024 * 7 * 7) #flatten feature data\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "4IXaWcbbvi_U"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model_path(num_epochs, lr, comment=\"\"):\n",
        "    path = f\"model_{num_epochs}_{lr}\"\n",
        "    if comment:\n",
        "      path += \"_\" + comment\n",
        "    return path"
      ],
      "metadata": {
        "id": "GSxlSkZbvjGN"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.models.densenet import densenet121\n",
        "# Load the pre-trained densenet121 model\n",
        "densenet = models.densenet121(pretrained=True)\n",
        "\n",
        "# Freeze all the layers in the pre-trained model\n",
        "for param in densenet.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        " \n",
        "''''num_ftrs = densenet.classifier.in_features\n",
        "densenet.classifier = nn.Sequential(\n",
        "    nn.Linear(num_ftrs, 512),\n",
        "    nn.ReLU(inplace=True),\n",
        "    nn.Linear(512, 2)\n",
        ")'''\n",
        "#early dropout\n",
        "#dropout_prob = 0.5\n",
        "#densenet.features.add_module(\"dropout\", nn.Dropout(p=dropout_prob))\n",
        "\n",
        "# Move densenet to the GPU (if available)\n",
        "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "densenet.to(DEVICE)\n",
        "\n",
        "\n",
        "def get_accuracy(model, data_loader):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for imgs, labels in data_loader:\n",
        "        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)\n",
        "        output = model(densenet.features(imgs))\n",
        "        pred = output.max(1, keepdim=True)[1] # get the index of the max logit\n",
        "        correct += pred.eq(labels.view_as(pred)).sum().item()\n",
        "        total += imgs.shape[0]\n",
        "    return correct / total\n",
        "\n",
        "# Define the training loop, using densenet defined above\n",
        "def train_model(model, train_loader, valid_loader, num_epochs=20, lr=0.001, batch_size=32, checkpoint_path='', comment=\"\"):\n",
        "    print(f\"Starting training on device: {DEVICE}\")\n",
        "    # Define the loss function and optimizer\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = optim.Adam(model.parameters(), lr=lr)\n",
        "\n",
        "    # Variables for logging\n",
        "    iters, losses, train_acc, valid_acc = [], [], [], []\n",
        "    best_valid_acc = 0.0\n",
        "    num_iter = 0\n",
        "    start_time=time.time()\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        mini_b=0\n",
        "        mini_batch_correct = 0\n",
        "        Mini_batch_total = 0\n",
        "        for i, data in enumerate(train_loader, 0):\n",
        "\n",
        "            inputs, labels = data\n",
        "            inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)  # enable GPU if available\n",
        "            outputs = model(densenet.features(inputs))  # go through densenet's CNN first before passing output to our model\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()         # a clean up step for PyTorch\n",
        "\n",
        "            # Mini_batch Accuracy. We don't compute accuracy on the whole training set in every iteration!\n",
        "            pred = outputs.max(1, keepdim=True)[1]\n",
        "            mini_batch_correct = pred.eq(labels.view_as(pred)).sum().item()\n",
        "            Mini_batch_total = inputs.shape[0]\n",
        "            train_acc.append((mini_batch_correct / Mini_batch_total))\n",
        "\n",
        "            # Log \n",
        "            iters.append(num_iter)\n",
        "            losses.append(float(loss)/batch_size)             # compute *average* loss\n",
        "            valid_acc.append(get_accuracy(model, valid_loader))  # compute validation accuracy\n",
        "\n",
        "            num_iter += 1\n",
        "            mini_b += 1\n",
        "            print(\"Iteration: \",num_iter,'Progress: % 6.2f ' % ((epoch * len(train_loader) + mini_b) / (num_epochs * len(train_loader))*100),'%', \"Time Elapsed: % 6.2f s \" % (time.time()-start_time), \"Train Acc: % 6.2f \" % train_acc[-1], \"Valid Acc: % 6.2f \" % valid_acc[-1])\n",
        "        \n",
        "        print (\"Epoch %d Finished. \" % epoch ,\"Time per Epoch: % 6.2f s \"% ((time.time()-start_time) / (epoch +1)))\n",
        "\n",
        "        # Save model if best so far\n",
        "        if valid_acc[-1] > best_valid_acc:\n",
        "          best_valid_acc = valid_acc[-1]\n",
        "          model_path = get_model_path(num_epochs, lr, comment)\n",
        "          torch.save(model.state_dict(), model_path)\n",
        "\n",
        "\n",
        "    # plotting\n",
        "    plt.title(\"Training Curve\")\n",
        "    plt.plot(losses, label=\"Train\")\n",
        "    plt.xlabel(\"Epoch\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.show()\n",
        "\n",
        "    plt.title(\"Training Curve\")\n",
        "    plt.plot(train_acc, label=\"Train\")\n",
        "    plt.plot(valid_acc, label=\"Validation\")\n",
        "    plt.xlabel(\"Epoch\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()\n",
        "\n",
        "    print(\"Final Train Acc: \", train_acc[-1])\n",
        "    print(\"Final Val Acc: \", valid_acc[-1])\n"
      ],
      "metadata": {
        "id": "HOkEczBwwEv2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158,
          "referenced_widgets": [
            "dc5e6381f69e4829b8ece6c97298c0c1",
            "abb14797247948d2bbbde6b008d19202",
            "b50b00d0aa1f454496d96ce0b40fd81a",
            "47cc5121f1b047d4b119070ffde1dec0",
            "9b13c285a7e4495e8050cc05c08567f4",
            "64ab73a64ac843bba5e12b94e5ebb60e",
            "569d511c0b5a4b5bbf3510417cea14f1",
            "c1d9b2c85d0145629595c37b4894cfc9",
            "6014599378cf42d894d73e1b16941710",
            "b6c821a7eecc4a968b52adf7f811132a",
            "a012d9ec82c94c93b01559b20e540e91"
          ]
        },
        "outputId": "a79b4efc-d351-44fc-e81e-c7218373ea26"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=DenseNet121_Weights.IMAGENET1K_V1`. You can also use `weights=DenseNet121_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/densenet121-a639ec97.pth\" to /root/.cache/torch/hub/checkpoints/densenet121-a639ec97.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/30.8M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc5e6381f69e4829b8ece6c97298c0c1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the output of densenet, so we can configure input of our ANN above accordingly\n",
        "# You don't have to run this, it's only to help me set up our model\n",
        "# dataiter = iter(train_loader)\n",
        "# images, labels = next(dataiter)\n",
        "# features = densenet.features(images)\n",
        "# features.shape  # this output is what the ANN should receive"
      ],
      "metadata": {
        "id": "a5YVXsBwwF3f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataiter = iter(train_loader)\n",
        "for images, labels in dataiter:\n",
        "  # features = densenet.features(images)\n",
        "  print(images.shape)  # this output is what the ANN should receive"
      ],
      "metadata": {
        "id": "RGAXs_NUwGR_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a513a64-1a78-43ce-d284-7f1a053951a8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
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            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
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            "torch.Size([32, 3, 224, 224])\n",
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            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
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            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
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            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
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            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([32, 3, 224, 224])\n",
            "torch.Size([25, 3, 224, 224])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch. cuda. is_available()"
      ],
      "metadata": {
        "id": "gD3CNoHxNcJP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad9addec-a247-4eb8-d076-a445b518f12e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model = ANNClassifier()\n",
        "model.to(DEVICE)\n",
        "# def train_model(model, train_loader, valid_loader, num_epochs=20, lr=0.001, batch_size=32, checkpoint_path='', comment=\"\"):\n",
        "train_model(model, train_loader, test_loader, num_epochs=3, batch_size= 32)"
      ],
      "metadata": {
        "id": "4FW3lSIswHEI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "305cc078-7112-44ba-f089-ffa4feca508a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting training on device: cuda\n",
            "Iteration:  1 Progress:   0.21  % Time Elapsed:   6.63 s  Train Acc:   0.47  Valid Acc:   0.35 \n",
            "Iteration:  2 Progress:   0.41  % Time Elapsed:  14.22 s  Train Acc:   0.25  Valid Acc:   0.40 \n",
            "Iteration:  3 Progress:   0.62  % Time Elapsed:  20.06 s  Train Acc:   0.41  Valid Acc:   0.45 \n",
            "Iteration:  4 Progress:   0.83  % Time Elapsed:  26.92 s  Train Acc:   0.53  Valid Acc:   0.43 \n",
            "Iteration:  5 Progress:   1.04  % Time Elapsed:  32.68 s  Train Acc:   0.53  Valid Acc:   0.45 \n",
            "Iteration:  6 Progress:   1.24  % Time Elapsed:  39.47 s  Train Acc:   0.22  Valid Acc:   0.43 \n",
            "Iteration:  7 Progress:   1.45  % Time Elapsed:  45.20 s  Train Acc:   0.34  Valid Acc:   0.44 \n",
            "Iteration:  8 Progress:   1.66  % Time Elapsed:  51.90 s  Train Acc:   0.47  Valid Acc:   0.43 \n",
            "Iteration:  9 Progress:   1.86  % Time Elapsed:  57.59 s  Train Acc:   0.53  Valid Acc:   0.43 \n",
            "Iteration:  10 Progress:   2.07  % Time Elapsed:  63.91 s  Train Acc:   0.34  Valid Acc:   0.48 \n",
            "Iteration:  11 Progress:   2.28  % Time Elapsed:  69.79 s  Train Acc:   0.44  Valid Acc:   0.48 \n",
            "Iteration:  12 Progress:   2.48  % Time Elapsed:  75.65 s  Train Acc:   0.47  Valid Acc:   0.47 \n",
            "Iteration:  13 Progress:   2.69  % Time Elapsed:  81.94 s  Train Acc:   0.41  Valid Acc:   0.45 \n",
            "Iteration:  14 Progress:   2.90  % Time Elapsed:  87.75 s  Train Acc:   0.62  Valid Acc:   0.46 \n",
            "Iteration:  15 Progress:   3.11  % Time Elapsed:  94.37 s  Train Acc:   0.50  Valid Acc:   0.46 \n",
            "Iteration:  16 Progress:   3.31  % Time Elapsed:  100.14 s  Train Acc:   0.41  Valid Acc:   0.50 \n",
            "Iteration:  17 Progress:   3.52  % Time Elapsed:  107.07 s  Train Acc:   0.44  Valid Acc:   0.50 \n",
            "Iteration:  18 Progress:   3.73  % Time Elapsed:  112.79 s  Train Acc:   0.59  Valid Acc:   0.50 \n",
            "Iteration:  19 Progress:   3.93  % Time Elapsed:  119.82 s  Train Acc:   0.56  Valid Acc:   0.49 \n",
            "Iteration:  20 Progress:   4.14  % Time Elapsed:  125.52 s  Train Acc:   0.50  Valid Acc:   0.48 \n",
            "Iteration:  21 Progress:   4.35  % Time Elapsed:  132.35 s  Train Acc:   0.44  Valid Acc:   0.51 \n",
            "Iteration:  22 Progress:   4.55  % Time Elapsed:  138.05 s  Train Acc:   0.44  Valid Acc:   0.52 \n",
            "Iteration:  23 Progress:   4.76  % Time Elapsed:  145.04 s  Train Acc:   0.47  Valid Acc:   0.51 \n",
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            "Epoch 2 Finished.  Time per Epoch:  1003.38 s \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Train Acc:  0.8\n",
            "Final Val Acc:  0.8369565217391305\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_accuracy_depth(model, data_loader):\n",
        "    correct = [0, 0, 0]  \n",
        "    total = [0, 0, 0]  \n",
        "    for imgs, labels in data_loader:\n",
        "       # print(imgs)\n",
        "        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)\n",
        "        output = model(densenet.features(imgs))\n",
        "        pred = output.max(1, keepdim=True)[1]\n",
        "        for i in range(len(labels)):\n",
        "            #print(labels[i])\n",
        "            if pred[i] == labels[i]:\n",
        "                correct[labels[i]] += 1\n",
        "            total[labels[i]] += 1\n",
        "    accuracy = [c/t for c, t in zip(correct, total)]\n",
        "    return accuracy\n"
      ],
      "metadata": {
        "id": "SGz7PSjEnz_A"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(get_accuracy_depth(model, final_test_loader))\n",
        "print(get_accuracy_depth(model, test_loader))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "vSczpbGwjI0-",
        "outputId": "d6ed90d1-50eb-42df-f1aa-454f208fbb79"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-08c9f0dd430a>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_accuracy_depth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal_test_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_accuracy_depth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    }
  ]
}