DeepLense_Test_I_MultiClass_Classification.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Vamsi995/DeepLense-ML4SCI-EvalTest/blob/main/DeepLense_Test_I_MultiClass_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdIIiAVg4GAZ",
        "outputId": "b39da1b7-d644-4bed-84ec-4a58ee3d1cb1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fceb2471bb0>"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# Imports\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms\n",
        "from tqdm.notebook import tqdm\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd\n",
        "import time\n",
        "import torch.nn.functional as F\n",
        "from sklearn.metrics import RocCurveDisplay\n",
        "from itertools import cycle\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "seed = 43\n",
        "torch.manual_seed(seed)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Utilities\n"
      ],
      "metadata": {
        "id": "Uu_CckZX1zpS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class SubstructureData:\n",
        "  \"\"\"\n",
        "    Substructure Data Class is a wrapper class that helps in handling data manipulation.\n",
        "  \"\"\"\n",
        "  def __init__(self, paths):\n",
        "    self.paths = paths\n",
        "    self.train_df = None\n",
        "    self.val_df = None\n",
        "    self.test_df = None\n",
        "    self.initialize_data_dict()\n",
        "\n",
        "  def initialize_data_dict(self):\n",
        "    \"\"\"\n",
        "      Initialize data dict\n",
        "    \"\"\"\n",
        "\n",
        "    self.data = {}\n",
        "\n",
        "    for stage in self.paths.keys():\n",
        "      self.data[stage] = {}\n",
        "\n",
        "    for stage, value_dict in self.paths.items():\n",
        "      for substrc_key, path in value_dict.items():\n",
        "        self.data[stage][substrc_key] = []\n",
        "\n",
        "  def load_data(self):\n",
        "    \"\"\"\n",
        "      Load Data from disk\n",
        "    \"\"\"\n",
        "    for stage, value_dict in self.paths.items():\n",
        "      for substrc_key, path in value_dict.items():\n",
        "        for substrc in tqdm(os.listdir(path), desc=\"Reading {} {} Images from {}\".format(stage, substrc_key, path)):\n",
        "          substrc_image = np.load(path + substrc, allow_pickle=True)\n",
        "          self.data[stage][substrc_key].append(np.float32(substrc_image))\n",
        "\n",
        "        print(\"No. of {} {} images loaded: {}\".format(stage, substrc_key, len(self.data[stage][substrc_key])))\n",
        "\n",
        "  def construct_dataframes(self):\n",
        "    \"\"\"\n",
        "      Construct dataframes from the loaded data\n",
        "    \"\"\"\n",
        "    train_nosub_dict = {\"Images\": self.data[\"train\"][\"nosub\"],\n",
        "              \"Labels\": [0]*len(self.data[\"train\"][\"nosub\"]) \n",
        "              }\n",
        "    train_vortex_dict = {\"Images\": self.data[\"train\"][\"vortex\"],\n",
        "                  \"Labels\": [1]*len(self.data[\"train\"][\"vortex\"]) \n",
        "                  }\n",
        "    train_sphere_dict = {\"Images\": self.data[\"train\"][\"sphere\"],\n",
        "                  \"Labels\": [2]*len(self.data[\"train\"][\"sphere\"]) \n",
        "                  }\n",
        "\n",
        "    val_nosub_dict = {\"Images\": self.data[\"val\"][\"nosub\"],\n",
        "                  \"Labels\": [0]*len(self.data[\"val\"][\"nosub\"]) \n",
        "                  }\n",
        "    val_vortex_dict = {\"Images\": self.data[\"val\"][\"vortex\"],\n",
        "                  \"Labels\": [1]*len(self.data[\"val\"][\"vortex\"]) \n",
        "                  }\n",
        "    val_sphere_dict = {\"Images\": self.data[\"val\"][\"sphere\"],\n",
        "                  \"Labels\": [2]*len(self.data[\"val\"][\"sphere\"]) \n",
        "                  }\n",
        "\n",
        "\n",
        "    train_nosub_df = pd.DataFrame(train_nosub_dict)\n",
        "    train_vortex_df = pd.DataFrame(train_vortex_dict)\n",
        "    train_sphere_df = pd.DataFrame(train_sphere_dict)\n",
        "\n",
        "    val_nosub_df = pd.DataFrame(val_nosub_dict)\n",
        "    val_vortex_df = pd.DataFrame(val_vortex_dict)\n",
        "    val_sphere_df = pd.DataFrame(val_sphere_dict)\n",
        "\n",
        "    self.train_df = pd.concat([train_nosub_df, train_vortex_df, train_sphere_df], axis=0)\n",
        "    self.val_df = pd.concat([val_nosub_df, val_vortex_df, val_sphere_df], axis=0)\n",
        "\n",
        "    print(\"Total no. of training samples: {}\".format(len(self.train_df)))\n",
        "    print(\"Total no. of validation samples: {}\".format(len(self.val_df)))\n",
        "  \n",
        "  def calc_mean_std(self, dataset):\n",
        "    \"\"\"\n",
        "      Calculate mean and std for the entire dataset\n",
        "    \"\"\"\n",
        "    psum = 0\n",
        "    pstd = 0\n",
        "    for image in dataset:\n",
        "      image = self.min_max_filter(image)\n",
        "      psum += image.sum() \n",
        "      pstd += (image**2).sum()\n",
        "    \n",
        "    image = dataset.sample(n=1)[\"Images\"].values[0]\n",
        "    height = image.shape[1]\n",
        "    width = image.shape[2]\n",
        "    total_pixels = (len(dataset)*height*width)\n",
        "    mean = psum/total_pixels\n",
        "    std = np.sqrt((pstd/total_pixels) - (mean**2))\n",
        "\n",
        "    print(\"Mean: {}, Std: {}\".format(mean, std))\n",
        "    return mean, std\n",
        "\n",
        "  def train_test_val_split(self):\n",
        "    \"\"\"\n",
        "      Train test split for the data\n",
        "    \"\"\"\n",
        "\n",
        "    self.train_df, self.test_df = train_test_split(self.train_df, test_size=0.1, random_state=43, stratify=self.train_df[\"Labels\"])\n",
        "    print(\"No. of training data samples: {}\".format(len(self.train_df)))\n",
        "    print(\"No. of testing data samples: {}\".format(len(self.test_df)))\n",
        "    print(\"No. of validation data samples: {}\".format(len(self.val_df)))\n"
      ],
      "metadata": {
        "id": "Mm0RueGK1y4s"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Data"
      ],
      "metadata": {
        "id": "GT9SniUD6pKq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Importing data by copying the dataset to google drive and mounting google drive to the colab notebook. \n",
        "\n",
        "- Please try to access this notebook via colab.\n"
      ],
      "metadata": {
        "id": "siMlRYjGYVhL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNN3kiwMKTXu",
        "outputId": "2c39ba2b-750a-4276-8e90-8a9dcaf62127"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Connecting to my google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The path (/drive/MyDrive/ML4SCI/dataset.zip) below to the dataset is the path in my google drive, please change it accordingly and point it to the dataset path in your drive."
      ],
      "metadata": {
        "id": "dLZrmupoSsMn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create images/ directory\n",
        "!mkdir images\n",
        "\n",
        "# Unzipping zip file\n",
        "!unzip \"/content/drive/MyDrive/ML4SCI/dataset.zip\" -d \"images/\""
      ],
      "metadata": {
        "id": "tlR9ko43QjPd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c687705-6527-4b2c-b9e4-4a7d63f8a13b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Path to data on disk\n",
        "paths = {\n",
        "  \"train\": {\n",
        "    \"nosub\": \"/content/images/dataset/train/no/\",\n",
        "    \"vortex\": \"/content/images/dataset/train/vort/\",\n",
        "    \"sphere\": \"/content/images/dataset/train/sphere/\",\n",
        "  },\n",
        "\n",
        "  \"val\": {  \n",
        "    \"nosub\": \"/content/images/dataset/val/no/\",\n",
        "    \"vortex\":  \"/content/images/dataset/val/vort/\",\n",
        "    \"sphere\": \"/content/images/dataset/val/sphere/\"\n",
        "  }\n",
        "}\n"
      ],
      "metadata": {
        "id": "3jaoaST-SWMx"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Custom data wrapper \n",
        "substrc_data = SubstructureData(paths)"
      ],
      "metadata": {
        "id": "KnNbQwb4dGsB"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data from disk\n",
        "substrc_data.load_data()"
      ],
      "metadata": {
        "id": "_5yF-bS5enIr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316,
          "referenced_widgets": [
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            "b5736b0e31bc4df4b562f92da5a3337e",
            "315c3fa76b4d4f61a0024e01d689f905",
            "89b132309442445cb6a8b4049b0205c8",
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            "cb91470c9f3f49368dd176500b711c5c",
            "e5b8710776184c178ca9ecabffb9b984",
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            "a7f9acb33466458087e81a62db001c75"
          ]
        },
        "outputId": "52725e2f-920d-482c-e452-f84d85e9782f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Reading train nosub Images from /content/images/dataset/train/no/:   0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea94949fe8824ec48a59701ece9d081d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of train nosub images loaded: 10000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Reading train vortex Images from /content/images/dataset/train/vort/:   0%|          | 0/10000 [00:00<?, ?it/s…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "37a634ff59744020a627bc67d8e9a317"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of train vortex images loaded: 10000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Reading train sphere Images from /content/images/dataset/train/sphere/:   0%|          | 0/10000 [00:00<?, ?it…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "95cf6c5274fd4343802be3ce43b4ef3c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of train sphere images loaded: 10000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Reading val nosub Images from /content/images/dataset/val/no/:   0%|          | 0/2500 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6fdef94f7bc47f493d8129192a8fe1f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of val nosub images loaded: 2500\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Reading val vortex Images from /content/images/dataset/val/vort/:   0%|          | 0/2500 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30dc427429d8420e85d9fdbf823f0d68"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of val vortex images loaded: 2500\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Reading val sphere Images from /content/images/dataset/val/sphere/:   0%|          | 0/2500 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ea7994b7f0a498c821dbc5a41203852"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of val sphere images loaded: 2500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Construct dataframes\n",
        "substrc_data.construct_dataframes()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RLpeR_XbkSKU",
        "outputId": "efcc2cf6-08ca-4145-9c48-b6aaf0e07399"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total no. of training samples: 30000\n",
            "Total no. of validation samples: 7500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Classes - Labels\n",
        "\n",
        "- No Substructure - 0\n",
        "- Vortex Substructure - 1\n",
        "- Sphere Substructure - 2"
      ],
      "metadata": {
        "id": "rOAX3n1RaAOt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Analysis\n"
      ],
      "metadata": {
        "id": "RTo5OgJI9Xj5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Image Analysis\n"
      ],
      "metadata": {
        "id": "tl579MVo9cqy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Image properties\n",
        "image = substrc_data.train_df.sample(n=1)[\"Images\"].values[0]\n",
        "print(\"Image Type: {}\".format(type(image)))\n",
        "print(\"Image Shape: {}\".format(image.shape))\n",
        "print(\"Image Data Type: {}\".format(image.dtype))\n",
        "print(\"Image Dimensions: {}\".format(image.ndim))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "he2Q2xWC9b0E",
        "outputId": "5c615e9f-aec7-419d-c553-97c81fe939ba"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image Type: <class 'numpy.ndarray'>\n",
            "Image Shape: (1, 150, 150)\n",
            "Image Data Type: float32\n",
            "Image Dimensions: 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pixel distribution\n",
        "sns.histplot(image.ravel(), bins=50).set(title=\"Pixel Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "vuIxqVrr-CVz",
        "outputId": "d52de227-00a7-4b7c-e873-efb452f2cf5e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Pixel Distribution')]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "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": [
        "# Image visualization\n",
        "plt.imshow(image[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "cXfF8wO4yjew",
        "outputId": "758da6f6-6011-45ef-9944-eda8701f7773"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f2861411df0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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MVJvTpXqa96k2pktWgnB9t+6eqtEIhthnD6pUnfI0kAAHWO6M9L4tNLK3FRxbG++/mL0mem4BAM/wwBQtkdABsGVZ5aJtejG06kMJLtkMosPerfnLVaHOYm3h+2ZXinCamp0T7aU1KVrFNDSsf+mvjqAz/b5n/ozWB00vtlUNsJm8m/Hc5EBQXxA08N714NEdw94Px9C7PrZ3rx5rV3wH/91Sfa17qsJOroRgJtwQgtaupq+KhSI61UXP2P3yNIQAPbybb+B/hHAhXxsAN/1UmVpscbb6mk20oNN5zrvgpVVNtNgeanP8BfjV7kph0m4ixy5cE9sbhV3VFuVR/NnYopK2H+37Vli16osLt7YONT1FT7iNAAlelq0KFSdx1yowVBI6XDf04/uN+7kwlzaEXthVKVGVo6+YpPfVr9heIAh3SiWoyX7fuw4dVSUK32hWNcHgQkvnQqP2unC9YDmx8kTUgVD2YKB80f9+7/oIgR8BuX2h92C8V7kNaGp36b37qhL15cDmVpP0A6g5sQ2XYHUX2m8bXd+3Z/2I1+3Vtwe5d6w+m7936t0ipH1rze5zLpTePIi7/4P1R12/sQaUS3aCqi619THvENTCS4Lx6QkBLT39DoB7rvVMLJX0bH+fGtDTTtzI4JsgiFA06LrIpe6qnVHgtIROC6eDxK8ERMPuxnRrdn3lNhqf3dlxvP1oJlsDfyuzU/y7NRny1nfgEkm1aX/8rbfzAdt2aT+097T8CA2DOGi16co6Klkl2OKz50WsAh3y+aGFWs3Bnf7w9lLj99BsWD2ButuXrYoVcxAMdWrzXvnA6gARfSeAPwDgswAYwO9l5t9DRJ8E8IcAfBeAnwXwm5n56xcr4/ACIUNvN648TIgIGyum1e6Jg9Lu4K53ogT3BH3fPK98ornpcGsVqBfGTqahRhp7fXER5wYaU+AsegxxeGd/10RFJw4JLWKQU1SnZOGH5CHRM3MoufRonEqSkRinrmy2PyuYZW0SAtjq98H7LjLvlbmsWRQ99cavo8bU2G4i0aKw0f9j23I9Jt0+b3bynWCodnzaeyTBin4XzZQBwrdek5t3sDlccSsdruEjsg4sAP4ZZv7VAH49gN9BRL8awA8D+OPM/L0A/rj+/XDhMEFNH+5dA5Sd9oJZaffeeF27iza7RnfSNc98jP65bUo9QR/b5scwvXbfhhRrGPW9XbjyzOTiVLSHLoCy81yCsZudrUfSdZj9vfIYDqlrWWp4gOr7B+rrjccuOmjK7rtEVNh7Zq+eOL4NCmjvfaifgA8hBJj5i8z8k/r5BYC/ADmD8AcB/Ihe9iMA/oH3XfkgGVXbzDZWbIc2YeGCo4FjtjNEOBlLZMM3zO7eYu8RZM3OJeTi0F3oPcjWe17VFv19T7/3a4MatLmnff+8Vu2t6rA619WvqyBsu2Da9zVhat91AlhK4tbwHh043VoPYn94m6k+KWpXtdibA+Xi0getWrR3T2ijPbsrgIJA9fe2ja/jXr5BQ53i1zVEcDdT0gUO4RtiHSCi7wLwtwH4cQCfZeYv6k+/CFEXHq6j1YVbU1Wje7cSuPzd6KfOfvdDcyOj307KaCGIz6Ch/A6g5MejTq5Db+++paCnyljbirkSXShr90chEyF4hK91vsOxgoq9iR1TWFUmVmWjC0cS2hOEARGBwS6AvJ1Umzj92UEQU6IqD2NULWJ7Nqw6gMoBp0OOVenpMgvHw7Qd53Yhh3ukLcYfFbPuJp1ZYPR7grtCvTucjrXFYL/3nwstRjm8NIOXy+R2Wz40MUhEzwH8BwD+KWZ+L/7GgkG7WIeIfoiIfoKIfmLGSb9ssgRVlW3hevmpdiqqBi/cswcPe9B4DxF027ZT9mB0u1M8arA4DnrD2u9c2wq0vef21J5KDYg7jaINv7+nP/sOF9p88dV2dtoIfePc6EDg7th2FkKfr9kKgHj9xfZ3UEfVzx3rh28u9m49dLVTojpQ+qMTFl5ueLDODyUEiGiCCIA/yMz/oX79JSL6nP7+OQBf7t1bHUhKx01nxskeYZQ/u4Fa+uUGMvcWeJz87USHTYSwkHqLpp103UHfg/otp3GB26h2r9J5m+u7pSfAmknSFQShtESY95mhrJ7OjTLR61Rudb2bZ0Y436CejdrQtK9+xaIqbu7VNm6Qz476dmkRPeaabhs5lzFsOKjeQq76vKpjy5G1gv5BIYYPZx0gAL8PwF9g5n8t/PRjAH4rgN+l///Hj6it/ss6guvB7sWcAyjsfqnAr6sg7M6OU+WFc9NQCfDoQfD4uUuyUUI5AUb+dj245TzDRGjzIZT+mPRz9jDmDReSyulFpmJsdsKeXhz1yjbEt/yBKnip0UNbqEwD6pTlEBUEgKRc57rNvJbAIofxVM7vqxykgoMXgDrO39oSVBbnjnqeoKpmRPWi7pyQW6FxqtpwNp3gtG7fU0LMgLU5XHWHxG6d4qq+Cm2K6kXFk3wEAUR/O4B/FMCfIaKf0u/+Bcji/8NE9NsB/ByA3/xwVQ1s9kFsOnpHV9rcuxmoFYQmEUVdSd2aOChxgXZ2zV3i6gL73Laj97yN3h/yBHSTjnAu/dVBIFFgbHbl6KXYUSHsml5gU/fdS8Or6yUqL36XNynENipGJRim7VgDm1z/rcOZcy5BuFQJbOMOvGcF8cVtHqKddti9Pb6KW/NeQaWbSNYuusntF3UbOfAAEUGZsLyAVj6wEGDm/w/aLbyUH3jf9fUGkRLao6DfT6km6CN1rvAwn3TVDtOrh7ZnF9ZVBXLOFinVTkKXYHlfjw0Tj2ob+UNlQyZGF9yOjl/abIircdltEUdAMLHtLTqJJOoltWLz3japewiPUuWItbuo7bPtsJ0MTC1RHO/b25wuvlPVx1uU1i3NTn+pXf250PFracrTiB1A3WHmrJIOkw9y65Medc0Kni614wt1gih8Z+oIiJgmq4bjgWVuA4MuEHUbyGjn0hk8DXC0fe4u9Itwk3M5it2fURa3Z7aJTPvmndivj8FH1q+uHrXv6fC8I+hCex1O29HcQd25GG49xBRa4f0pgeezHDDLJGpGJE3tvRRNbtTLVjDsOJvZ+EU1xJ5vqlErzO0erzeoIJ52nuoQ9vZA13aziHV2n9MTwJHjMPT4pPMJaNl05rL49wab2sg/AN1kE5w1nZSRgROhl5JJrt133Ckdnbvf6x/9lF5NfZU9uIWjIQFGPHa6K8E7kF+utedrW8KC3uSyG7aLetMPzW682cE7UJ7GETCrQFyAQL2IAppo6/XnddKhu0cfhX5dSqBPhTZy2oxfD5lUZYfNb/ukhePtONl8rZ4VUVYwS28TjtjnPQ/HGr31hEFEJA+VD20i/MaUPpSUP7fmq4uwt8eIh98eBcE+qhJ1sx21Qv7baWOEwt2fOyYilD6s+rJBN/G5G+a8Us9Q77q9EuvW6/35vfY33IV/3ouAiwijfa9Yz177drglatr9mLmypy485to+En2cObK9b++ZG8K6U54OEiBLAVXvHhFCdhN1oj78o5LKqdzru5Yy174rJaoOw4yF17U+5rmZfIWVHz3t+J4VofIHVym/2WEDdOzmIrC6IkS1nd+eGVnz8J0crhngKBEwkDjJtCx25u3Cx1riEqh+fvWegfyrd6q1csqp+kjrrY4erxtU91PPyuOCrCZg9xBaq6qUvkKd2QdbC9Nmh8+rqCYBrfZKtSADj7T12Ix6fAlTj9YuV48419YxDk5V3ARK7ZSnIQS4NgFVkC4OSE/yUXI4aPf6oFW2dYWRARabnhSFS4s8aiZ/2D4DqNSMLhm0Q+7Y9dGMtYcUNlYQv7exhkSmvzr5ONcqx7LUbQ6Lvadj2ntWz4mLKKoaRowNtUUmCgB7b1fbKq6iIWStz7sqTJ3FuYLUjem4UoXqH+rn9BZOsFRsiFUO5wGMPetUua99pqsNFzYHu76ydMgFfv/umHXa0ZYnog6g7vheJzYvttXttmWzqPfqtgHcYai7UKtZ1A4nt43o1tG9vhEAe++41R077wrIbh/vD6rCpl9tV3mI3ATqgKFOP3hJ/SAe6vV3q8Y0v+8y7dr2izD6ggrW9ttWr2768H2UrmXjoWseaF9d3VZ9e8ycacvTQAKEAreaHSbC4nb3FPjUySrEuQ4r1d3+wUHsmPouBnJEkigyyNUlOwghOLjsEoAXSoVGGoTkHMDaIKcOkrLkpnGH7rU/trtVOarMSqaCAVvTHTUIigrJaycU5fOKDRqJpkBjund223Ynr3dZ/a6F65FMjSpk2w+sZtKgQka+JZ4zaNf3EtJGolvevY6J8Hdunl8J1IhSO2qyPb9KwbdTnoYQgExQnguD2mM3Kwjpix1+vVwTd97twm+lJq8rqCGmio6ORg8LgSkx6GjA/sSxZ4YJH4WH6cGV3hmtGKa79rz09Pd6Vx/C85IIgs6uuVlkndLlBjKD13MZA1XZXIXD0BeIl/TToNJVKqHr7dmfEcfCF4WaTO3d2r5r33O7KwZP1Cr+rFbVXA+P5whajonGqhCD1ryd0XyXqJp3Vb9cIrdjtwVHoP48H0J/7NgH8YTUgceWzUS60GGXBED4sr/Lbyur72l/CxNuF8JFdaQH1Th34+y3JN0D7bOv1ke8F/qqya5VJqo3ob3tRH+/xfssLtQHrDnexki4xbZ0rB5VXzYqyJ76sbE07BCXvfKgOmF92pnPj0KHF9Sxx6LLpycEcicripWOTlsNcCcVlg18C5VqCb/jbcfteQbb1M6buq0uroOPuruU/VNVJbbDnxXVkUuwrtWpG5WovrRh2ZuF51C2eV7Vfr2Ol7lcZ/favwv3VuMY29hZvFW7OzA59nmsf5NaKyz86oDU6vthSxzCEF8/0U18L0rkSKinn1fvG+ZEJC17/NjmPfbGda9fL5Qnog6EBZ3G7U4adyJg42GFvIJRzGq+wA+HsFNtJbnoS9knrx0G6bA/BaFiqbYcsp8RY8Q3kIsat2CNP68GyiZvgL42wOXe4DATU4kFCFzxAC3CCHwFn89lokb+I6IYjxEYNvpxuwgj5KQBAnFj0Fc4zakSlsb9hDrdmceyBYcx36gGjZDYJYbXOgV9NT5Nf1fvH9CCz5uOlWeTWRmQPA1VhGAQ4vHeeAhq875w1THMxXKxv7PPXZ2XUYVu1WFSta1XnoYQ4Hpn7w5ufIk2Kw3tMPN7cKgDuXb1VYfvYZBC1GE9QaXj+++4U28gybrXVbeUE5q3r7SFgT4pgiv0LknUg7u5v+P4NV7PWvqlblR/cbXjF+pyIjjyJmlL5nUXqn0fyMmuSkidIKBOvZv3jf9TyD9QCYawe+/5KDz2WdVXob8aYdtDR++nPB11IBIclPrnqccdtP2+URMulZ4ObPW09/f0+94zIiSu/rUqilRQ39um3moFU9PGWE8PYm/uayflTp2R2XaY3bbL1BIrTqJuU7g5cdjU0a23rS9e10L7SzB3R8evinIXLcG6EcytYN2bY1Hli/1g7x8RR6+9xqX0iN+HSiBcL/Enl8rTQAJUdF+faMGJBxx2M0AmhTrC8LpugmM2JZiKShVBEJi5bicGweqomlw9M7DLrB5cthtdiM/3nShA+op1N5htz7fAoXAoRs9c5zt+k7KtF+dupirLfEtE7vtv71/Qw1rqMrVlth1qqPqyPLfexeQ96yy61TuEEFvQ1M8BQCFSjloT61pyFcQ+bwk0UxUbYeD90aC6DbsfVaTISwRuhLCKeoAgqG2MpLO8P2tOIG/6zEuDuCISba1Rm3MrZnTL0xACWjYS8BIjGxf9JckZ0MUeIbZJl+V6WQLMGzFMbht4f74uLjs+rVUZqmd1YGWcUD6oubm+fefYL7GOoZiFKtMlpbr9ofgCyywTtoHim3YAxdX3QpDKZlJqPdU4cDHzSlu29TjEt+s4RDZqO7dptpt3DTxE27ddAlL7I75H79pYTxeWc4M2OgRur66eetd+VzayWvev1MCdtsXydNSB91taOAdcfNHuvWG3q0grh4MBOVAjQMLgEVEF+7y+0EZno9vnxbYHQdVOjvfD9nq5dE/sqw4b3b9lR4dtn3kJkj+C+3j0NY0KtCHY2vqstO/ZCutL8L3TrotCYkd1bDekPdWva/J7hOob599Dc+eJIIFaikVoVC6pyZ8K3je/VTpdLix4rLeYCQOUVgjnf5/P5fOylGdGpjhRFbtQvYeFQmMo4bUb+LZuOIEIG/3kpBDkFM2GvuPHd2gQTeyLqr8CAvDnBlKtak8iP6PPTYBaZ8WSh90uBn/xMgsEB+QetRzQeCx1tigplka4bNQVs0KQwfahmh92ZmJVeoKg/c5QxyCHsnTRTRhDt66YChrzMIxNTITv0lOFkNq+rywU9vVOMF1llmzjO3bKExECpVQmut5u7xfWO2t3F1ihk2OoFrfcHnTwDsyqFw/XyUpaiB5KhGkcGVy3INSpparfw2dvRwvh48Q2uLoEZa/tg5bEixMwpxoFAEVQOJNfruWEWr3Q6yiNfZUsLmxKHTfiVARAbHu4ZlcdoVQRhw7dEQi8Nm1XY/7rLYxqkUckR82O3ahgvfbBEqL01B7lPNp321hN4vdR2LfemB33ZFCqebWd8iTVgZ6J8CLz2cD78vVllrUl6rrSMtT7WPbVUUevzj0G/1Lbwn3xGZvrGvhYlVbdsfraBWj69/alNs+uFrXdt/P8Xb27f3F/LJrvL+rnO89/kHnvvWev3s5zetdv+KfSoIv9dbF9vXeMffNQ25ryNJCAtc8cYCgEDlWDHmBsSC9mUlkChVC+B8p1HeuA76SaVsvZdqmggu6bE3bsmZkDZA+ZcYJ0rt+1gXYKlSvYFogp4xLcBdgRRdmR/GCSpn4aUgO1Y96BVGzaw6CcR2dnDE5JADQ7cHiviAxUZcAQdl8u71I5H8X3DG3gNVfvH68rUZHJ6ybzUWj6VP6sQ3433petqTleY39HK1QHBbTPA1CpR5W60qo8jcVLno+N9SY+J86tOD/j9VV4OQcLw46z0IdGAkQ0ENF/RUT/N/37u4nox4nop4noDxHR4dGV2Uu5PoVK8rULG4BDb1uke0QLjZO7hFahsHJxubCdUO1v1pZoulpXTyoCtWe3NvkuquFiubBr2hRkwkXk7aQbhmKHju0ObeZlqXf6XASCCxW1GpiXWzyzkNcMIgINCTSO8ln/rnb+0A5mrv7ZPdWO16IQg7JchF6FMrR/mRm8Stu9ncMgKc0iHA7k3gYmu6pYe3eC0sY3xa1A2C7G8nFL1kXiz++L/gNxA1jDSU5hflGi6iTtKFBac6lfFwVCePeHyjdCHfgnIecQWvndAP51Zv6VAL4O4Lc/WANhd8H4360g8Hv39brqcwu7Gshku3BPrXiMGlAx/h12ueISmjbH+h9rgvLF1SstzGz/vgT992BjSt3rXDA0/7p1GRqwZ6YL18XfrT/jdeH5lRXnESz7bmnJx7YNsS1RrbtAunV/a+byRi19qJ5L13ATs/KI8qGEABF9B4D/AYD/k/5NAP5uAP++XvIjeNSBpFu9duO0s0p6qmpBGDufG5NbRBLQzm691KLrqkP+ekeu2kFp85wNHOMszwkT3Qd6bDSvZueuTFy6Y28keZhwthP6gusJCE0rRuaMFXfvROV7fSazLFLbbSuPuYAWNiUKiGEof9tk59L/VVtiX7hqVu+ISIpEbLfTv3fL3u7XQ3ZRWNu/MG6bDcRUHkNCLRoDKstTuym0qGQzz4PqwzoH3Mdl6JuPKz+IUPelz235sJzA/w7APwvgDf37UwDeYWazZ30eclLxo0ubZszhTwpBQ1ROYzH9NJrFqoMdbHJlNcdFyc0l/bfo5rmrc9u1sdTWhcZ5JUJ/Nx3lesIFHbC0D5sgqPDA6v3dsafd8THUkN4+z0sRcqwCJmc1aaVyXeAhqvc1PwnlGGgaAd2FoymVACAX7saFien8KhCqHXyIqblD/rz4XDWXiltuWKzMwLwEmI8SxBP7y6B9HKf4+x76a3mDnIoHoF07jSXHQsqbOSb/seTCZALRNp2eqw9EpfaoeraWsrj5tB6gHYTxkXACRPSbAHyZmf/kB7y/HEjK9744dqFxhGpKjLwfyFNJ3UD2tb4Jj4aObfvKl/3PbelMut36IolmxXb0FsLmoIfnvk7eUyO6untK5V+8LgqJnJv75fq449vnLpIgAqIKkVJBPFGYEgE5b9tofREgOq+52kV337Wjtl30PgVqlGJfdQjGzTMTbd6/t1iZt+dc2rXOf3V+u1TnQ+XDHkP29xPRbwRwBeBNAL8HwNtENCoa+A4AX+jdzMy/F8DvBYA30yfZG9+GCUcW3iTnugbHk23Kqir81n5LVHy0jbn3HaLjiOMN3THHxN/iRNLnVNe17+4DFawdxuhGprlhxjlC/nEU4s9IwkD6IemwrlkWFRHIw6MbPiIuPjQ7ea9ofWggvj+HCEzkaoFPxWGQHTt1uIyU6t1sGMBQMnAVdIBhAC9LESjxes+czJKdKm55kXcwRl6tF4aASvwD6h02EIP+vVuEwm89d+yGtfc2mPCOciNed0mgVPXtX9cTAJfUgQ+MBJj5n2fm72Dm7wLwWwD8v5j5HwHwnwH4B/Wy34rHHEgaUY56Zfm/xtxSSfeOicSSXERY5Lq61mOTiJV1jt5ejznOK0aKeV0XyMy2bKR5iDrbcAfGjDc6ukFqGkdYSnEkKsIRBq3X4tGYkqsAyNl3bWaWhR2Z9gDVaRyBaQq6vr7rNMnzTDAAKpgYYC5qgu3ig6pe0xT06eRtI0UFPC8l4AkK+YP1gk2dAJzLcI5kGqt/do0JS1v4+XRy60cYmLr/XX1AJeij1cB/Q1loFZdgm43db3Ouh/x4J8nIUNKZO2I2K1dAxJcW+iVk8I2wDrTlnwPwTxPRT0M4gt/3mJveL6PpJrkWzjXS95LHYY9oCTfuJn209rpeH9veCK22zoeKLMiwEwWYu/FOtMkfd+6cyz+USYTUJKa0hWuwPdRjk8tLItBh8usRft9AfJJr6TAp2mrUi6g2XCpGpPlO3qgdl27tXUOpdnsOBGtEhdX7tIs91FUhP/+aqv+r0hLaF8zSFUncQ4c7KuL7RQBWviHOQsz8nwP4z/XzzwD4/g9UESW02YPrn2uir2e7fbQ+H1w9Wz6gtilvI9X67S51FZWiyWTTXh/VCoXJ0X5PROKqC3QFQLGzNzpltbuFBTcvcm38LucC77nsYAQCR+GS9J2SWlFyEECNSoExqCOGOOxZqvMTqNRRv5i3Qc6SxFY9CQKtJvy0n0J/RHNjdJyKkZNRRfH+t/HgZiNw1TK2eUe4h3ZV8zVyOXuEZGczu2j39+vrmJruZtSUp+ExGEqVfjnq+9px8Uw5Y2M3jjg+wEvlcOGpq0yHjvfENug9PC+1Xt5mlQU2HICkKEu+WDiGIkeYuJb7iUYxy1kglemq8TQf/R5oBADKwi8TO3AN5shkhFtqnEimUd+TyjX2u+Yt4DUD633dlyZsphGYDmK+jSqQCQiD+YkAEx5rBh0PEqY9izrGoe3ef0RASNdFOddON3EMwucoWJzETKmk9KKQiZmz9H9MCWbWl4R6x9VYCyKS9q7rNjCp2ljWynollhigyj4VF3uiOp4j5ICw7E2VF6vlTYhzJFG9IU0jCJ0+C+VpCYGOQ5At9DbVVS+KqgfJu0dswTo37MhxcbeJLCpk0GlXA9tkcgUbsdXRI/lssvl7pQ0kdfgc4fpBF7jtrkEFkPvK5DfIv+EbwrW+UMfR207XV/qySgLab5nrk5BblEMJoNB/JsiroJei83rknZGdcSOo+kLhcjumEf0ogfhQMZ8IyaWYQFm9EFuvwRgCzisskMrGcBPg5JC+yd9Iqe6zXrH3cmdZrvNK2v8adbghqPX5fGH+98rTEAIUGfNH8gIqNR86ebVrhsklMWZ7sq7/3lE19tvRqCict0lFtPiC3vHpd6bcnEQC8+6/az2+ezZCotK37bdhkJ042s+rNulCHQZPpGJCwyAyjaN+XmtdP/ZFLDG6LqAfX1hRxXIdmEt79mCsvav1RXzvVHT/+L8/ozU7Wh99kLIzf+zdYlLUol5sN5SK2O4QzBU/0FNHwn29pCx7plIrT0MIIOj71AQPcSNpoS+Vm6y08Rq3F6/VbmThoEhUqwad9E4bGBiL6ZQmnYfkTiBVkIyVKIDWLIss7DjiIMI1GRknjQoFV1M2cFgXhZKANQEY0FHUN8fgbx4tD+sq+n8DscGCEmhgYC2Wg8I3zMWCsa4ibIYE0AgsC/j+JJaEcSwmv9NJ+nI61n0Wn91yCi0ROgygwyRQn1nasePZKOqLkprhOjJvyLhzByuS8whBNalUtbArm1OTjF3gKSLiy1wHFJmZF4M7Enmbozocw7obVbgyn+s9pvL4etoBBU9GCAC1PgOEFyoX6H9hEsSF38D3rhkOQMy+K1XwVqBAF2eElpEcMptzCmRe+xygLxQi6bcGaGqejpV+PBTiTncsCpDdzG9ejOm3z9aGMdQxUCDDEjDJb74ozEHIrAfMYtYbBxEYgHy2vh4FZRAzsKyOdEqUYnIBAOj7npUAng7aNxzeLZcxtHcwoUEkbQl8g5sUAVS+CbFfIloywaIL3OC9EIphDtj9pnaugcDVdlkfuielLu4uodv7HE2HFMY/qrqdBdxaIqIwcAFxISNxLE9KCABbOATg4gtUJbLEHQQRHlIEREs+huhFjkeNtVD3AgJ5X21uiKHqEFGHxmEnBMoE7jndpI5vvfoTRObbvo/OPw6rx7G+JtZPBGQjVzOQAA7PI0CEWc4gQPowMzCG2AwOdnvbMZllotuOF1l/F751W7yv4s5vAtOuiUjC3iGYGyMfU12XQ6SiqS2N6lY5Wm3ci7XtrVrTqgER2jc+L+/LZB7q9E3yEeZB4AkKgY3v8zB4Z+fzXEH4cJNLTmPwzaRi0CodprKrN0SeQ7O1PmKc50Xt7Kh39I6NFsBmEKsJUKkXXKBzW0/0yAMc/ooXXXjljX98ECDRRGfPPh5A/jeB7+6KEEgk1w4DMA7gwyRC4f4MPo6lPdY2vZ4WbZ9nXrZrVBWwRZizmBu1XdQIMJrEB4GT8hGGdIj1s6hQHtvfhD2LgFs3wiA6gWFdKwRhDkc+R/zdAt8w6zMpLNzMbsVxAWL1A1vL0zAAFAKyVvjctPo2QU9N8JttUo6MW7fldrHbemjVip3yNITAjsAy3Z9Rwx5PFRb0sO79G+eYsPjjYq38BIIFolUnokUhmnJa/c50StP1gcoygKGoEE5eGeyPsDY423Dc3Tp+AOLplwrfQAQcJneq4vuTXJOSwPOrK1nwBusP5PAfADAOyDdHFUKqNx9G/5uvDgL/mYGVJaWfLnZaAiegi5/GIZguk6gDPlDsfANHN2isxfRoJRGIhmIqZAabQ5SNuS30KCRTAubZHZ1iH1owlbfFrTBBxWz8B3xMWrQS0ShQ1WVBZJRyvUHsIQXUc7CKaLXTnSKHZvcHfiH229NOOU6oSJai1/QdHSpJesFJiDqD0S09P2+g/10sNuCXYNeeBN6oF1RgZXDZvVQsXNg5BTfPKTGku5sLkFE9Bwf5jXW3tt/Y4S2AlJAPA0h3eM6D6MzMoFXgPQPyN2W5lxNotc9c0AkReBxAc3EpriwegPsQuEVk0Gg6zqqmN34WLM/rjmvstwjZ9+bApbnR/BbVM8q5oIGA8sq9gW+oKmlUyT01sjcvIy/V+/4RXqlteRpCAAEW607rDGfItFoRgqq/u0BoBqF14OClo7f7z7LrXNTB1IGkqBr6/RryEjT+BtFM1X5n31cmLJtwtrs3k4ftmkCWIZEsLiuHSaswqK/u0ZnBV0fvJ7bFSQTKGfkwAqMSemMCJwJXpCyJQMgsCMD6Whn3PEm/DpUaA+BQ/mZFOmTk4RAEAbMKH6ren7MRYAm8BGTgloq19sFAMxcsliJRQYZEYBMKNgatudU+m+UgBWGi1gUjIWMsgydj/YCl9hMp4demmpbrgi/FGrJSx3D2KEQutOlpCAGuT9XxryuY3+QCiFA/sqfBg6p0aAPX7TotEuetZpwqmg3bHbvVPJQzKG6nqAkwNAIpvpe16XCo9dnoOht2P8IATCPo6krNawpH1anHVQudqHx9LBYBIvA0qtddRr6xxQHk6YB8HJCHhLRmLFcDeCCAgOEuy+InYJ0G5IHAI2E4ZUEEDAyn1YVAukvgozLn8wq6EwzKxtoPCXw8gF7e1upHdFC6P4HPc/X+nM3EOaC4xmbNRTCLG7IVEwoxInVFzUMEQWBopbUe0DS630RlblbzopGO6Xh01YRy9jwPbsUxkrGHGqP3Ymy/jjkvS42Goyk3qjXRkuD8UyMQdsrTEAJtiX7QbdmDSO0OncIO3EpFq8dLXV8dWNR4DdrvqZ50DvtSPdE2pWWmm8lZGPis5FrRA70/1rgIqJj0IBDVCD436RE5wcfjCCYCT0l2/CEBCViuR+SRkJaE9agogIA8EWgBaGXkY0QHSZEBAIIIjczINxM4CWqgJSMfFZkwAzOrcNEFGvvczFmq4pB9bth7yZuof5uZLiyAClmta3cHrHIfGuHaRkgCIY5jG5AUnbi6SVyaujYl79zTXhMQcJU49huhqmp5ekJg5+V2PfiMCOmZWVqWNe7swTUznr1XtSHYqwHUjhgtYRcEgU/EGJ3XJObAMAjZFh1u9DdDCrxmid6zdzFYP8/KtKuAat2BlfDjSXX6cQAfhRnPhxF8SMDKWK8G5EMCrYzlJmGdCGllVQWkquUqgVZgustYrgiUgbQA6yQLPa0qACALfR4TaMlICyOdCeuzUa47r8CihN2gUYmLknhEwlOYhUG5C0cx0ZxL5lSWQUlCkoVAXmVMc3YBE8lXANVOzxYIRcWvoIrIjKqZWSMMDUxTbaKNn4NqEtUEz+Rk6kIL1VvnLFdrUzGzmnszGgESnZVyPFCnE+vSKU9DCJg5R+F6ZSKMu3y0EDQ7cakrdGzQ0WkaJb6d6hNZWvfhKCgssITUQYWCl1kF34wxtl3cMgSvJRjGJ5N59S0LME1I0XPPX2GsJbmZRZXwq7wJMwvzf3MlcH9exMQ3jeBpEEbfTKxXE9arAcv1AGJGHgl5JKxHwnxNyAdgPSZQBngAlmtgegnkEXhxM2B6CaRZdt71SDi8xzi8lHp8KAbg8ILAC2N5NmC4yxhOItTyzeQ+Bel+UYcjRStDEs5gWSvhLWgjq6A2LmAA39/reKpAHSZ1aApBQhNEOFhdulh9TG3hLAvoeNR5UXb26L7M6yqWFxI/DI4ehxTGJzWmSe+YXAQ/eBNQFqMYaUji2RhQo/0d8yaYN6Obi9tdP/4GPHHrgLnM5jq1UpRqu4kTWuSwx7YGj6xNifoUUJv//Joaau4SQA0EZWbZaYL92ZJ4VLA1Oixllk3eSLIIF1Nh3CXKLjkBCCIn/4z4y8dRdvZBFvzyfMD5uezuIgSAfADObxDWI4AkxDv03/ktfQ8C1iu5FkxIZyBPwPkZgRiAbM4YZhEQPALpLOgoHxKYdKrpO6/PDkjnFTSvIgzMamA7ORGwZrCdMgyIeuThyc0uZ30ZPfmIwAtk8ZqJN2Ux+0eSzXwR1oZkq3TxEH0Zd+UGyju/YJ97eRN6BLShxnVn545ejkDlzuw6U6LqRGYAfeHQlCciBJrF3eMEWua9RQN7xSR5vL519rlAnFQL1etMlXmo9fKLOzjFCTGUPHvVISfMhdiKerDF8JsSbERQ3HmGBPZMvAl8fVDYDfCYkA8J6yGBddc/P0s4v0EC6Y+E9SALe36DsR4V5Yh/C9KZkCdGWgjDrQoJAsDA8SR8QR6FLzA1YdTvOakQGAg5ETAQ0lLGOB9HER6qDtCSC1s/DECSR7X97ojAiK9mglPrHYiyaErykMZOb/EjJoji/DNUSsVVOEYbdj0SW36hxw0BW2EQrwumSXdJNotJVCn8VkUwcSO5EC8QyxMRAiUdWBsY4YtXva18IUcHHXXiaSMKeS27SDdnn0nJgB42obyxlabXmbeZDZrpiObr7w4ma4jEW4vZryL7IE4dw1Av+qqtmt5rCJNrzQr55R8GZbvHhHw9yeI7DpifD7LjT4T1QFiugeWasDyThb+8sWJ46wxeCZSAT7z1CnfnCfM8YF0GMAPz3Yj1IAsTGUgLgVbC9AoY7hnrlagG4x0jzYz1IM+7/+SA8Z5BK5Bmxvh1STfGpETjYQAfB6Qlg+xfZoHtMzd+BQEN8ACcZ8lJMCTw7T1ArDv/Ko5JjrxInZVGUQ2IANKd35KPMAOsiU6G5NGWvNQRiJRREJu15QLxiHFEPp0cEdA4iuOboZIQuFTyJ9qunsq80ed7PgQ1d6aDpHzj87lsEOaI1nNPfurqwDaHQAvHQ961S6xnXDwRGrWulpUZLm2/B6q8fmSMfXOdDRzLH/KjEUVh5wdQx6ZbdJd6dyENJQmJ6f2pnhCVajCNyuxHQglYjwN4SsgDidmORHXJIzA/UwFwA5w+kZHfXHB884Tv/NQ7uJsnMICrccE4rDiNI07ziGUZMGdCngnpJO3Io5CHeQKQZXEwydpcj9KetABpEcciWoU0zIckasPK4CH0l3So/DeNIPUm5KtD8SzURUMAMOqOr/OAxiB4zQdB66SUwMhlTKqIvO0mILyUfm+Wlta7L4YIowh4Lzm7xcFVgjgXjPTU+cPe3MZnJHUIaJuP8XQi1fvd3yGaxb1N+yrB0xACGzU/7JTBHbKXmPFiucQB9FSJSMRduNbTgKUdnQ8oizU3ErnXbkM6cdcxyC8PDOhBdd1pdP98RwHq5Zcn5QAmwmoI4IowP1ckcMPIby64euuEz7z5Et/zxldwziNO64j7dUQiRiLGmhPWNYESgweuXOhNuCABpPlEORHyyEgL1CKg3oV6fZ4SSFUCUQXgDkhehvKePCbQklztQc5iRbDrcgaMZLNduR3XJNGNbP1vpKqPk1qAJlt8YYxSELKbem3zaAOH2rFNZVyjxSGSn4062QoC82XwzFMdnwY/ywEB5T6yPA0hYH1gcfrO2s61o03rRmsDFvWj1nEomgQrHWlw1neTlTYgg8juG2QzSR5JIGjqq2rXBmohEXU480sP6IEz13o/UQjZzeLnb0LgKHEAPBB4GpAnMfetV8lZ/+WKcHo7YbkWUu/+Mxl8vSLdLHjz+T1+xSe+ju99/mX8suO7uKIFKwhfOH0Cv3D3CQDP8M6ra5xeHIE5gRadYDMh6eaczsB4yxjuhUOgrAKAjUgkHN5RnXwUbiJRxnBmMAjDvIDm1d2XOUGh+SQ0CBH4ahKno/uzvH/0SDT9O5CxdH0laAAAjgf5TASaJlEVgh9CQV1hcY6DWgES6BDmWyNg6OoKfH8vKurNjdzDGjth3EEMQ+/lWvSpONTqZLincBnbhLA+rTSVezXvgC431SsfSggQ0duQI8j+Fsh+/o8D+IsA/hCA7wLwswB+MzN//YGadJevF3k8W6D6f8/XH6jQQwW/Y+ShuQAjqBbuANQIGhssk7TmlOMmoUYvNMjOzUk+e5GHlm8vBhA5acgyiY38s50pJfA0FCEwCAGYpwRkse2vRyHy1qMIgPPbGbQCOK74ZZ9+F28cTrgZz7jLBwxgvFivcGKZDrfLAe/eX+Hu5RFYCek+YXqPMNyTwHkW0+F4yxjvZeFzUmRAQgimhQFGUQFYVAJaWdBAAvI0AGPCcFqLr8FJFwgxsJbwZhcAwyB9sQTbfCKJkMxZvh9D/9kYUuVTWMZkADBM5bvMzgkU4jABhxG4u3fUwff3Zd6cThXhWy3+YK2IeRk5unrHIKcGJUibclVXiz59o9K6WhU0pmTvlZ2V9OjyewD8J8z8NwH4NZCDSX8YwB9n5u8F8Mf174fLrjdfU8yPILoAPzaeP7gU711XEX7vt0Rm2PTP+EwiIf/Gsez05j8fUYV9XzfM4b8vfPX4MwtAnsSldz0SlisIQXcA1iMjHxl8YIyHFZ+4usOnr17hjfGEkVbMPODd9RpfPT/H187P8Eu3z/DOi2vwqxF0N2C4Jwx3hPEOmF4B0wtgesGYbhnDSchAyhDdPwPDOSPNRRAQQ0i/VdUAQHb6BH9nJvIAJm77ou2PlGSht9e0/9SU59fZmQqel0HGyMbcSVsjaV1N28ZxuFm3RadD3S45a8EI4FJff75s0ePuXKx2/D6n9WAdWj4wEiCitwD8nQB+mz78DOBMRD8I4O/Sy34Ekor8n3vfD2jNGw0/wLmcG1AFEamDBFATezwvHiPgwRg9S8EQvluz7BSmn1uYalsOB02gUVjhzbvoDiOOIBP49lbCeQ+TkF+ezCTqx6mexKoni+lPJ99AmJ+PWI8kfv1JCMD1QEAC5meM9Vp0+vHtM9564w6HtODTx5f49PQSN+mML5zexhfu38bX72/w3vmIX/zCJzC8O+LqRUI6AWkFaCnw//CKMZwzzDeAkxCBADCcMqaX4tqbDwlpyW7h5FWCiPKUMNwvoDnstjZPBzUXAuLsNJAggnkVa0HOwALwcRQfgzn0GxH4OEmAkiEDzY3gCMFSlBmyGwq/Q0TN4SMDLO16lYTUci8YmZsGkDklEVWknWdOatKRieehunKflFSJO34b3gwUdTMKi9azMRcEGhHAR6UOfDeAXwLwfyaiXwPgT0KOKf8sM39Rr/lFAJ99uKrA0LZMLQAPsjD3yRgnzdlzugGAHUflRSFbCtF1ZAFDVjy4BcXV0xjcMAk27qeAwK82u61JezNLYRCPL87i8jvPMvjnWZNlTCWbT9jJOFFxnAGQrwtsFb9/WVDrQT3/JnILQJ6Erc8TsD7LOH7qDss84u484eunGxyGFQMy1jHhJ7/2nXjn9hq39wecbyeMX50w3hFoBg7viRlwOAOj7vrDOWO4z07oCkGoAjgDdM6gNYOyRBdSEhWAzgGmLllciXMIZpKO1jgECF8A8W4k4wDcqcgqUiHvvI5yJ2MJg4ZCcD8jYRpVIKjaRjWXw6ezCIXDATQHu9qzGxcg5jHKqvZVR8JZ4NCqUY96feEYBiAvMv4mlALZZyc30TjWqoIlRVFh4B6D3nU1CngsQfhhhMAI4NcC+CeY+ceJ6Peggf7MzFRlNqga+EMAfggArnDzqAcaQ9rmHdyYUaxE004TT15Jxri7BzTg9wFbvS2SPL3S0/8t6KdySNES9DoP0lG4KKG98r+E8sI9AHlQE+AgZkDz9BO3XzPF6VohxrIkvHsnUYf3y4QxrfjCV97GMg/gc8LwzojDO4ThpLr/K4H8w8wY7zJoYY0LyDVqMWY6c8lglBlplusoc8lXQFDysJhBLQiIpwF0WtUJp1TPA4F4kB2dQyq01joToXGS8XHX46g+qKty234bO4rXWr3jULiIag5SsRaYGtFkvqIBNbmYBOH02h3DwLsOR3Eup2butxarnYQ7sXwYIfB5AJ9n5h/Xv/99iBD4EhF9jpm/SESfA/Dl3s0cDySlT3LxdCooIB7cKdl8A3kX/K4LVG9sqYMebImyw3u2GctGYztyJ6qwcgHtxZubLTeeyGODnIX8KqShHaJaICaFgeZR4aEdBGJowNj/o7ndyu4lAoDcbz+P6qqrG1cegeUZqz5OON+PGMaMZRnw7rs3uL0/4AurLP70pSNSYtBCuP4S4fCu7PwAcHgl+j2tjOnFIjv8kkUgMeRvO/hU+8T8F2jNSHdrgeoK723v4uOATHIdzbLolzePmG7P8ozDCCTI9Roe6slMFd7zpN/b+NuObwtuGsHnGXR3D1wdbfIJGpuuxS8B57LIcpZxMhQYfTXsGt7mifDUadMksSMxwctBx+7uXswmnt1IkG88E5EB0PW1rwHE5wT/k9ZnZQP31cpkloNLiOADCwFm/kUi+gUi+lXM/BcB/ACAP6//fiuA34XHHkgKFO++EAtNFjceA3Qsn1sMFOJmAVppdnjPAhPjsI3FTyPMDFNFk5mfv+Wx00UvnWuHaArL7E+3HWZZ1ANND/5QvY0ASZCRUKwNi0xizwFgzjMqfNJpkTiAQ8I6Joz3K+YbCf9drpM8PAPLM8L5TWC9YtBKyFcZPDD4bgRuFoHmA+P0tWuk24TxRBhOhOkFYbxlTC8Z430WZ5+ZMdyvuvOvSHem+JMs2lz0/bIzATRnUFZhoaHDQvwBdFoxnNQZ6DC6YJDFzUj3K/g4gYcsPMACz4HgrD+RuEevrE5IugCTLRbXFaRd4wB+87k7ISEl8PMbJQ/DQnfVYXBLA19pbkaLeNTne+p0AHx372c1xMNI6XgQ9a+1FqQBMbDJSyJJv26WgxjrYHPB1IFpKmiIw8G99k7qsERBTdorH9ZP4J8A8AeJ6ADgZwD8Y5Cp/YeJ6LcD+DkAv/mxlXm4r5NFxawW8+/rxf77bo7BJniikpZ7Tj57bduoGsEFOCZyaGClC5xmV6GjmRjJCSfWSDqvoYGC4v8vpkDZ+Q0NqP4/yr+0ALwA6wFAJpBuxvnWJgRhfHdAOgNpJgxnYLgDxlvd+c8F8qc5I80r0r3Y9AFl7xPEEchgtrH79r374Q8AGMSrLHTjWAzqLjrJ9fq0ZP0ua3yG7qZ+YAmhtZxQ3BUHAum05tEEbAbyKtGVNi65tJE1+QitrNYX3XiUh2Cg5FO0nTiMjbkDOwfg7t+CDEouQp2/pH2WAEC5C/dnKSY+akLZS6WNwxNRjVQrVYRqta1TPpQQYOafAvB9nZ9+4H1VFDfwaO4z7y4TAJ2wYmfxrbQuxXGBPmLht4ShL+qYySUwsPV76ISw47lN53WHlux18vVRd//gBGQCzwQBi0ohLrmpEgD5kIQHGODuunlSpv4MgAnrUXZ0iQsg4CSqQVqA6QWpg48KgHvGeGKMr1Yx760Z6SQLg+YVdHsqr2mT1Bh4Is9bCKBiucm2sTlXC1lyHWTQujphKKHEigCMMIMsbKRBAqNMPVvKmLtwSgk8DMKDBJQFLKDzDL45FjQ2ryXhimVc0vdw0jAXwo5HiHXC3mEuY4zD5Kojn85yxgMgMP/qKP21ruDF+KVcTMNAhWDbDENdD8JYuA4XLpaugGQ70Y6xPA2PwRBAVMU/A3UwhDsBCdMu+hSqjCusaaKLc0Y0+QRG1zLvhu83n4PuZafcsEVyBTMjz4vokZRqUxIE9tPAojMGJxa6P2uyDw2Csci5k0HBhPz8CuvViDxJFCAyI0+E+ZnE/C8aBZhmgJMmAzmW5qdFIHjKQDoTeJBEH+M9ML4SuJ9mYJiV/FMEML17L/H+ZlbTieSEHzNwOhduY5rEo9AErS0ouzZnyXNgDk9DQro9Fe7mNMtv06i7LxwB0LwWUnQMRNmSXR0As1gYUpK6hkH8Es5nWcBEktLsLHyL1cfHSdWXVbiDkYWHWFlcpRPJYk8JOB40+EeF0/FQ0E5KejDrKB6LQUBIavcEGgfQ1VHUwJj63peAbnrj6Jsd7LQqnYsuKkxocDEF2tzdLHVFLvT0hQA7gxpttiUst2WAlRxEC/ElD18VvguU3TfqZoYueiVK26D3x9NwqsQk01hJ88qd2Kpa17KLGPxryRxbY5oQJBucZQZWSEhwAtLCyJ7Nx9QA8REQaK0IXV14SVWCtEgegHSSOtJSBMB0m5FOWVj/qOfr2QLOsMf+M6ctP+eAxefBfPkBRztY1pJSLEuEoLsLt4FQofCYKkhLuvgxJvBRofq8aptF+LiXoXpVWnsZg8dZRBUCRK46eH/r/ZE7wjAUNcbaFNsVzZHMcm6BcUFGXidynqgKZbYSv+ccyHD9TVWEKrcFN2MD1HP4o1QHvuHFdtUUIqQoxE+nzuIGalJk6E+mjTsn4DDJF20I1hCuIYdJkOH2/yFBtivTC8ciEGJd9pm5Joe07Rz9AqwMCXw1yqR0iS//eCSAIO6/1kWMymVXGHtFw7PkB5SGAcNZVIXhrOG9i/wbzozhvsD/to+cKV9z3VaLuAMK7DyfxQkKqLMCrauaCQvTTUDlHUjMgi5sPicuxOkaFtuyIk9H3eXlNyMWnewjkl1d20bqrmHh1pi5CGhFB9aGaLfniYr7coTwZoGICzDLQPHVQVWOpXiHqtnQ0sDL0AXOx57pfVsIxipHQBgXQ6VVaUzhXs9HSAx+YwrrQl5Rgi8M3mvm1mh79WO9dQHbKTKOIsxjcClpmnlZijdYGLgoUMhiyVUfrKK7TCCkLJ6MYULQs5uSFsscPLLukDEHwDCUXP/m/eXx4wAPA9bro/sJSHJPCQyyoCBOoiPPN5rvzxzmEiOTqAPDPUAn6dfxDgAJSTjelcU/nIQDKG6/8m94cQKdznJ+gKoCtKzA/cl3MzaGehgkVHdZfIJKoM4ii+aAMuF1URBGqdeEILOw9ooIaBoLuz8ksHJ5JgSYCFBvQToL7BbHqcG9DU21Yp03tIpwoSWDkGUzvRoFaRFhuF/c0pEPgxwhvrLngCQUwSQu28pn3M9B4Ez+LmS+BMdDOW9hXjwdPADgdC5q7BIXt6KrNEhf3t4J6E0DMJ/Krm88A4DqYNgmh8UeaR7L0xACBFQZflfU0i+yp00gDq+5lpqACwuKO3HOxeMKqCGYLlpG5BeEhLEO9UQQEcab6S8QZLbYK1jIXJJ92mCpEGESFpoHzQNwGMqEPxjEF309HwZ3EJpuGYvGCFBW2L/AnXzAwvYjyWdx+lGUsEh9xtmlWVBAOi0VQebnGaxKLJk/he1alg7bVAJKjrZcfza1wIKfchZ3YaiqABSfCiVdnTsBCioAiifhKl6FnJWMsyAdQzGjIgQGcFqBgZCvR7HIhGC0ZOSijnU+JOSrAel+Fd+USds8Q4TNVOIC8jQiMauw5UoFKH4RmhtBycOoPrhfgZn3YuoyZgDCdyGui1QSn0QCsfJTiSSgzd+euhDK0xACQCFJLgUPtV52MfQ3eHnt+kuH+GwAru+zkjvt9TGrDBAEgCW4iF5kUQdTMxYPciKP74a6Q+lFfi0zRFcdUrGZAxogVNqU3TuQMM62WOFe1MTi8CNBO0L6rUfZCYeTEoGr/D+cuKgPWdKDR8ZcVBhbpAp9zTUWkD6Yl9LXg9Zn8RoOlQN5lk2vCYvGzcFB9w6fXT93tcHgehJ1wVTAtdTDg5or14yUs1oMkqZNC3p8ZjcLZovJmJL4QJAgFcl7INeZYxIAzds4gEZ2NGLC3t9H5wubqThGDkZ+SBEVALD5D6ggqDa9MP/dETd4IEocR8ufpb5VIZSnIQQY8hLGxluHzGHStQs9HMpAU3AmAmqXUoPdlgIsSF2PLTcii5L48htJFGO0D+NWr2fWSZ/qaw2dMBfyq8mzXxFWlhGYhOAz1te9Ag+E+SaVBKAAzs8TJGxX69P1YQvdeITpFcOPQ1yNEGSMtytAEOvBaXW3XZoXPzfAuAxeMzCfUZ3FOC/lwI1RsyNnOyJ8EHiSc8nBEBxdXLUaBcKD2VUDAMBJPfjMMmHjenf2xbWZ7AOBSfrIYH4k+5KaEXPIvTCcVtDCWG4GOUzFhJIJEWYRCgR9JmnWpoThbgFPCet0AI9JUNSSwVdH8NUoQvX2hPzmjaIfVWVdjRxUbWCxLGnfeBxCjFD0bEjZBYuTjLpO7KCUeNp2pQq0G2IoT0MIBLLFfQKAYhlIQe83Im8YykEjgN8XhYF9T0BJAQZZ/LwstWNOyOtfpZQyIeR6qgqcaSrkX84AUuEJdMHTsoJvrpzhpiULhFW93khB37kAic6D7Pb5kDQ2gJBmrmIFSNe2kGtwmz8neEgvrZAUYCxWAFczNNpQBEKumG2HsMsq3m7WB+3Ob1wMK1MdJxxngEm9Iqn8ru9LFq/v5j6NEFQEZXOi7P7q/28C1biYVa0CnIpjEAPpvDqqWm+MVEB1rBonYLmRCTTcZSzXg6tOMRgqTwQ6JGQ9bSmt2fMf8DCIGjavniMRzICZIq8mRXZSH18dKpLUSxI1B0TCXdyI2/CGyAaK6mTqRebiFajBbH5WRbRi/HWhDqCQgZW5yHZ/0xeBshDDfd1QyebFH8wT0HpnRfNka8JqQnwjUWi59SvbrC52v14hKJLY8m33yYPsdFnTg5X03wZNdVyHWhCY7hzj+tPCuqsDaRYkINAfJcmHEoLu5Wc7srPKQW+P/drkTyxqQSqw1SIoAQn8ipmB5Kbyvwv/YG61SewQvoxB7NuSh6BWjRhUHaQiC7J8zqOoXsM9PB1bWli60tKcJwVSoyYaPQMJ2dESm7lyNZNoLsI9ohWbH4mATKjchiOKNeSjc5ojdxU5snEAeC2bVDyhKtYb/98pT0cIhOw+Ubev7LQxzDikWvLdP0i9SqDkXCBT5tpSkEgZ/VzQQCT/DiPsbD8sshM6nLUsMyHBhR+2mdRZZlFT2CH5KUC0ZPECPCSseoZfPiZN4V0P2Krpu0HC6tMKjAtjvqEy6Y0YzGoK05JWIN0J/B9OwnAPp4zhXm300catrrre1wDo+gr84mVBZtMojLmbxLL7sQMqZKOzi1kNXICq4LDwaT9QZChIa1Gz2kDFQgF5RxFEmm8vA5ZgBQEFiCqAauJb1CWgujwB802SRKiL+l9oBGYeJeuyJFIVBCaVQNHYAAsUmV4scnbjGxPSYfD4CrZzGc8r7Jg5ngZRZ4YkSPHuVDaPGDE4B7XIjnRXpyFOA8RhZC38TOzfaSzqQCLYITYchXqnPB0hAJQXUpNHjOXmuWThBSCRYdaBGjBhMdaeJWYail41z9L5Vn9k+m0HhHIDaxYpOx3UTJldEFRCyRBK65hhu5YShzxqOjCNsFuvJ3VakZ3JDvOkBRgW9sm4XOtutUo67/mZcR6S1stO5y4B5VA+gDXDjzr5OCdg6oDqsFza6/bxGMW4ZjFTpQGYz1X8u5GpkVxlM/cFpyrnCJJGTzo738DTIQhx68fWFm/mQ2YJK06qvyuvALO0JONPigPVcLtgfmsSIUvSNzxI8hVRowh5MEFC3s9Zj1ujDM+kbMI3jxPSPGK8XVV1qxHr+mwoVogly4K2De76WHZ/syCYKhnfW0OnvV9M0DKrqpyLx6oVE8JGOq5rCePulKclBKwEEx2AXX3GT/cJpeIE4osHT6yu3z9QPLlsV4rOSa6f+oMKdI4lOglpGDCryUrs3oNnBeLR4D8ctlKG6/5ggfSUoUiAy+7PuvZNJeByPSATOM1chBWgh4SUCcUEMTk1aou/W+scZEXVgV4eB9f/TZWKFh3jF5xH0bKGvo28AHNh1FmyI23GAXDXYqT6PfIkbH9aGViNY0D433Z86XOLw7AU6sYjmG9FdMgS/wxGhnI3CSD1SaAl13lvBgIhgTmYjK8tf6b0JwUyubKY2AbnY4IyXs6Z1WbzB7NwN+WJCIHyUhIQJNFWnpE3JFeIZjuKk8mk6uFQkVdGrtDxKBlbUgKRul9GfwQigCQIRqCyLhyDaoY6TDpHH4Cw0MQRRnXCMTkxRUuWwzYmgf2AIIB8oJJwg+C7E6fC9HOS73z3H2TC2kQWVQBqCsyKHoA0Z32NIhhcECQN74WkDPOJNen5i/HdpCPErdu8LFUQiB3diNxgcl01ItSEgIVVa11GGhKRn/HnsDg4vpRzB7TvJ/WmXNm9Q+l+Bq4mPWqcgSRWmfUo/csrFVKQASQVtLrQlyP5kWzrUXwtOAHrldyTNLUaCE7AUtYNiIDlJhWBvQhhSitjuNWQ6SHJIbBjEivFynJ8e2bNwDQC6xnRu9JPlg7koEcOWjCdBjvZGYq+mqK6qidc/XWhDviCj5YBoDj+GAQCBPa1+dbiS8ZAIWVKLWWTm6mMb0gZciINA7wKe2/PMC4AkMloE9RO/bHnWq44Er91PowODcWDTRDAehVQQZIFD4hOKhC37hM5OUhVgyvg9Kak+5Y0X5BJCWB6mcFjIcjSwkAuejCtxTGGVk3rxVyi/sLpwBiHsiuNA/h0KupS8OgE4F6elRC0FNmALMiDIYG4y6ngVt3fEZ21weBxJNPMJyNn0O0MfnblfexHmgFFvSKo2a8gETlyXcZ2uZJszNmsmZqUZT0A65VNShEK4ECu6jHt65GATEgrcHiRxR9DNw6P7xhF3TN0MBhpOBLS3ezEIacEXB/l3U4zADVnW+5Bu+72TtsVTIbIJXYjF/MrMwOnk1rRklzzCt3yRIRAvZAfZPFN/4wqg03CuKsAQQXQiekQf0DNrGZ/Nkfy0YoRkUnDTW0ntGOwLSnmjBL8Y8w/Sey/vyMJ6WSJQQFVByCT2ByCTEj4mYdKNeSBqkM91iM5THVzgUFZY//PJRuQ+cLb4SGyyEwi5bJYox4K2WG6qpmNV+ALqBXOgbBq63SrQURV9ln1aI528nEQHRvqAMRUCTymsPMDmmuB3DTKagrkJCoZFkY+ijk1D/CQbJ4EaUmCFSX1RyCtBNyrCZHgR7ZHE63EeqQSjm3CiVHMsqbeWD9FvsSTkwS436qxowUUrUXVAkr/HyYdMwZSZ9ysmt1fnkApu3VRBwBUO33JGqv+6Hd3csy0ZX0xaBSddTKXJKBRf7USJ6pNyiGF/HJJ4tOnEflqBJ1WzwIMyGDzpCZAW7ijHsi5itnOIgI5KUk1BmJKBcCqaqPsJvCFzqq7Wt3LNfmxXzGWVPwHJMlnmles6o0omXok0QeyvI+jiNM9nJOJGZwsdqMVvNED0+4L8R9kdcQ+tcSxFiaeJo8/qJJjAJXlRcZKIiwlCSlEECCDLVErsqpV5MSpnMNojkRlLJhEBUhZz2g4QFSFgSVB601GOst5C7TKtTC6ajapC8zPCIcXamYlhDgHSIJVXdD5MKglBmI14uQIElHAGhkYN65BYwlizAvJC+ZXtxu12BKeUj65erZXnowQcCeheEAIVGrbdzqhaAguvuofzcsCvj+BxlFMgZSKgAj3ejGvrJjRFQDPEgTkUHksOyRfHcrkPEzC/N7NMmD3AI3C/KdZEp/kIQn0BkBrwvx89MSfpzd197Zx1+O8jYSydN72f9JMQSYQxO7P5eSfEwsBlsUkKCw5+STkQFpVpkGdfCkSUyGTrnxQ/iSkwo6OLD3/dPdjB+Qls+buUz8I2aGKW3IVAWo68TAWYpAkSEdcgDW+wgSvIrQ8JfBxwHC3gBbG/OaoJyTLu69XciTbehAIvtwI3F8/wyIASHT69XMnESS3I/DpE86nAfPLETwxhhcDppeS1Zn0HMZ0Bu6OIlgOLxhTYj+DcXkm3oNpldRpxMrrXKlDz8qg+5OrO/l4EGSUxUHKYzju7jU3RVCBLerVjlb3AdG5O2vWYlMPdsqTEQJe4q7RRvJVjhMNNDK22v0DGmjUxh1whth7w05nTbCEI5H9j2cAqI4qGW+SowXbTfNkfuca4aY2ZyMEicXEt06y4y9H9QQkhaD62ZxXsG55nTwBIFIfgMAnmHNMRuUODADD7bkIgHWV3agNflJiinOw81u/2GTielxaAeAJMc1tm+wILRUmTWYdthiFRMWz0MyV0XFoSCpECv8R22AhxXwQ114wPLdhHosAyAfCegWsE5APqgIcM3iSd50OKygxZgBXVzPOKWPOBBoYKzHyMeH4NUnPRquM1XgP99HIAzBkIWbFbCmC2mM1GI5myBY7sc730I+ajAVqjqURRVBrP/viNuvXugoPwyzh0t7vfz0JgegsZOvWmH+gTLagS3WPd+p6//U7opKu9jz30c6FmIoTV3chHlKxt2sxl1XACCc9Hcg5MtJsQMA6ykTUr4NXIRR+KuC16m1zToQ8MCiTowlzcoOaGg2FuInwflZOQ8ygZEx0TL+9LCVUOOTXjymunPyLTkMIAtciMEncYOuTfQPxazxEyMvnJ+tqWzz81hNtCOlZCYJsi0y8BC30uoyHEqwHqDAGlmsgT+IrwCODjxl0zKAx43g1Y0wZ63HGG1cn3A4HvGICM4GnjHxNWO4SRiYk5RZwVxAcDwRejBzURqym/7OPH+n7eF4Jszx5w1n8W4BiJQukdpVDk0Q4tk6DblZ86m7D5ofunn6647ZutyYRAQRdXSwKfqU7ASWXgBboAiAcBGoBF6p2KBlDV0f1xtL6j5K2KhJNYAadF/B4EIkeFhsfkurbwPxslJ1okMkHhp8ObPq8LdysqGA4FXNhVvUgE4GShgJnUQOGM/y8AXh1Yo0QU1Uu8fRLFrVFJ5tH5iUSvXQRJpotnVUQmO4IFFQ1GobqLL3qmC0E1U4/e2BO9HE/z6ADZPFrHgYJRAq+Ca4TC8dCt/divRgG5DeuFLmwu1wzwQWAuQHLyczwk5ktinO9ZjmX4ZhBNwuurmdcHWZ88tkt3jrc4ROHO3zH9dcx0YqvzM/x868+gb/ytU9jWRPmecD89gDwiCmTmw/zJIJpOMs4rVdJNKERYBowvZz15OaE4U4PoDlO4GdH0P0i+RYsbwMgfXZ1FCE9qxMWifCQo+1TUZeHJH15oGJW1HTrNmZ75UMJASL6XwD4n0Lm4J+BZBv+HIAfBfApyKlE/6geUbZbfJIBvqBLjsBmh5YHw6MDbdLYBHVGVHMNjmXiuYCwlM+rngQcmetIfKkwqjIAkaanUviZzqvkuxs0E/CQkK9MDwXW6+IEtE5UCCtLEJIU2gM6kahwAfoPgO5sykivQU0Bgg2cSyYhq9JIqmkAaVy8uC6vBQXoe9GQPEYfCcAcxsUEh5GxlnchjKHOiQo1VOOngqMgBiU8bIIa8gi7FtEBnmsgqGN0Li/KY0nWkZaseRlJF1wRAHYik/W3xV4MY8bbz2/xmZtX+I6bd/DJ6RXeGm/xmfEFvjS/hWNa8KnjLb5yfY937q5wPk02XIpCigAYFy4WCADuCDYS1mUU1+Jz6FNVYXzDMyRlxc21YwnoAmqHN7ecGNqwewPXdYETSLu/PFCI6NsB/M8BfB8z/y2QSJHfAuB3A/jXmflXAvg6gN/+Qepv04Vz7CQrlmElOplQKt8D8FiAGBNgOx0zut5VNuGiE0YljAJsXzQEVevlpLvRoag0nGTHX4+yK0l9KJPyUCbnegU5TPQIZ7ddWIzFrMjB7dhNghT/Dro0s4fGut5v/RtQUuhsuM+5/R3HpFGr2rGqSkynxaHONkgolhyeU6lgQ0GJq6oFFIKsYBwAuRuw9BXEj0IF7nqtC3Vi4Lji+uaEzz17D9/z/Cv4m2++iL/5+q/hVx2/iO+cvopPjy/wyfEV3hzvcDOdMSYlO7OYdC2q07wJrf85vFJsE4CSsBXQHBBxvqYyRkCt/q4hYCj2ezyDY9OHO2bdUD6sOjACuCaiGcANgC8C+LsB/MP6+48A+JcB/FuXKpE5KwuPjkdh+nW38WtCajBe9TQfTZ+UjpJK2mFoJA1tpx81H0BFLg7iTGRCQ22rlZ02Z0EO4QgqwiAHXphmAvKMwGlhrCzSf7km322WK8LpLYH5pAd7rAcxTYmtn32iWh7B6T3dkdU6AKgqoKnD0srFkqCegOm0Yr0aJAAnD6D7WQio01IntQh2eXdC8d9y2ZEVbXFDEnI4YLNyELL/jSx0f4C1hqSeqxGKRtT11h2EksDZ62MgvCY/20BUr9Ft7xbrv1wPxdf/OjmHIkSq9PP8ZgYfMm4+fYtf9tYL/Mo3fwl/6/PP4284fBm/5vBVvJFGJB3c751+Gn95fgs/ge/BXx0/BSIGZ8LwKiEfGPPEwIuE4V0gabYmWuE5HUw1o8xyCKsd0KoJTzGiJHNhOQOBYnaiZRGV9TDVrtqnAK7NfKunHtnYXVIBYvnAQoCZv0BE/xsAPw/gDsAfhcD/d5jZZtvnAXz7g3UB7vxjzj5VPkD73wguk4pEemSUkHeRWK0ShCqsLaex6A5j8F9ukP+XtbDQhjI0yyxNVOA0MzgNnv0nHwe1Cmh1zHJc+AgJC/YoNYCVoDJnnfmZuAdL/D9LgtAVWG4Yw1mThSZgfCk7zHIj1w7nQjTJMwEeE4ZZ2eDMIgBWeV9eqVgDKkLJkFEWHbJdyHtQUqM6LTrTS8sHAEAkCAF4EhcAGJMvfo4qBLPwAEkjNbMEcnFj6bGMQFnVLXe7XoHzc/LAH0NbfMg4vH3COGSclhEDMWYecOYBb6QRR5ow84qv5DN+Zn4Tv7S8iUQZX7t/hrvTAXlOGFiOak9n4QTMmpNXxuFeBPJ6FAmesphvebC8imqpCe2nWY54Mx4Hq5CCFOYlaWIcznlLdGcG8+K5Mzbo6kKuwQ+jDnwCwA9CTif+5QCeAfgN7+P+HyKinyCin5j5vvzQm3QGafYmYy7wdTe3AIDKTBiSlVZlWQoUi4Sg3aq7lBFRSFRcgQGH+U5UmVegrVcjkEb9NwgstWtsxwIBPIlQyAcVHqNxCbobWP0mys0MtbK7CvspQRF2VwktGtT0AHTc9KdZc9JOn4cIz+o+U8WCl1vlNbc3hlrYTLSkPMtYcgcUdQl+OpOrViMDI+PqOGMcZGEkMK5oxrN0wgrGLZ/xbj7ja+uEL69v4Kvrc7y73ICIkTMBs5z7QItYeYo7sXapZm+ShsLDvf31TQWw94y/Lc1iDfxKNX5xk6tUt+zjcokHiOXDqAP/PQB/lZl/SdpK/yGAvx3A20Q0Khr4DgBf6N3M4UDSt9Kn2D0DGx2m0jdtgTdJQlmRgZmX2PylQ2gyWSaWuPNlyWYcz3MHICHMPILfuCmJI7M8n5OGBRsnYPdkgOasx3+N6l7KWNXB35J8mBWAFkEE65Hdkw0A+HrFug5+cAgfoeftiWAZTpJBeFS5yQlYjgmH8+pISByKSkorAFvTU1xkZrpK5LkBALhjkI4v6mPfB8Qxqzw7e2nezNHIiqkmiSRk+zyDLYNRmpys9HwC0TK0MvLNqAKYnB+x/0u/6HfGCSgHk44rDuOKw7jgOC44phnfefgqfvnwAl9bM760XuOeJ9zzhC/Nb+Pz50/gF24/gc/evMAvvXiO8zl54hawjMVwx66yjbfZk6GO92sVuZnmLKTmeQYmyTqVXt75hkO3J7EIDEpmxM0vLmoTomp29Y0v8gPueNesmaZ8GCHw8wB+PRHdQNSBHwDwEwD+MwD/IMRC8Fvx2ANJjXWmMtHcdDhS3QGNoxAZYx/s1pxzgalJDctEsKPMOS+lc4yPUF8DXyAK0bAKm87Xx7L4SRHAlHzAK3VAFz0/N+a4qADGJs9vZqzPV6S7AXnK4JFBhyzWhTnBPNjSIruFwH1RB0ZT4xmYdNIBBYFgSFivE5I5pQCSMmxIIJpkElr+QADmOk3H0Z2E3CswZzdJeQr3eZF08NGur/3iQiYIFAqC2v+23I7zWU2EB1kApg/bWLBE23nuQDWhCoEGsEUBqknQVDBA0qzPgzgHzc8zeMrASnh2OOPV+YB5HbAi4SdefQ++cPgkfuDmL+FPn74TpzzhM+N7+NL8Jn7u9pP4mXc+hfdeXeH04oi0EMZbwnAvKCAPwKj6/zCLaTCdNY8jAzBUZkeuryxHl92e6mhM5hLKziH5jeVldGeh7HMZKde+HO25mNGKtlM+DCfw40T07wP4SYg39H8F2dn/7wB+lIj+V/rd73uwLnsBLdXhIi0sbP62a00QACgvnBvJGe3Yeo58dX6eBQnZc+xfLOpzL4ks4J5YFqhjEWyS6z/4qF8hOAspzB8BDIx8zMAhg6aMNEiEGg/ajiFhHYBE5H4DRGI9IOUR1oNiXWLxUhtKMFLVV+vORKAErJqpxsKrN14nhbeRP3LYdUK9lXqRNwLbS8UPDOVfnAcqtCpE4L9pQJSlYVN/CRk/jb24gsZpQOMoSt33yyiuC4lxziNOecS76zV+6vTL8fXlGW7zAV88v4XP37+NX3z1Jt55cY355QF0NyCdSA9tUTKQ4Wnf2/gNCx4CAjIaE3jJMk0ygy2BQdzx3RITzYXBQ5C5CO4BfetM8Gb9yLINM/O/BOBfar7+GQDf/74rswXvocQ7Ojt0wRvT33FsiQeYkAWWZC5RVyv8lByYRcGevSyyg8XotQB1KWfgnMHTILHhC0mSkIWRkDE/02cTMD9LmF7Jqbvz83oQ8hFi180EerbgeD1jmhbM8ygMtJIM6zkBSxKBMg9Is0zA5UZ2oiEz5puEPMpZAlNmrBr77z4MS+7r+y0paMetezAOGt1TGXwX0o2TV2ZJHXaQE3jcIkAqlJTdZrV7W70+FmYj91RbC4gHiRk4TOoYJNel84p1lPRsy5WqHyxkoJ3OfH6TVI2Cx1vQKijivdsrXB1mHIYV781X+OT0Cqc84T/5+n8Lnzm8wDvLDf7s1z6HlQlffe8Z5neuMLxKElB0EjJwOEl+wuHM7qFoB7kgC38DO2xV8yHwMAgRqmHFnMjPpfTihGwuO0ci8RPYrBsCxqMgO83DYQLc0709UJ6ExyCAOpFIkFqWLsx3aZOG5qoKlMltMNJ00khYJUJM7sins//mk9oTOYhfPfQ4qaruMQHHSUyEqkrTvIIPA5gSxrus3oGE6ZZxelPy1w1nxvxcJuVyLbsFrZrw4jRgGTPGccUbN/eYlwFLTliWAc+eLVjWhLtXRyyZkO5JdiK1INAqacUtBHk9JAz3K9KsuQSnJJrQupbTcMxRyKww6wrGQfgUm0yJiuNVSs5M24K3NNm20AXWEpCK63Zl4g2cQJUHLx4Y67ufRnaGE3uEFxndscYOApGgnRXnNwYsV+SBWJY2TLyBBHmdP7ECVxnDmHG6n5AS437I+KvvfRK3y4REjJ/5+qcAAEtOuL87YL4fgbsB43sDDu8Vktf0f06S32F4b4W5Lw/nXFK4rxLBSfMq5tohwHONG0AMpR4H4UcWMRsyBeS11qhgL5tQdUpRXB875ckIASu8Ckxywinqm9FX3ZBD+4Km22ukWhXKakRh3AGBkgkXqAk0DbBhi2H3RmpGGJbP+ajZbkiInzwOgCL0pNG64jqqXE+IFXBBQgwixrImXB1E4p+XAdfTgtMyYp4HLG8Aqya6zJOYDtNA4MSuJkofCGFmacwk0eYQDRSlH5v+c7dqoOZoKi++go5cp7X6ouCNCUiYQaZCZAZNhYz1051cGMtGwPGgVzPL+iEtyUnBHLwveYAHZgHwmP88AOmcJExYQ7zPpxE5E4gY9/MIZsK7791Iu5YEvh9AZ8L4KuHwjpgEzTlovGO1DFi2ZiX9MoP0fzCQzovwNRY7YEJO59HWEhZV2FzcuJs5Gw8nBVZ01QEZLO/nvfIEhYDuQvaFQfHWzhkOsyiHNLSTTwktYiAccuppxJBLaitOxXvOfLTVMkAYqmAVJBtoIXnyGwdYFtthzqAs24WlCAPEdk2sgmBk0GxwmyHHsLHYq9cBb13f43qcMecBU1pxN0y4n0dcXc24Ha6Ql4T1KKRhnuGps1lzjYm3HIGvRsmRT5CkqwlFENikcbdg01fHOiJN/S8qQRDdt6dJ3Y2DkAg+/15yVsY7PEf72zMLmfAeB0dlbNDZXI718BBJEZawHiSNmJ/dQEDM2uTzagDSvQqQiQFiLKcRy2kEiHGfDuA1gW9VcC6E8S4hLcD0gnD1NXHJNt+O8VasAUnPI5CYjSynF7HwL7QqGbhh9UUguNWgMt82m5olah1IeCzLNjyOwKJq2LxWm188jal4IO77CTwdIaCTMl0d5ZARO33ImE/1VW9DWJklTVYVchxY7ZhbQL4Tep7PGrlmhzfqYPipsWZqurmSnevuhPzs2q0DfmbfMbnUJ8jEtFx/TAnLNWl2GgafCRMDw0l065mBZUy4+dxLDCljHDK++xNfxddPN8hM+Ds+/Vfw105v4evnGzATpmHFF5YB9/mA5blNLNkND+/K+YRCEgJESc4YvJ01i1BSAZYkKGrNEAV7KoeNZhamXmG5MNKjWFf2zEyqErg1oEUGgKoTI+jqqnAHYReUPAOBA2oEABMBN0ecP3ntUmy9FgHQZl9KZyCPDCg6mJ/LwAz3wPJck5WuJLb+cwItYqnJxwxkYHpv8Hqmlzp2s6CL423GcGblHQjTixXjnerfLBvD8PLkqeWTOQSZ9Sqa7LIKNkuxzsUiIKnsSLNd6y5vqcQAmc8WGKR8i5nKJc14LhYFtbDQ4QAEd5xYno4QCBBSdgU9kDH8VmUS1kVI4+gCwInAZemQYKmqSwZi1YURTVyR2iV3FeZxkNTQOjnFi01tu7PscjxGAQVPZe0JKs2z05lrue58HjDqvbfLAW8cTrgZz3hvucKb4z2uhxmfONziq6dnePnsgPOnRqwvJiwYZIf7GmG91l2QlCCbAeaE9dlU0llZAFG7M2lAFEMXYBqkwQsK5EypHHzZumbb2AA1HxN9AyyRiB9nPpWxjmOiMRs8iFswWE8sniSvv4yF+eKrKZDgiUIlTJj8HIE0k49pmmUxMmWkUxJuRd17MSfZ/V9Scf09YXOMuyd0NVUyyanG7p8wDZrFGUE/g3sEbuB/zy27JfNsDKaxkH/Wn40fgXE4QFD7/rpBArFEGBNLk0/Qv4sdsleiPuXegtah5bIq9JW4mKlSks/+zFD1eUU+Ehia04/NRMcuDCSZpcb/MzR9NgCGQFJehahaJrx9vMMb40ls1YcXuEoi9e/WCZ+4ugO/Tfjy8iYyCes8vpSFlgfyicq2Y14N4CWJ3/oghGV16Ki9j7miYlDUZdmcdbd2nwpFTNUYhMmew3eUKitAdeqOIYbGDOvpxEiiMtMyeIRmsqStIaGnJ2AZ4BGDliOQGJI5WB2G0llUs8wiAIYzhJwlRlqEbB1vw2JfGNMr6JhCjnLTuARaChkr+QJtk0kbD0HPIJwDuUdB8C1LSeASLTNNFKCNB69r2fHbNeLRssn/vhjghSckBKr4dNePGmtBtDuHENdoIfCswlWi0VVmijsGaWpzQxhq0gJLMI0cCDmAjgeJX58FovEbzwpXsJIcS109ywhBAiU5vWa6zS6SPQSZ7YgxBlPCOozgKWMaViEHc0KijF9x/RVcaWzwCsJEGZ+9foFPX73Ee7dXOKUJeRhw9+2M4TZhuCOQ5stLZ8LhpWXbzaBJeI20qMdazn5isgStpBJebBPLdp5IrAIhriPA0/h3zOfYRGnSNIk6cp4FPU2jWAFM5z9Mkm1nyUj3C/LNJCSnqlmWaJXVVGgp1y2ZqC18Jz+AkMtRfAbWIwFJIH+ageFeBIJ81hOcNT3YeK/5GVZd7DpnxlfqScoQr7+7RSwAhrTszMGTmvViOHrjD+DJcQdFp+YzED1eKVVBQ/nuHnQ4VLk0jUjfrIEHypMRAqQ2fzl3wHTJhiyxBCB20q39Fjs2Eoami0XGm1kW9CEcnTXPBarGfG02mFZv5agh0pYHqkJB05zBaQ1ehJZbQIgkJiCBcXhPPAGXRd5rmSa8IsbbN3e4GhZMlPGV+Q187vAO3kj3eHu4RX6ecJsPuM8TvvKp5/ja3Q1e3B1x9/KIvEwYTrILrkdhsq+/xhrLnoBZnGsygJSTmP90ZyLL7W+78CKBKDgcQpahXO/qHurKwhlYohb1AfBxjCjBwr2XFbg6uiMQlhV81PyDRMij6LEWE5AHzRQ0pbLYs44DCPMzArLs1Oc3lRPRXdqsNJKCnD0vYDy09fCCJVUYC5qwg1uGsx7hrmW8W0vsRlbTn50uZDA9zBEyCG+kqqkDJmgdDaSSCCRFIZGLz5Z5usYU/Nr/MWkOR6IcKFaBvdgOPCEhENOKAQA41zZkoCz2zMJ2R/8AoCalgBoNuMGAg54bwo+NpBpT4SJi0IY7zRRJzw67lLxkBpYsZjsGeCWkWR1ssgWVkFSb2YNc1iMhnxKWacTdPOGcB8yc8M58jU+Or/D2cIvPjO9hoIz31iu8uz7Dr3zjl/Dz6ZMYUgYR8Oo8YD0TlmdU8RHGO2TNhJwAYNH4hyVYYewdzWfdGGzohmoeaoCQq2gENSn8tyPbjKyNHpiHcv5ghP3IWbzoJlVrwnmClpAjqwCwg1eGWRcRCWk3nHRXTvBAHgvjNruowfiBqSxuBsb74vcPkOv/453ME/f4W0XPIkNUJgQsvsTe0+z/cYMyAWEWEXV2q6JdAfhEDUFxfj6GzemAiKMA8PlpG2H4/lIa/ychBCwbTXWybaV0607u6a10B2lDXsP1nnPwPEtHzgtwPAJYlZYPSSxdjwq2apZgILcWKHx2FcIIw6Q+AhkFDlr0ntu1xZY9vcq+8F99dpAJeCuLNU8J6zjgS1/4BNac8PLZEc+nE748vIG3hlt8Kt3hb5pe4RfXAT8zfxqfHF/i+XDCz46fwvXbX8afu/pl+NobN7j/thHDXxP/9rtPJownBq2S22A4M8Y7UoGUReDZ+Xnn2d/JnXQ0rNgWNVf57kh2fkvjFqMyLSYAQ4G0bnVRrifGaKg7LxOQr0ekk/jc52NCHgjrMeH8XCL3eICe2MyeaAWAWwnGO3jqds//P0BiLq60D17J2Y5+QrHJMQaO761yJuT9isO7Z6w3o6RsPy3I0yBnOJ50HrbRmRB1Jr28dX8Tg/A+Z4280xwKln3Z+9Eryk7AlhOdtA9MENi8N6uM9f88b/wCnryfgJ3063/HGHkjXDouk+4eHHQgTz+uOfEqF+L5rB1fnGEq1nVdwTn4HFi+gZT89BskYbyZFOpmCPObZMDy9eRhojwKIZcniWxMeghmPhCGewsRFjJKjiMbkA+Mr/zim3j32TW+7RMv8MZ4ws+nT+E/zUf8muufw0Qr3k63eDvd4tnzE/7G61/E15bnAIDPH9/GF959C6/eHgEaQCthnuVZhxdKTk6E9Sg7vZ2Zl68npPtR7M2mAuVwKIj6DNAYFu7VVdmNzmd3IOIFjT6bFNon36EsXFasK+JwJEe0CW+xXo/O/lsGJw/NJXHuWSdBAUzA8b1ccjYwCuPPet8CMTLoISIVwZcBO63IEphO7y2u4g23pvuzhwB7chbnq7IkcbV3WwrcryJU4xxWopSjalWZsrm2yCxLOYLMEK8t/DZaMMTOUCAs98qTEAIirvOGgNLzp/fvallqv8cvqK8x/TWe2hpvXXPR4yxrTlYPxiy4mqweQOyyBmdtO1nZU46xnhREKYPW5OsnzcB0x1gygQ6su54sopUB0Ih5JXxtusGXrt7AKY94Z7nBW6oWfGp4iXueXBhMtOIrx+e4Wye8nA84vTlhgbi8prMgjeEsi2NBcqcaP4fAfB80h3+6X8Q86AEoDGCqYWdIXBGPa3O9PxWkxoYERnG341T+YUxiNZw0KcsQ8jSE/ABWj/xdrAOA7vi6+9uhrQBKYE+GC4e0iL5vh7WYj8dwyq5OuK4P1OHY0O9XCQ9mHoo6Y4IvW+h52NRcXW30c7NMRRdgm1tWxziCuWPyVtNula9B74nWgIv5NbQ8ESFQSjzIgrlMJLenql5FANwyEGMNNAgmWhTaa1xQGAyLAUjLIicYqZswz7OYZJirwaTjQW3rKJAwM9LtycM3hZHXtF/njDzJmXnpbsUxM8Zj8iw4osvq1sYArQPu+AZ/hQlv3Nzj0zev8Gw4Yb1OeHu4xc+eP4Nvn76OXza8h/Mw4Pnwabwx3eNXvPF1TCnjy4fnON0/x3BPKow01uAgB2dMt2K9SDOXXXBMyIcBE+6R7iG7tx1VPo2gVyENmU06JQJdKBjkHRL46liO3R4H5KuxHpdp0AWdsF6VzEykLrZ2ToOpVOuhnBw83TKWwXwDyAXCeAfPzUAaTJQWOAcwzIx0lmhL0sNb0jmcz8AoCT+AkkMSkEStpjYtK+h0rpEiILu99YFlrU5qdg15LLysa7EOQNCtzXGelypAy4/dM9XY1bTFuZ2eOTBmf+6VJyIEjFRC2W0UJkYnCjt5yA4h8aAjO9MdEP3IXjhyBusq/vMZsstYxNxg18lWIjv9KnxAtCog7HJA4SeYhdmO5XjwiWuhx0SE4bRKyrGjst26cy1HksMr2E4KIvA9YXxFuMc11iXhZprxp7/+7TjlEZ8aXgIAzjzgnkcMlPHZ6V1MacEpTzgOC47Dgl8AcPfuFeZTwvkTCcsvJYl6uxe9cjgJA84DYRjktCIQsF5LgJQcYnos76+5CTDrzkRh4Rvst8msk5avD5p3YXAzX1oy1puD52X08xpJpsL5rdH1+pixCZAFjQU4v0GaYk2ExuFerp1vypFsw0nex7RLYvEVGM7Z/yaW3H9eFPJjVR4pBvcYu2/vq5AdywLWE4jJz0nQ/lkXIC+iJlnqMEBYf9uMep6YRCA9IRpAlTW7OpuBNNDLTt+mIhCrFPAXyhMRAlqiDt77DSjCwRnXvL3WirmzWjH32Xgai01kPxfP1BHV12KCBoNoMcy5dVLy60gWTEriRMQsBBMxMhGSOiPxQMJun4XAy4OoC3ZE+fjugJmP+Gv0Jp7fnHA9fhJ/dvoODJSxqqp0RTPeGO4w0YJ7PmC9Tng2nEHE+Hx6G6f7CeuccJ6PGG/Nm67sqraTWmw8AIG7M1UHq9BAoFEQjqf8tl1vLOoBiJDts2X9MVMlMVYl+4QTgCzSBBcg6yS7f5rZF785P5kDFqDHry/lGiE8SRKwziIE2HiCpQT5JHVFtjGJ84GMhDPd2xYvUBKAtrsth3moqkFlmnOLVJjXrUq6N++bCNnNb0q4PnTU2KXydIRA8BEgQBaP2pndYcgn1lCd2+5qAlDcNbmOZ3f9KZAoG10pc4FaK4JeO2giiMUJosol1CIPWbLF+uk+OYNvjq5y0JKRmOXcPz00I49JjrVeAWJx7uEkqcnnN2RrHO5HLK9u8N7nBvwcE+6WCZ+9eQ8vrq5wOkz4zsNX8Wa6x9vpFi/yNb7t5j28d7zCpw4vcTV8B949X+Hl6YivLAK7V2XdRz2OW47SShgHWWBpVM/HQ9LjvaVvc9jlh5emwwLpvKi5FJ7sI08D+JBAZyO91JynwT75ULIwpVnTrunCzKNyFkraGUJI1UGfCu0XOxVYBMB4L88bZsZwv2K9HkALY7xb5Vi2QFpa/kV38GF2otcF37y4xyju1TvM5pIh0jAXeZ5r7iTOrRQW/V50bIsKgp7vGbmJiieghdrHzfF9lqchBHRgJEKr2O55XZGGAK+CbdTTjgHeAWCWU4kPBzFttR0aTSt2yAlQnI9i7gEr8wIMelSU6W7tAZx2YAeR6MDD4GQY3c9yWtEwwNNMWy68JBD88I52w5gwHID1IF6F6QxMSmzliZD/2hXePR7wzvUb+Movu8HLt444PRvxl+++Dd97/WW8NbzCz58/jU+Mr5A54T5P+L5P/BxerFf4wt3bWDPh5c0V5psJy82I4Y4w3hOOXxNEsMxiP18P6lOwSuoy4w7c6WnOwPUkJy8PJNmNrayM+S1JDT7ersB1UocfsfcbND8/T5qQE24lsX/DWXZ3O/LbHH4klbfcM97LNVlzNVi95utPK3v/mvCwA2MkeAf+HRNJgph5KV6TNjUtBfjpXMZ9XfUgEPGJoOMRfDoVNNkSgPo5WsBwOpWNxPR7cwYKc6s64EXVkXbRu84fnutu3kFI7JWnIQQcRdYdRdZx0ZEoQm/bkd2sEuzVJjQinDLnnijFzXljGFC5zrQDadxEQo06nKBBgImrxAgkIciABEoMXgGCWhIGEuGRWbKbjwnMGcNJtj4LWlmPANjgNLBeJSyZ8OLlNT6vqbFuxjOuhxn306jnFkoffHJ8hUk9Z96e7vAdb72Lrx5mfH26wV26wnJMyPcJNCeka8mcO96G7l2FO0gzYzyxqy0SV0+e2DMfE8wkZ7s9AKysR4IlS7lOzsZb0I9nZh4AOzBkzOy5F+wI9rSioIIEDJrbT4KEuBzPdrLMPjJG45z9ZGb37DT04tYNYAO7dSxJk3tUQj9YQ9jY+bhYW4uUz6nwjFiietl8V6m+8Teta3MmYRQ8UZg9fWIQpfHR8cT1JnWqiAIgDkjsqMgrxE5voBZNowgYdawoiTKKJBfEoaSfnZCbkzsXuTCIJkKikphk1EQeOqkIsrCQZKc39DBkVjt5wvgKSEtZPLMeOEpZcwxqFuL5xQFfywm3pwM+88ZLHNKKUx7xucO7uOcRE634xPgKt/mABMb1cMZ3PfsqbsYzppTxi5mwLAPW44DzMiEtBJohPgwnVRFWYD0YR6G5+gZovgT4osxTcjfeeGbjOg1VCnDpK3iQkSUDyXEWJoB1l7Z7JZ03u9AB5Ch2C5QaZon1T4uoAPGsxeFuccet6qSfObieJyoLPS5EZs/C5MlPbBdOSbL/6HF2m1Ovbf6iHCDqde/p//G3oPrSurrvjJN9mvh180gVCrYe/Li4py4EyP2izQmiIe6A8iJK1OXzDErkx4hLzDxgacTB7KmzvAShwGa6snBW4xAQFvfBUgDpfW2IpxI+fDp5W+n5s/r3vBRprgFEyFmCaABPlZUAcJYThPK9ZDFebgYQC2Rf7wQaL1eE+QVhfnfCcjPh/tkRP/uJa/y152/h+c09Pvv8Jb7r+dfwbJA23a0HzJyw5AFjWnFICz559Qr5LcL9MmJeBpzeGDGfRyxzwvl2xPjeoFlzSITSRFiuZTHaUeqjLkKwMPWACKrhBLdy5FEEiJF5yzVE+LEu7ll2eNnFlYxMQpQCxZ2XVvbcgTZ+w1326D4ASGcz+ZUFn+5n8GEUT847yZPgh6/ERZhzSblmJk9mGVdDBurd5zt/ZhcAbpa2KesnK5XEoGxIVJ8t5r/k33nG4CHV8QE2b4ZBHIbCgq4Qh/lOVEJMzexhHfXKg0KAiH4/gN8E4Mt65iCI6JMA/hCA7wLwswB+MzN/naRFvwfAbwRwC+C3MfNPPvSM1mOwgjTOnnL1IjSVPAKbNsfIww4icBOLdZihgPLOer0MtguFKsdb+GyDRcIb8CLOH0WIaEiypoymNfs1Bj3ND50HsRxwZgxJwlvzSKA1wVORZRSVYU6Y1wnz3YCvXR3w6u6Ir99f42pckIjxieMtDmnFmFYgA0sekDnh225e4JxH3C0TvvLyGZYliVB9Y8ZCAJ2SE3a24159TW33BCyLkpmZYecgAoocLP/eIH+TTkRLA+6M/8AC0E7wY9mJxZEKgGZXZg3sIUyvikBPgYcY5gw6Z0/5JpmFleSLu38sJowza7yECA9mBkXS2QQAAgFICZ7sI9YV70EHggeHHnelbi0JuvNLZ66NJSpEdMZF34YJp7DwK8e7fnkMEvh3APwbAP5A+O6HAfxxZv5dRPTD+vc/B+DvA/C9+u/XQc4g/HUPP4JrNQAB9sT8zcGrqhwplivrQOWn7t8FnqAtza7QelhVAxk8u6pc7+NYJvaylrgEC1HOAIhLUgmX5kIWypkB9p4DAM1fSBK1Rovo3KysfVolsk7ONhC/gvUkabZOc8KX7iYMU0YaMk5vjXiuSUquhgXnPCCD8MZ0wmldkcB4Z7jGMKjgGVecbwg8JayzHO9lC2u81Xe31OcrQKvs/pZ2S0ycgdCz+RhfUYUZJ7ku2vMlUWeB82mBE4PDfdYU3eQBWABA5yzEnp7wK6pjGV9zPtrwSRbnrypftWtvck9kuT4RgLxdeLYJGHlt49yYqLul1ffjZtW5p7L/b9qRUaLl7O/L5UEhwMz/BRF9V/P1DwL4u/TzjwD4zyFC4AcB/AGW3vwTRPQ2EX2Omb94+SnkEVU+EA0yaAkQNh091kIEujrKb23q8hBoQXFhG6dgixq6qyU9akqFDa/hnMRBcw3od7Qs8LP1zNcAUGEQJHlAJDQvZU1YaLOdt8cy0YbTjHxzAA1iVlwnsa2nxBhvM+bnA+Yb4PCupCBfjwB/dcR6HLEeGOsN4wvvHIFDxnhc8fzZPd64OuHtqzt88fZNvDwfcTePOIwrPvvWCyw54YtfeQvXz05IiTHPA04vjuBVIPz9Z+ALcnxVMh5r6IIsYiXwypFcJixY4yMgjP2s1yuyMcg/nNhh/3ASb0ZxMBKvS+NWTHUAgOFuLnZ/TQMPqBqycPHlICqL2cLHAVDScTVHM1ZL1cLwxDNqrbKQ90ptDf76pgpIsFOY04G8rjaaJr6A70/VZscW0kqpOAzFktq076lWgQ05AG4RacsH5QQ+Gxb2LwL4rH7+dgC/EK77vH73gBDgoMvrQonRaWY+9Ms79lS75jxvOxooxI5fHwgYy8QbJfdqBz8mX/gcn3kOPbqupa0htNb1RwCYAbo6elsBeKYiHpIHMLk5J4lnZLpfxO6eB4wLg1MuBBkJSbdOtqPKAl2uCcORsKyEdJaIu/U44p37Ee8ON/jC8DbefOMO8zpgngcQMQ4HcT0dpxWnkxyZxBlq/oRYOg6sJyLB8/I5QtAN8vCKPJafBxUGCSH6kjGc4QIAzUZGzHLEGsvndM6u9+dD8mSelmrMxtJ3x4H0ZB/z2QjHgMXNAYA78uSsY8V6crUK9HEs+fwBP9FKNplwcKrOE+e1gi/KJkGrRWWqz8rmtC3amgZ9DreHx4R8GxvnOZuL5vPyYTiBhwozM1F71s3DhYh+CMAPAcAVbrawpf3bmH9g/4Uygzsn51TOGO33IcTVQ0P1N4djHbfO2n4rhCJSlg73RJtBH2MWV7hYVg00msZaVRgGOS4s6Uk1zOIbqK61dvwZ7iF673US4uwMzWrMWNWKkM5qfz8ylqUIxfcYYJZzD4iA+TyCkrR3PQ2AxTF4l5HDe6vFg3sUHdjhH1Fgky12wPkMj93XhW4WAznGG3qKj9xk2XxLnwVznz1jNXVSHY5ClF/M62eh0Buzmu3UngNRJRqGeh7ayUx6ve30Xr/zDLXlyn4j/c6Sg1ZtML4kclhmjYqlzeYEdAWGvycAzhLEtlc+qBD4ksF8IvocgC/r918A8J3huu/AIw4kfTN9kqtEoMa6ZxbffuhCY6p3W6AWDmpOjPAq6lQxY5FDqBBuLPUF78OxBLz4AJv9VlNiefCHBSOta0hgUrst8+lcxZV7G2PHnM6Sxhuj5ynkFSDSyazPz4cBw+2C6d0Vp09fw8xzt9824vhOVn2b3AafDxKLIA8FlpdXkotvYuSJQWdZ4fnAIPUcpJmQr7Po/bcJ+SDZevMROH4luUTgJLxHWoPfui724cR68o+gBzMPRh5gvBeHJFJ3Xy9krL+mG7ubhQdIqbgtM0sKLxWgBMByKLolAIB7iBrSevVKv09gjK7v0+FaeB3OFWowJMlhlzeHM/cyNbUy5KkwCO9uyIAfoOs7dwgacm9GQza21m2er7oWDsHc3boMm3CyuXohqxDwwYXAj0EOG/1dqA8d/TEAv5OIfhRCCL77MB+gxfT+ZYHF/KcxJBrBzktXXn4ldwCAomIYFB3qBW2SmOOprlo/UiomI7cWcBl4zWhslgbWlNBkkXeAIoLQzryCY7LNcZRJksMOMk0lW7K1OyXIsboMoiy7d0hYOb0rpykxAW+csrvkHl4CpzcHoVzOjNMbCfkAPdKsqC3LMyCP7Hn40lksEcsN4/ClERZnz3eazGOA5OdbAEmBSCXBKIDppezu66FclxbG4ZXu6iSWguHMwRtRicB5BRJgp/mk21ksJ2qjpzkDa+Oam3Ox1oxDCfoxlW1dZWHbPDNz3Rq8AzkDs5CjG5VBn+FPNCEe3dE1szKlIigcJUTiOjL/lFAx+P4+0h+WbEfmUnCka+6p1Gi7HwVVXkoyCjzORPjvQUjATxPR5yFnD/4uAH+YiH47gJ8D8Jv18j8CMQ/+NMRE+I89VH/V8H21RYrq3Rf9oyMyuCABH/SxtgUuF1f6ZywemxB2HC9mTTCU0xI2NuEajzMTNh6TQLVPuB+iqjtbAjy56XBasV6N7qLrfvgLMA2MPIt93pJx5oF0wcp1RuRJph7CcIei46t6wYNEItq1HsOvrz7es1sO0sIexz/e6k6YCDRJDH86ZwwquABI0o6I2FYVAMvqC84yPFWHd/g/I4K5kHzMBZXlVM0zyS5d2H5u09XXF9fzYCf6z54vaeXqxdktLcR/YOfe7OxVZqfmeWZOvCAIHmMd+Id2fvqBzrUM4Hc8VOf2IdBMQPqCmmCRgUIQ+rUdvTvAp7ycQeO0ZU3RLPxep8S8bdH7MP4eJL8zxZl98jBQLArzUriCHKR3Ig1FXcGe7UjZX2OxgcJqZw2qMnY4UTlclRnA5PfQumKcVyETjyMO7y5YjwnLswHjfQafSVJrZSEQlyuJwfd4/HvGci2OPuNLkoNTFdrnAzTrrhzDZRzBeJJwaCT53oTAdJs9KehwzhJLALiwII3oG16cJNcASdJWenUPECG/cSW6f5bFR+e5LESbF+Zv0fjcy1TJJd4jDSIIoPNHS0yr3vWsm+fCG9nCjyjRBHjrEmzzIpXn5FXDfT0wLczDVs1FmK8d57mqxHsjh9BGLu6UJ+ExaKXtHIFVa/lNPsj/rpvZwhVSh8bJ78/zqej7jR+CfceZ1UtQn2MOPsEZw9WD4PEF5k1aM2i6ZzHzlO/8WTao8wo6HutISGYhrmICyjgpiWRCai5EujsJ+z0OSO++ksSd4yAHdkB21LTOSGNCOicMZ9l18yFhPQ5I54zpFbkDz3qddMFKnID48VtyVLhNPquP/6A6vHkxXr2bPWuPpedOq2Y7Nm1q0OjEOWsOwOxRh0buWfg1AKSXmqAlwm6zrFhEn/adf4xRfB6DnwFeKt+OKkFn3NHbJJ3RszA6+wAVR9TCfslxOVR+Jt2M2v5MrlFsZPp3CltftARiu2mGa3vlyQgBii/ffhdLu5hb5wj/7kIxeESpsKYx8MiK7RBJN6+eV5iqDNEE6TuKeX+Zbu8nAKMEJFl7bRCHsoNQY0KqEAKMNJTJTGv2qEgkSSEmJNmKtAyy+FaZaGTedishmX2dR/VMVMKOUM72Y4A4V4E+EosPvTdhuBchAyjLz0IWkrlws9wH1kxLd0s5o0+dpSSWPzvk184EyI7aCru8eWU2ZmAXnEbSGskcSELWPjJVjMaxEL+tmhgXemT9299bAXChPKjOWokouIMAqrn7CKegvfI0hEDsM92dAdSsffw+5AnkzNizUNJGwrOrCcWUONTumFZMEpufAoIVwQi8uFPP8zaN2TyDp6mQZkc9XYYldoHMHm2IJxGEgQvqkL8/V6GsACTVFTNwcy3vq2mveBwKb8AMGlbwLIk8h1lZ7OPkh3jSmjFqfgNauBynloH5+SjRebd6zLl1zyT9QhkY7rKfyMsjiQsvSwQlrZqae14lpXhKwi28vBOVJqUScGWL6Dw7w28n+1C08Q8G7cXj0om6ED3opr5GANiOTXamgtV3PsPP72sDc3qm5VjCzh6zXPFaCOd24VeORxEBtAg3s2yGrV4fdn8axJRcIYN4r33XsZ4DT0UIAGURrqvHBbRukZVK0JN8lDQAA+V36wQiYUzNlyAufEcBugMPg5iIG/3QB26aXBWIB0IA8GhDgYOimjgyCD4I9PyZoIFWB43+6Xbgh6kSZsZKkv7b0cHtXdFVhwS6vZeJOI7g59dAZqT7Oz9wBQD4MPqpOTwN7oFHDAlFZvHESyd9n5WRs+bmm1c59tz6dl5LinULq85AyhKkJQE8J/DVwV113WNyUru+7cLxaHjm4nUZ/ewtNTpn8JLFCnCYyjjaBnHeusiRHvPFd3dlYZ71EFaNDjSh4kE+VizexIKAFGmQzbOsKqJuOLyukpuxgww2KNcRy1pU2nhdZvAyK3qtFzRbUtRm8T9kGrTydISA6UpAMHHkzcBaaaWefx/NKFF6Dv0OEWFTS9+i8/EGHTDWOuNws2lsUjw3DkhezjM8AcVQnFI892EX3awQT74YIqo7V88vfVlA98pbzOYJpxaFu2BbvzuD5gF2mOhw1sW95JLrmRmJB4HuOSOd4fcjw7Pz0DqDj2JW9CSjigrskA47/ozWDOa55CzUvtp4v6mXnY9tPCHK+s9O7Y1BXzmXxJ3+GuzIzC07Pr4N3L+0iMxLMc6rYKaOpXof894LG48XShC9qc9fRXQgl283xV3d/6P0GPxGF3mJSKaEHy8xoxEG6nW+wC90ADPXagaGZrINpa6IJDynXJkwns0lljXEkwfvxIpUNGhnDirMHpUHv9fgHwPIEvRiXm7DoagJQYflZQXdlfOoaR01yQlkZ7aDVk9nOZWXyvmM3jb7nDMQMvT4s9YswsWy3sQzJe3svGEQFaU9qWddJf2Yhcia0DW+xPgUZvA4ojrQ1Fj9dRUUEDiW2lVXoyDDe7jaVhGOTYq6MD8ojMVG928SfDhCgBF2ZV6WBbq6C7L95inbnUXNRVhw2em5IgyHam77b+18vzD/gScjBMh3fwq7IoC6Q9u7TFeKSR2aRUijJA/J57no9HY9Z/C58Rj0BxePRR9sc2SShzt8SwFmV8RSIPaqSWdtMEJJ4x2s/TFoyUtmILh+8u1dpcr4WYrxzMBFs9xq3DrNi6TN1udXPg6W+YepHKs1DuLBqIs43d7rjsZyupMiCz9009BAzMW3rMA6i0BQshRE5TQjbaec7RgEQCLgcCVpuMynXxe97/LTqAfHBL3fCD/SbL2mEthC9oCxsNEE5zM7RyIKJR+HBon4SOtGZGqfIQRm0hgULqZvg/MdByGpuujxJUdm3GjY0eBmw0wE0ATWZ3pbiftzXMsTEQJBukWoxHmrEuj3G2tCuzOjhuqVEKFUf45/B0lLptsl1IIm3EtDTfpEK0J7IGe1W8d06NEWjbD4Fc6WQJc+1ONlKX7vMdY9BlpFl1dATJRmzbDIymGojwc7zypQgsOOlfuTuE7bQg86MU2T+kHoLm8CziwZpntH6I8V1XHemYvHnxGlVWRp0j5ZvK9cX49WgGhqNQEBbK0KPfUuWm168zCa+6IHX6xjkAAtbudPj9O69J05l3FDAHb8aOJc59j+nfI0hMCOGnNRv+FGKreWA2Crb1kJktjRhJXKrptKVFhUOzpcgRdf21wmUXA68rRP4fy4Kn6g9Q1IzWS1tkVdNpobBxTTmP3t/gnBL+F4rBYhs6gW0arAOXsK7tqVVlJryTkMgbwz55xx9F3T/fCB8h3C7mvtbFhx50tiaXT2Kp6j0fsBqHm2XNuLzd+UdmxbWN7mvGgRYhjz6P9PxuUE8k5et7OQrf62HWjWhM/JHaK8V0+nPA0hEAqva23WaBMkdOD4ZmdvI79s8UZyxZ4TU5vtdKToWnM9AToE0MbDkMJk0JRS1XU2WSNB1UQ2+j024dUKUAKXCv8Bg5xBNybLu0BJdnn77fauhMwChVhbhuJwk7lasD6p1YLhUDuRnr8nApNfvZJ6TY83D79kBCkjv3qpXpdJ2XQdz3DsfBsO7v0QhSRQn/Hn6kDaOOtYnd7nUb+PSWrsuqiPmxkuxhrwsNHbq2LjZ2pAmDLRzO3z2NoVS/w7qsY2/+cOSejPJxDCBvYNzifwjS09QUWpjvBLemoQyuLtlShEvJj0DL4A0ZOQ63nSaUqf9a28u/T/GLC0ez3gi7o70YMDSmUbjx5r0WchlZNrgYAScjxspUxmD55ZctUnwpGc6/aac5OTcywLexyLiVPdnr2d61ovisjcmzCwRagu0VGnj447DunbxJyRrANqa4wL/lzD/uhslAMqAtxvpGea9qLQ3gnj3hyM3n9uhkgO433uNoy+H5ybCy/W+sBEJBHJwt0SBdaFOX6ZNvwYyqUFvnPDRifqqhHOGdikaaBWR5JXDh42cC1/0CuN/u5EVrPYK13UoPmlNpQv+88FaiQSrzNdOQgsR0fx/6hKZJYFbIt9XeHZde06u8fTcod3b91X/domF34ui97byqpHt775tBWk1XunVI+t+Rhs+rDTttwZg45Pv3/fqys+4tI49eprEa3V3Vnwu6ryJTS7U54GEuCgIwUpKL8Vwq+CPOYwERlXFCLEJaoRVusqLGnrZ23XRt0sOg85TEOpK5c8B9HSAEA80XSBlGQjdbx35WAUnJnkb1N3qGSYjQvM1AsjvAAh48ZRPSH1nRz2Wn+UhSU7r5qphkFgvb3n9VVh4cPuTG4GlAXDp3NRQWycVtQCJDo+DQMArVPjA8ShaiyZndbVoX0M+iLz0ASKMGsSdthY0OFQn8RjfgrcILS4MzaCXdCSqkBBbTDVLDL91fxp5wyTo1ky5GR9VS36gpwqItjGyWIOxmPxaoyWooZrIM3WLNV2BHJTnoYQIOzCnMq8YiWytg61NFtuu8OH6zYoI3a8tyXt/xYFVXu9X5OLWcgG1C6NLqSRgAqeaBx2SVMVnG1O4f5wAKVlTvZIRhcwKezK5X/X6fUodiLysxHaxe/Emi0AjZt32/7aScxqPEW2BdosXHsOEcCpCAD12jPU5IsQKBYUW+BR17c4DfU9MH6HKRCGLeFnsN1VlLCIGwTlpr9mgzK9vjVrV0Fr0IXYpAaLpu/WPd7dgU0NNdf1aC1RtUsW/ejvQmYyb4oIs83XAJ6KELhQdnWeyoEi7V+n11ZCpl24rd220tV2rtl7lO3APV5i6LhCb/gAxuaglV5pJ3WvDXFHBOo6bUEhTFpzwOn5ysed2dGZ9lPrHRlNdKVR9e+VDk/b71FzG5v2dyw0bsptS2fc3PmrV0LdG36AEi6l6to+aDs3L0FzvaC6f/OdttGdgyLnFX1s8s5cbMrTEQJ7L9te0xAsDlOtxPubrCpdVAHUEypOIiUNXSL3BIF95ypK2ZVkZy7vZT7ePliW4MJgYuAHNm0J7YzooNqlTM82+z8zcD67J+PGn8Ecb+KOb38jqC2tA1RsV1yozDBPwIqF9/5kV5XMTwCA6/5+1l54F4/RiESo9rsjK+tPc8kNfUw0BuFQ5oOjq3gvghppc6JdYK7awHffDWEXrUg9VOCPpkKAt+x+cFYz68EGMdjHcH/blkg47pWnIQQYEhwBbCRn1Xgu11jyxBg9BaCCRpX/eSyPJB6rTg9eWBX8y2FyZJbFNi9gHYDNANsiZipEGaDsv+3EQZ93D7d6F69ke1h4ZLBYr/NFY2oGgpDRetgERXMPRxfYiBa0vqpvImehOr7v8uE6eQZVEXYAwgk8IWdDIDJbNl+OkVOBEj63gtrPslTP0dL/YW6EzcL1aRubduOx33Qzse9tV94eglsLEl/UtpPDHLa0nqgmWD8lkqCiiFDDnPLno971Y1suRRE+HeuAEiUtdKn0/ECmtJ2wgTzNQq86Tr6or28Y/f1mUvyj2OBjPeH5GwmfuYaYrWNTFICR/a6gb64/x0VoJFa8V+tqXWOjd11VXxM/H3PkV3Z6K0ry0ZBq9cZ2+4YU7VpJ2neL5lGvLxCRbR/slZ7TkTem6fv4uR3n+H+4ruKZgunPXXfbZ0fuoJm3mznafZ0gUHrzvvd+D5SnIQSIygvpLs8GsdV32n53IsU6PFF1TQvnN2pGSwy2hOOmbTtdZLu6ObzYBFX+wSeHEWmmhqhDjT8rwsh2wujibtOiRbdiZq7cVosw6Ji77DslHau6VSVgg/QW0NLxYZAdtekXDbEuIbZCXvlnj8xrVA8r2n+8lnfyY7/094hyOMQYlHdohHwYu5iqK2adjhGcfp+V6FcSBVCcN0HYGxHsunr8F9sSrVHtcytL1wNCQedWRMJ7sP/pqwPAVo8yPYgD1LI46wjPTA9sWdtYp8IwUyFoHEU/D3p8l0Rp9UsbHIP4OYHzUieIaEuA9j4M0aHGZFb0LjQCscmwXL6Hn8Bk/EAFrT0WY6ggsHnQ8fnssQPurBR3/SBI4+/2maaxPL/xvHMTG+Cn6Tg3Yddq+LMLMKC8i0ZqVkIyq3kumrtifyEIv4i8rI6WL4h/t1YpzzJU+tBccysTYSyJ1HNvLZ6kzaIHz5K6LgfPQdf7ByDM896iLpaI0la0qlq5qar/ofIgEiCi309EXyaiPxu++1eJ6L8moj9NRP8REb0dfvvnieiniegvEtHf+2AL2qKSM4ZM9hyIum6Svbri//Z1jAZTPb716d4tZhIK9TrZ2GOm7ZnNbl61u1EV4oIq9uJcdvzAedi7RLu615mViDOi0BYSpUqFiKig/Wd1VvX3oHhuFiHgi9QXqpnvLvXRzs7nfVwt7LX4Udi4VaRlsxB7qlz83d+P64UWkFuPwLPvN3Eoel0VCWj37CzQNiHInqq7iRS00lGnHyqPUQf+HQC/ofnujwH4W5j5bwXwlwD88wBARL8awG8B8N/Ue/5NImrEVK/Ukq+KmTau4JJtXr/rvnCPTOkNgN3fUxvav3uDElWY5tkAaqjPueYSWpUgNxOxBy/tPYKHX7UbNu/Kxsxbf8SFE58Z2xsXVLimqCNFvbBnORvd85Zr3Yljf7bPN3Wv5U5CYRVy0VpRx9vX39V8xQ753Mw9q9937N7cs3Hf25AMMcnDylx5P6Wtu+3HPVV2b12E8oEOJGXmPxr+/BMA/kH9/IMAfpSZTwD+KhH9NIDvB/D/vfwQ/T9RCMzRyWSQR8+Vkz9S6MR6993A+RZN6ITzAI6GR+iZdCok0kLL2G7O2/Rm/o61XliZlyzIo9OeMlmHfqSd/x/aHc1dpkJFKwXn6nBLjlFwgRgjGuvjuKv2SJ08Nws3xuIDiFmbook2n+bieKRtEqcaVMfO+/OHoVadAIXVQxGqQLnG+nYa1f9+qDmGdWfetPxAS/Da8xPBTY5xU1lqs5ynBFOrUe+ZbgFIJU9A/FuubaxgygVU1gms8LT92kael4ImdoDqN4IT+McB/CH9/O0QoWDFDiR9uET92L8KerpNKnvZpuyhgNoum7FJzRwWjNfTcAzGJWza29uduJ4UG36iQyhVv+XCMAPYLvz47KgjdnfMtX6e9SMXvbykUg9C6UICispXwQJqonBu4XR81yhgo9BpoXbm2o5v10UI30LhGGHntzb8Rhzj4HFnv11SCX0OWHvMfKjCgLA6EoymZJOBVS6HByB7by63/gVtSHF3fq79utryoawDRPQvAlgA/MEPcO8PEdFPENFPzJBMNCbZIly6BPG9rh1+oB3MHlTcNqzfJY8KStLPu2xt++xmYW/UIKDqi+o+g/w9xnqn3Rtf//h9+14G9XswN7anZeQfA3N7ELV9TuQY7NoLE7qrjl2A0L3kn70x3ltEe3C+dUbbc4fv3Nh9RssJXLz/gkrykVgHiOi3AfhNAH6AiwL4AQ8k/RTbrhbj/LX18p9BJmVSvR4beLMmNB3WLkLO0VJArvf5JdFpSVWQsjhDuGdvMIyxtvZFj8Wwy9KQPN0ZjSNyzIobySzdbZxwUjXEk4xauyJyCKgpHSbk8+y7mKkpBpM3PvgBWVQnQjU7cBujX6kKsU22E7dttD50B6nym3vJWQkIJQ3T9r0byw0AV3UqNQeAB5BpYJa/b2h7O/+q9HZhTpojGJYG8TRjXu6tnX3q7qvn0t6CTYdJCeK1mpvW5taZrVge+huElQ8kBIjoNwD4ZwH8d5n5Nvz0YwD+XSL61wD8cgDfC+D/93CNYQe0xRZ3xEo/LxOKmwXmte2hCLtvsfoSomohHRkCjVovwXBdeU4jtAwm2ufwbErZn+91cMdmbLd0EkoAqBn2uLhaZLFiw3H4xG/hf9VXQx0lGXmQKJTtPnvnViW5pE7F4ia2Zie1erTvNou6vc5Y9yijeruyunbXVoCmTY26t3kHFzxDvSnYOPmG03AeTXHVpCcYGg4rejm2/ILdI+Rv9FV42Ir2GBPhvwch9n4VEX2e5BDSfwPAGwD+GBH9FBH92wDAzH8OwB8G8OcB/CcAfgfzQyk7gDaO4yKEbq7p7cgP6kFcS+pOBVVdD+lou2WP0W7ZaCu2AOxfa+KyNu8xwVZfJVDD7xEt7EzKqsoOZN6dTL12PQSBq2ofmKyma/eY//Cs2JauGmX3BCHYVRvbdjXftyRdVXfTjr159ph+qcpj5lzbhkeUD3og6e+7cP2/AuBfeV+tuNAXPvCtiS4wxZuMxLZzxF3ZLu+QXptO6+ysVQBHM7njLhHzFoi/un7HCv8DI+zw1OrM9UQifxzX3zd656adsV1VzIm2M6ftO8cYBq8roodUsf5Vm6OACdC/hvxcta34VhTVoPjtP46q2kOCj3HFNda+V1c1Z0I2ITEVFkLX7kmHciaCf08JchR9v11VubRoWw9YoEJg28AiBhBUJRubb7Q68A0vHAahNzCwn7SD8+KfL0dgDXVdBhV7u034LSYSuUgAGQeAwhtE040l4TDY2FRQ6tghOlvT0cadtKeLWz8kqlJTlXbA2+nQcplrG7hxEZpXD5yRz5349zi5gg5NA4BhQBrr0OkqHp9zOT3aJHpP4FeqYBCYUZ8HEE1oTqi1LsGxj5vNoFL7WmtNWHy9eRRNj5XJLg2lrQbVe2pf6o//Bs14XSW3oCEkF67jWP42z1RA+vqMbvlQ1oFvdNk1lTTwfOdm/3gRDnWY6Y1OvseyBpjpCCU+6wIb/WC5oLN5G6uq+5aO6rqGiNvtlwi1O9dc6s+umhLrMU6hbWtH+F16L7+n0f/rnxvovqOebOpEUPsaQrpFXH4Npc09lQDAzrzqtPVSuXTdRlUN7wLg0erD00ACCBBHd/nqe6Aw0iYJe5xAYMZhxEynLumskG3YU2f19NJA9ER2wxHDWsP8MPni8ytk4sJEycLeRGkY73oCFWhKKfedQNzLLXzX6RO3FMxLUVEsEKaFuBfKRm0z9BB2wgrOWnvUmSUuOn+v8N7tSVTFQhEO6NB+dTSmUPnR/E3DJbR+AwL79ZlTs3RaMrJdkEBh9NcVoGmDAOz6lm/w+U7Rczaiij7C8Hb8daEOEFw/jKXHKpvrq8VXu/QNJq0e/N5DGTGF9EZ/jMxyJzlD5fHWwvH4TEqi7zbWhrI77ziSVHxHR9e2Z8Xnckhf3XIHod74PD9kxawJNhaRnVfzrZsbm4Wy6XM3N3Z2snahxUVNqatuVGPTQ1wuLIZQbzHL7S3M0pa1uq7llwAEs+JQhFdnd4+Bbt7eRBWjv+c81M7BjTk2vg/TRsBuVej+97E8DXXggqDuwtgdPXqPxd8l/sLfj2VUu8/pQM/NhA2Tvl44FyBfy6637WwZ6R7kblBG91k5dxdLC4nbtrc75abtjTrSlkrHb+8N71dda3/vRc4130WuaQP5m7Y4MuxsBLu2/d57Nohi85yAHON7XlSFQv0VUdx5Tm++XFIrnoYQaMqlhetEme9K9aDava2uGr/fHEhxQVfe/N1+16mvtzicFGoWrhE4jzaR4fKAVmUPmbSXLUvdB7Y7x74MwsHZ78A+V/0b+jnef4kpt3FtBcNG6AUy9FI/tHPI54nlf2j0eL/ugst0XOAbAXzh2YgcCSV3Zrq0ccX2dDeLnedXgqwXxNUpT0MdQL2buY4XdXwMXakHNJApwrnIIFOqgzkCg9yDXAa5e5xDJYmjs1Cl64e6fUfdyY0QHT8a6e3PT4QqziGvQBrUA3G/b7xPesUhdNIzA2Vxx/bUAk2tFTGYK9TjfzqPE+/vJMvQJCHZjmmPwqpFexGCA8F7sn6/KLCcITdBradIMaA6fTgn0Mt+QNpGUIfnV9aFWEyYRdPt2slL6PXXFq02tbhf1/FMtDaVbNPD5TgQLU9GCHhpzTMXritSL3TUjrfYnnlGqqrJmA03YCUMQg3dwiKsdpiGzwi7Qf0eufmtEzTU6opcT6D992hUio4+zT29vaqj2bHsPR+yTuzUF+vxjL+NHl7+L+bDPaI01it9UPfb7vc7wnFXzdkh2Ta7fqd9rWDbEMlhPj+EVDlzOAlpv1ziCmJ5MurABg4CpcObiV5Nlvi5B9dR61otCfOoAW3bE59hyTYuQfUAmwsq6JieHluCoOzC3vdbmnfefYeLTWqgfKeeDbQP/eGLZG/89trQ40F6z+vc5xaEds512tHOmx4Mr4RwHGtVQ6p2xc2uiTyNdb5vdMe5GgtXf5+8dQBlp7Ygia0Eaz3hdPedTZJ2du/gLGGTrntGQdhxrOOL1YDhpwH5Qm9MXc3niu2P2Y9RnE028RGBgW/bUOBf0IdbmBcm1WYni6pVd7LV71Mx19Gc18QLVFyBIaCNx6K+Z15FF+6RclUfhTIMJRgrPHNDKEa1LwqfsPP2+CN570FPQLJ5Mm2TlLT91CEpqwC32Db7bNaWBrnKd/ZRdu4qACgKnxhQFtrWRUjcosv98mSEgE+ipZ94IV5jgTj2++7hI2u9sHs7rum622faopdJ3pJj5d4Au3TSVYsA/QnVNGLzvpv3sXs77Y+wr22XqwTB5LeB3Y1lYG9npFGv7wVWdZBW1a40bCZo0e/XTbvjc6jhYh4qse5H7aSB46Gh2YF7fSKNrOrqMfItzK83ttob1EvcuJrSJhLxzaIj4EozH/bxeDLqgJWKkbayAxHbSdOD97HsQs52AsdrelJ0Rx/dPLuB6w9aAC4Vq3tnQHsQ1QVr/K19r3Zy75jaYlx/bwe69PxeWx9se3h27/pLdVXtbnX4xlq0V88ea3+xrU27qxIWbPtePStB9xkdYdTd/JpnP6QiPg0kEAn4qA40u64TaNWulyoWfpN2yf3gtyxpIWJqQeKZXeP9gVjaizFvJTKNwXy2LKDpEA6LrHc/r69uYFW3sdvE4RktO76ubnnYOKR4/YFptwAeZ67X7mSLTHS1u1AngCsWZ7GDM07su0RA3loj7DcyNa6Jmd9YhGx822w6FdopDlaVR6L2Q3WN3R4dfyph0iza6KPf8+BrhUycp02Mw+6mkQjIJTjOrGj+Ll0ricRnpJGAO3TL0xACVjhXkWs9mGy/tbt/DeH7kDcu0p7krSGkdTJXk6MLM9tF2ET/VXB73Yeom+sbnmNzDFXUbx2W16pBpV8+xCgb5N/TR6tLQ1670P64UE0N8QnZs0K0QpCKOzbMgzFYUHr9H9Wu/Z2y7rtajdp6X1qbfYOgJHOhedfYd9u+2SnKTfmfkZxsTK/umm6Cyn4K/M3e86L35yXs8uTUgUulWuBOsvShzkWY+PCD6r8pbQZ9Q071nlURfwEO9qwO2E7gugkdVaLdnS7dE66t3qXdsS5Bxw7M7EHZ+pYt5G6tCNV3F57X1td+vweh7d+uCtAu6PdZ9jaED1NXb5N6SIXZe/ZDKszTEALWvsAwW5ovf/lg5mj5gq7ezblLMnpdVndrPgm7toXYtiHJnIuJhzNXGXOrNuhnSSM2VW2Ov7swU7WFxmmf/W8+x7ZUQieGxLb3A9XvmzDi0FfV6U6xDoubiL/rc3qCoYp1qOImglAfarR1KX9D6IBNX24mPeeNChfrqrxPUYRkFS/SkH9+bxT4nL2utr0xLNyc0OLf8b3iczYkrX3sILONc9sjy9NSBxChUTCnUaoOZKyvC/dWnVhHj9UwtZ5knhE2S4puFwxUToyNnoWR9aYU0jrrvRuoGk1LzYJ1HdegdWCU6xyHc9H/bMGEug3igrOoIyFZR3xXAMJLGNxtdNKNGmCn7gxSafUODaoxDiXq+wbTK/jd6u1WQn0t3C3QuIyljXNVdsjZzXg0Kkbkkbr9EOdSIxxMeGvjyj2mt/fMde14xXrNs7UH8zmkLWuFf9POxyKUp4EEYml35Qcg/6NhfrsTPsTQX3jupfr39EQvD0D46p7Ou+2pE5tJ1nturLc1Zfnjm51n77dQx2Mm2/veoXok16USLDotEnGhn4JJ7oIaEu9/sN0NCnrw/TuqZvV/w/Vsro0ItKnrfam8oTwNJEAROjVhpWEXqHbzHYcKqaO1k1Odkixeu9a77wZm9Zw/UHf45bRj2rZhAJ/PMA/DePjH3ntVtuS27WvnGCpK5ZxAa0fc4Rs/9/guduBHdUQbs/ifh3rstw3cbn01OD86TRiAYn6MCUebNvr3JM5dJQPUEOoYNuSaCzMNLW532Pr9tjtsb0fe8CqmDrTIL4yNoxqfb0F90+dF9GZt9ec1jlue22CoCfH3KwyehhBgVAu/6tQV4sCh0LgnUWOetepzhHfzUiB+WBDSaU3ykVb/tXZ14v45c5Xvza4ti1e/8yjCywPVphTz3W0swtF11iA4vf0XJkCEmd52+21eAjWjdaVtshRLk86aJr3tvzqJx34wjj/XyblO0NJOdKa1wwKyXJUya0Jor5vrqPbJ34P6XldTNvr9zntuPCxNjY3CR1WAIvhRbyCJ6szNJgzC95vUeb1+MvPqA0LhQVHdO5A0/PbPEBET0af1byKi/70eSPqniejXPlR/aPkWWmnhzA+mveq96B6pEv+udfIGtjX/WpjpEy0OWIBrJr0r/W3n3Xvwc0Ou7Uzc6u9LcLRpQ4/A23te9Syu+73XzgfhdITuDVHm73NJBdib3Htwu2nXBgl01Ind/t7pR1+o8brHWAtada59r4aD+aCwf688Bq/9O9geSAoi+k4Afw+Anw9f/32Qswa+F8APAfi3HtUKCgPUCoJgMbCyp4NWTDmwvb7R27udGRfxjvtmj6yxejeTXhn0DaTeK8GkVU2itm+a9u69j38XFtrmOdhfIOUxNSHYg+u+K4d6e4KdW6GZqLY2NM/tIohmV9+7bsPRtMRcaGOvb1rkuTvGsW6rU8nkiOB6mxHHxd0i3UYo7gmjCj2Gurtzpikf6EBSLf865ACS/zh894MA/oCeSPQniOhtIvocM3/x8lO2O3alz3d0tPJ/yS8XmV6/viVe0OyaqveW72MgyNBduJWlIE7aVvJzrvTTnpfYZnCtzcGsBeIto+56eK6tCEHX3EMLbQ6FynIQ742TKLDpm/TpDau+8eazZ8Txi1xF28/UT022QWvN5/adzSwbg4uiWlMsAn3y02C/vHcjlIeSirzkK9jq+D3BRyr05P5a7eN58VwHaARdD636/LGpMgxVO6t5urMHfSDrABH9IIAvMPOfan76dgC/EP5+/IGkoXQZ52aSVNc0aCCiit13cAhYM8c9obN7/0O/UZOd50KpJPkjEYM/Zyen4K7dmLc2b+vjysQZdcpGIGz8EDrv0j6zKq3344XyEGJrd8T4v9XfM93uNL5S/+y7DZILfbThs5o2WnuqfmnUkaq9sW3NfKhUl3aXp9R9xw+NBNpCRDcA/gWIKvCBCxH9EERlwBVuADT6ZWMq2eievpPUHe2TItFuBF+9s+071HRJRto+r5XMu9AMFya+19tXQS6Vqm0BVYg/QNAn9yvwevzwiqpN+owB3m9E5AlBNjtzfJcdgUpEYGP5N8RWzag/qoTndOdQBxW26s8lVWtT2rr1+s24X7rfntFaITqWld78K21s+pmb8XigfBDrwN8A4LsB/Ck9puo7APwkEX0/PuiBpPRJ3nRcA6NaAdBLJe0k37qCWE8A0sMvLFbfr+0FvTRwLh42IWrDuBm8ljy6SIRFSB3aa8+uBtpUkibQqIWzPhlNt/ZAGGxCUvfSkFXoKY0VGomejhvIGz+nwlz31IH2fc260C6geF0V1ttsCo+Z3FVb4sJvF2+E7Q2qqsY0pCqr1JswF3s6uX9vVo3eQu4R1w0S87MzW12firMSN7EplwLorLxvIcDMfwbAt3n7iX4WwPcx81eI6McA/E4i+lEAvw7Auw/zAYAl7djsFkGq9nbl7kTQ3aN1Bd2+SIcv8EEPC8tNTPHWJnVYEAhRaG1IqagTB909vq+3bYVD5noSoIZ9cULb8WKtHhrrDoLIOJXepOntoPJ+W2Jt7+yDR6lM7YGeTT9UPEZYaF3hHd65LPYQvdgjBb1PavTQ8hyUqHskWLm/d17BdrFXc6cRPO07xEQ6bT3VdRwCnTrXPiQwH+QEdg4k3St/BMDPAPhpAP9HAP+zh+qvH7Zlqt938cE0wqTOeAs0knKHbe0JpFg2un6rD7b1XSjtgvZ69ybqXrt6unXnnR/TxsctYPiCagXO+4HWD421E2kN6nq/ZdNXzUKufmu+a8d77/l77/LY+bw7xh1+oSotamnru8C7fNADSePv3xU+M4Df8VCdnVrKR4s/j6wyIHpOiE3f6pEqfS1Trjq6RHaUA1SunC0CBKxjBDqnAHHt9KGNLpdYqiygWYA1hNzmfdtCdQ9g6pFsDcx0hx1L4wVsFqYLSHOqCRaIrWqSSnttd1SUIPxAYM0bfqatO6KeqO5g3Y5jWx4ieKXrhuBEI/1mTmZez4WArM3CaYOruLPLr/Fw0lrF2wgb2yBYg+JC/7fPr+7v8Bxu0Rg8QKTfP3vfd8qT8Rj0iTTvQ72Y166NvCov3MlxB4iOzWpSjNdvoGRz/y5nEJKNxNx7el2rFrSMe93mnRJgv7PearrcmPis3t5x4kB9UlJQgzZx+ksxmbaQf5PPoGlrbKdM/DpZSeVeHQnd1mTZcANR1anq73AfPWtBzzpTjasJEUqV4NiUIGxjQFurOsQxqxLRROtKRcbukOCPtBJ5HzWWiR5Z3CsfyET4UZQI2aNUfQiCtXBnd9Huwf5e6UD7LdPfPPsCFO8/Yp/BfVAd6vENvc+xtOrFDuTdFSKdshXAqCfl+ygu5C5xONjOgcfA8l7d3ftim21MO+9Ro8D9tnbbZCjoQtsf+q3bV5Vg35rIL82np4EEqHRQDOutd+lgfuEmuSLPiId7tt6FFRHDmicgxvcjDNDegkYYGA5nIUZ/fmyhdYTE5rvvzysP93qrvxtiyN9FScN4JHok0DaSvyGOCppCQTMdpOKwtfm+V3rCt1qIQ00AbrLz2vWNOtgiIRk/Bk2H6vnt+7dtjmSs99mq70+86bOeYK5UH7s3vI8/M6hqG1STSM2roe7W0tUjreMzOn28KzQeIYyfhhDQ0kb0VRFdWihlMIZqUlTQrAeT9bo905XfR1xOm80hp2FbHHon0EEnrevbUZ1o2h4OUZVqSpKTXba7CUpy4Zgv+SOUun2C5VUOGdnAzuyLA4Bnu43CpQdNexyCPLdjP9+5z9Svqo/2+B6D6lybDGOadgB68GewWsRn8bxZXPHdeuTvph2o1dAuOg3BbBviOTM4oe7zYMWoLDCUqnd7DLnYM3E+dO+TUQceVTps7vtlae3ei3DwktqxV1q4He7bZWgfsN/6jvCA+mLP6MJd1w37bP0G2nbUpk1f7VhBHurzh6B++6wPxKh3GPJL92wW8yVrySPh9e41F+bXpdKbq49RFx5bngYS8P7vESkPmLjkR/+4gdmJPGd9JJWqNFBmOQCqA0aNAbd7qrbYhMh8mdQKKsbGO1GhYQV9OaOyNoRdpcDZZqIGdnyjCtizLiQZrRde/yScS+pARUApomp/o8GyOneSmOadxRVVvTYXQqc90Qrj1wdCrqAVW9BrGRdTmQ5DndAz6vGxX6I6pu3Z8FPjCIT8DC3xHBq+/fsR6ldLPu9dV/qmX56GEKCwAJqXtwnT7nIb77+eSSefC9MNVBO1qi/o6hWcSlRNCA8oCSfmVNDUeY2+2tFCfp4ZHO61GHl7x3h/60xVJVVpUlz7c4LHodfde89qLBqYymUS15eF/ltLhmVHN6w5HtXLMp/3HGKw+32rWuxOZH3PnrCq06qNnveBxiAY4nyb67yUolJVLy7Xx/4Lm0rdL3XfPUhM2vdhLtFEpc0NP9PlIz5AeRrqwPtDR1VnRpj2EOPeCpdLZiPOvD0JxtSI9r5NIotOm/dgfxy4lgwLkW4bYdIh/zbP7lkDYh3oTMYd099FJNYw017HpQzLqHewPZWmvaZtZ3ec9sqeX4KpXBv+KezsHauE/9sLc+89p617cwn1n9shCjf1cu7O+0tIAXgqQgAoKADY1YPbhdsVABVvQJvr2h12lxvgxhOuMR9VIZoXyMeertnCOCcIW8FUqRI76abDe2zu5+zRft336Py9qaPRhTfvha2+7O1rCcJ2R4zj3I55ZPjDv95u2hPom2IIzebZAxtGO2d8XCP6C//8nj11DKhNg9GU12lrfO5DnEDVn8G5zN/hAV6JYpaYj6sQ0S8BeAXgKx93W0L5NF6356Hy1Nr0uj2Xy69g5s+0Xz4JIQAARPQTzPx9H3c7rLxuz8PlqbXpdXs+WHk66sDr8rq8Lh9LeS0EXpfX5Vu8PCUh8Hs/7gY05XV7Hi5PrU2v2/MBypPhBF6X1+V1+XjKU0ICr8vr8rp8DOVjFwJE9BuI6C/qgSU//DG14TuJ6D8joj9PRH+OiP5J/f5fJqIvENFP6b/f+E1s088S0Z/R5/6EfvdJIvpjRPSX9f9PfJPa8qtCH/wUEb1HRP/UN7t/egfh7PXJhzoI58O1518lov9an/kfEdHb+v13EdFd6Kt/+xvdng9cmPlj+wdxkv8rAL4HwAHAnwLwqz+GdnwOwK/Vz28A+EsAfjWAfxnA//Jj6pufBfDp5rv/NYAf1s8/DOB3f0xj9osAfsU3u38A/J0Afi2AP/tQnwD4jQD+HwAIwK8H8OPfpPb8PQBG/fy7Q3u+K173lP593Ejg+wH8NDP/DDOfAfwo5ACTb2ph5i8y80/q5xcA/gI+wHkJ34TygwB+RD//CIB/4GNoww8A+CvM/HPf7Acz838B4GvN13t98oPQg3CY+U8AeJuIPvdRt4eZ/ygzWwDCn4Bk3H7S5eMWAt+Qw0q+kUVPW/rbAPy4fvU7Fdr9/m8W/NbCAP4oEf1JPaMBAD7LJXvzLwL47DexPVZ+C4B/L/z9cfWPlb0+eQpz6x+HoBEr301E/xUR/b+J6L/zTW7Lbvm4hcCTKkT0HMB/AOCfYub3IGcp/g0A/tsAvgjgf/tNbM7fwcy/FnK+4+8gor8z/siCMb+pph0iOgD4+wH8X/Wrj7N/NuXj6JO9QkT/IoAFwB/Ur74I4L/BzH8bgH8awL9LRG9+XO2L5eMWAo8+rOSjLkQ0QQTAH2Tm/xAAmPlLzLwyc4akUP/+b1Z7mPkL+v+XAfxH+uwvGaTV/7/8zWqPlr8PwE8y85e0bR9b/4Sy1ycf29wiot8G4DcB+EdUMIGZT8z8Vf38JyFc2N/4zWjPQ+XjFgL/JYDvJaLv1l3mtwD4sW92I0iOUvp9AP4CM/9r4fuoQ/6PAGyOZ/+I2vOMiN6wzxCy6c9C+ua36mW/FfVhsN+M8g8hqAIfV/80Za9PfgzA/0StBL8ejz0I50MWIvoNkIN6/35mvg3ff4ZIMhMQ0fdATu7+mY+6PY8qHzczCWFx/xJEMv6LH1Mb/g4IjPzTAH5K//1GAP8XAH9Gv/8xAJ/7JrXneyCWkj8F4M9ZvwD4FIA/DuAvA/h/AvjkN7GPngH4KoC3wnff1P6BCKAvApghOv5v3+sTiFXg/6Dz6s9ATsn6ZrTnpyFchM2jf1uv/R/rWP4UgJ8E8D/8Zs/zvX+vPQZfl9flW7x83OrA6/K6vC4fc3ktBF6X1+VbvLwWAq/L6/ItXl4LgdfldfkWL6+FwOvyunyLl9dC4HV5Xb7Fy2sh8Lq8Lt/i5bUQeF1el2/x8v8HqKyz2cFAkd8AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Image Normalization"
      ],
      "metadata": {
        "id": "1rsxd6Wbo4Zq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the pixel distribution is heavily skewed, this might affect the gradients during backpropagation. The neuron will become saturated if you multiply your weights by large pixel values. But eventually, in order to offset the impact of the large input, your weights will be updated to really small values. Sadly, this will not occur because the gradients for saturated activations will be too small to allow for any appreciable update. Your weights will eventually become stuck. "
      ],
      "metadata": {
        "id": "wn6rAzj0YDtM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Standardization"
      ],
      "metadata": {
        "id": "W3h6omvpo7hN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "These initial inputs will be multiplied (weighted) and added to (biassed) in order to produce activations, which we will then backpropogate with the gradients to train the model. We want each feature in this process to have a comparable range so that our gradients don't get out of hand (and that we only need one global learning rate multiplier). Another way to think about it is that deep learning networks typically share a lot of parameters. If your inputs weren't scaled to produce similarly-ranged feature values (i.e., across the entire dataset by removing the mean), sharing wouldn't be very straightforward because w is too big for some regions of the image and too small for others.\n",
        "\n",
        "             \n",
        ">>> $ Z = \\Large\\frac{x - μ}{σ}$, where $μ$ is the mean and $σ$ is the standard deviation."
      ],
      "metadata": {
        "id": "ZhdoSiD-dfdV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Constructing entire dataset\n",
        "total_df = pd.concat([substrc_data.train_df, substrc_data.val_df], axis = 0)"
      ],
      "metadata": {
        "id": "0umSVLPB-CQp"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating mean/std for the entire dataset\n",
        "def calc_mean_std(dataset):\n",
        "  psum = 0\n",
        "  pstd = 0\n",
        "  for i in range(len(dataset)):\n",
        "    image = dataset.iloc[i, 0]\n",
        "    psum += image.sum() \n",
        "    pstd += (image**2).sum()\n",
        "  \n",
        "  image = dataset.sample(n=1)[\"Images\"].values[0]\n",
        "  height = image.shape[1]\n",
        "  width = image.shape[2]\n",
        "  total_pixels = (len(dataset)*height*width)\n",
        "  mean = psum/total_pixels\n",
        "  std = np.sqrt((pstd/total_pixels) - (mean**2))\n",
        "\n",
        "  print(\"Mean: {}, Std: {}\".format(mean, std))\n",
        "  return mean, std"
      ],
      "metadata": {
        "id": "Yf05tM9N-CMc"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mean/Std\n",
        "mean, std = calc_mean_std(total_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ef03twez-CJo",
        "outputId": "cb7b8052-08f0-46b5-a02d-34461d46e6db"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: 0.061701331169849535, Std: 0.11730575144553053\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardized Image\n",
        "image = substrc_data.train_df.sample(n=1)[\"Images\"].values[0]\n",
        "standardized_image = (image - mean)/std"
      ],
      "metadata": {
        "id": "S7ggjoR_y_GR"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pixel Distribution\n",
        "sns.histplot(standardized_image.ravel()).set(title=\"Standardized Pixel Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "k9gtoW4azHTR",
        "outputId": "ff8c4add-f521-4744-f604-f32c0cc099f9"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Standardized Pixel Distribution')]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "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": [
        "## Label Analysis"
      ],
      "metadata": {
        "id": "C4nY5O2xzgXW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Label Properties\n",
        "label = substrc_data.train_df.sample(n=1)[\"Labels\"].values[0]\n",
        "print(\"Image Type: {}\".format(type(label)))\n",
        "print(\"Label Shape: {}\".format(label.shape))\n",
        "print(\"Label Data Type: {}\".format(label.dtype))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9C6qelK0DFW",
        "outputId": "5f0fa9bf-d36a-4995-92e0-c1be959aa28c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image Type: <class 'numpy.int64'>\n",
            "Label Shape: ()\n",
            "Label Data Type: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Label Distribution\n",
        "sns.histplot(substrc_data.train_df[\"Labels\"].tolist()).set(title=\"Label Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "-VtM_bdDzpWE",
        "outputId": "2209fa21-4597-4022-bef3-c9c258ef304c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Label Distribution')]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "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": [
        "# Data Pipeline\n",
        "- Train/Test/Val Split\n",
        "- Data Transformations/Augmentations\n",
        "- Custom Pytorch Dataset\n",
        "- Pytorch DataLoader"
      ],
      "metadata": {
        "id": "CmJ0FTXF0WV3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train/Test/Val Split\n"
      ],
      "metadata": {
        "id": "U6a5ORLH0qMu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train - Test Split -> 90:10\n",
        "substrc_data.train_test_val_split()"
      ],
      "metadata": {
        "id": "Wb9yJnlz7qq7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40e1f76c-f60a-4089-8c6b-78263d688ebe"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of training data samples: 27000\n",
            "No. of testing data samples: 3000\n",
            "No. of validation data samples: 7500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(substrc_data.train_df[\"Labels\"].tolist()).set(title=\"Train Label Distribution After Split\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "mN6fek1e8Z4U",
        "outputId": "4a67708d-344c-41a5-ad04-80268701d04a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Train Label Distribution After Split')]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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VK0dQjyRpFhqqG6rdUPBHdHee3Rr47SiLkiTNLsP+p7wXAF8Hng+8ALg4ibcol6Q5YthuqLcAT66qGwCSLAC+BJw5qsIkSbPHsFdD/d5YUDQ3TWFeSdJGbtgji88n+QJwant+CHDuaEqSJM02ff+D+1HAdlX1xiT/A9i7jfoacMqoi5MkzQ59RxbvB44CqKqzgLMAkjyujTtohLVJkmaJvvMO21XV98Y3trbFI6lIkjTr9IXF/HWMe8A01iFJmsX6wmJ5kv85vjHJy4FLhllBkk2SfCvJZ9rznZJcnGRFktOTbNbaN2/PV7TxiweWcVRrvyzJfkO/OknStOg7Z/E64FNJDuPucFgKbAY8d8h1vBb4IfDg9vzdwPuq6rQkHwYOB45tP2+uqkclObRNd0iSJcChwG7AI4AvJXl0Vd055PolSffROo8squr6qtoTeBtwZXu8rar2qKrr+haeZAfgWcBH2vMAT+fuP+Y7EXhOGz64PaeN37dNfzBwWlXdXlU/BVYATxny9UmSpsGw/8/iAuCC9Vj++4G/orunFMBDgVuqam17vhJY2IYXAle39a1NcmubfiFw0cAyB+e5S5IjgCMAFi1atB6lSpImM7K/wk7ybOCGqhrq3MZ9VVXHVdXSqlq6YMGCmVilJM0Zw/4F9/rYC/iTJAfS/SvWBwP/BMxPMq8dXewArGrTrwJ2BFYmmQc8hO62ImPtYwbnkSTNgJEdWVTVUVW1Q1UtpjtBfX5VHUbXnTV2x9plwNlt+Jz2nDb+/Kqq1n5ou1pqJ2AXujvgSpJmyCiPLCbzJuC0JO8EvgUc39qPB05OsgJYQxcwVNWlSc4AfkD3j5eO9EooSZpZMxIWVXUhcGEbvoIJrmaqqt/Q/b+MieY/BjhmdBVKktbF24xLknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknqNLCyS7JjkgiQ/SHJpkte29m2SnJfk8vZz69aeJB9IsiLJd5M8aWBZy9r0lydZNqqaJUkTG+WRxVrgDVW1BNgdODLJEuDNwJerahfgy+05wAHALu1xBHAsdOECHA08FXgKcPRYwEiSZsbIwqKqrq2qb7bhXwA/BBYCBwMntslOBJ7Thg8GTqrORcD8JNsD+wHnVdWaqroZOA/Yf1R1S5LubUbOWSRZDDwRuBjYrqqubaOuA7ZrwwuBqwdmW9naJmsfv44jkixPsnz16tXT+wIkaY4beVgkeRDwSeB1VfXzwXFVVUBNx3qq6riqWlpVSxcsWDAdi5QkNSMNiySb0gXFKVV1Vmu+vnUv0X7e0NpXATsOzL5Da5usXZI0Q0Z5NVSA44EfVtV7B0adA4xd0bQMOHug/SXtqqjdgVtbd9UXgGcm2bqd2H5ma5MkzZB5I1z2XsCLge8l+XZr+2vgXcAZSQ4HrgJe0MadCxwIrABuA14GUFVrkrwD+Eab7u1VtWaEdUuSxhlZWFTVfwGZZPS+E0xfwJGTLOsE4ITpq06SNBX+BbckqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqddGExZJ9k9yWZIVSd68oeuRpLlkowiLJJsAHwIOAJYAL0yyZMNWJUlzx0YRFsBTgBVVdUVV/RY4DTh4A9ckSXNGqmpD19AryfOA/avq5e35i4GnVtWrBqY5AjiiPd0VuOw+rHJb4Mb7MP+oWNfUWNfUWNfU3B/remRVLZhoxLz1r2d2qarjgOOmY1lJllfV0ulY1nSyrqmxrqmxrqmZa3VtLN1Qq4AdB57v0NokSTNgYwmLbwC7JNkpyWbAocA5G7gmSZozNopuqKpam+RVwBeATYATqurSEa5yWrqzRsC6psa6psa6pmZO1bVRnOCWJG1YG0s3lCRpAzIsJEm95lRY9N0yJMnmSU5v4y9Osnhg3FGt/bIk+81wXa9P8oMk303y5SSPHBh3Z5Jvt8e0nvQfoq6XJlk9sP6XD4xbluTy9lg2w3W9b6CmHye5ZWDcKLfXCUluSPL9ScYnyQda3d9N8qSBcaPcXn11Hdbq+V6SryZ5/MC4K1v7t5Msn+G69kly68D79XcD40Z2+58h6nrjQE3fb/vUNm3cKLfXjkkuaJ8FlyZ57QTTjG4fq6o58aA7Mf4TYGdgM+A7wJJx07wS+HAbPhQ4vQ0vadNvDuzUlrPJDNb1x8CWbfh/jdXVnv9yA26vlwIfnGDebYAr2s+t2/DWM1XXuOlfTXdBxEi3V1v204AnAd+fZPyBwOeAALsDF496ew1Z155j66O7pc7FA+OuBLbdQNtrH+Az93UfmO66xk17EHD+DG2v7YEnteGtgB9P8Ds5sn1sLh1ZDHPLkIOBE9vwmcC+SdLaT6uq26vqp8CKtrwZqauqLqiq29rTi+j+zmTU7sstVvYDzquqNVV1M3AesP8GquuFwKnTtO51qqr/ANasY5KDgZOqcxEwP8n2jHZ79dZVVV9t64WZ27+G2V6TGentf6ZY10zuX9dW1Tfb8C+AHwILx002sn1sLoXFQuDqgecrufeGvmuaqloL3Ao8dMh5R1nXoMPpvjmM2SLJ8iQXJXnONNU0lbr+tB3unplk7A8nZ8X2at11OwHnDzSPansNY7LaR7m9pmr8/lXAF5Ncku6WOjNtjyTfSfK5JLu1tlmxvZJsSfeB+8mB5hnZXum6yJ8IXDxu1Mj2sY3i7yzUSfIiYCnwRwPNj6yqVUl2Bs5P8r2q+skMlfRp4NSquj3JX9AdlT19htY9jEOBM6vqzoG2Dbm9ZrUkf0wXFnsPNO/dttfDgPOS/Kh9854J36R7v36Z5EDg/wG7zNC6h3EQ8JWqGjwKGfn2SvIguoB6XVX9fDqXvS5z6chimFuG3DVNknnAQ4Cbhpx3lHWR5BnAW4A/qarbx9qralX7eQVwId23jRmpq6puGqjlI8AfDDvvKOsacCjjughGuL2GMVntG/x2Nkn+O917eHBV3TTWPrC9bgA+xfR1v/aqqp9X1S/b8LnApkm2ZRZsr2Zd+9dItleSTemC4pSqOmuCSUa3j43iRMxsfNAdRV1B1y0xdlJst3HTHMk9T3Cf0YZ3454nuK9g+k5wD1PXE+lO6O0yrn1rYPM2vC1wOdN0om/IurYfGH4ucFHdfTLtp62+rdvwNjNVV5vuMXQnGzMT22tgHYuZ/ITts7jnycevj3p7DVnXIrrzcHuOa38gsNXA8Ffp7v48U3U9fOz9o/vQ/VnbdkPtA6Oqq41/CN15jQfO1PZqr/0k4P3rmGZk+9i0bdyN4UF3pcCP6T5439La3k73bR1gC+AT7Rfn68DOA/O+pc13GXDADNf1JeB64NvtcU5r3xP4Xvtl+R5w+AzX9Q/ApW39FwCPGZj3z9t2XAG8bCbras/fCrxr3Hyj3l6nAtcCd9D1CR8OvAJ4RRsfun/i9ZO2/qUztL366voIcPPA/rW8te/cttV32vv8lhmu61UD+9dFDITZRPvATNXVpnkp3UUvg/ONenvtTXdO5LsD79WBM7WPebsPSVKvuXTOQpK0ngwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTr/wOKejyD7PASsQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(substrc_data.test_df[\"Labels\"].tolist()).set(title=\"Test Label Distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "tCaPnXy58fel",
        "outputId": "b3fcecb9-b10d-471a-e052-bd62fde59434"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Test Label Distribution')]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "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": [
        "## Data Transformations"
      ],
      "metadata": {
        "id": "MqIHdZw_8YUP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Performing Data Augmentations as per [[2]](https://arxiv.org/pdf/1909.07346.pdf)"
      ],
      "metadata": {
        "id": "NMbYgpExzRYa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Transformations\n",
        "data_transformations = {\n",
        "  \"train\" : transforms.Compose([\n",
        "      transforms.ToTensor(),\n",
        "      # Data Standardization\n",
        "      transforms.Normalize([0.06170],[0.11730]),\n",
        "      # Data Augmentation\n",
        "      transforms.RandomRotation(degrees=(0,90))\n",
        "  ]),\n",
        "\n",
        "  \"test\": transforms.Compose([\n",
        "      transforms.ToTensor(),\n",
        "      transforms.Normalize([0.06170],[0.11730])\n",
        "  ]),\n",
        "\n",
        "  \"val\": transforms.Compose([\n",
        "      transforms.ToTensor(),\n",
        "      transforms.Normalize([0.06170],[0.11730])\n",
        "  ])\n",
        "}"
      ],
      "metadata": {
        "id": "aoXSetpB8s0e"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Custom Dataset"
      ],
      "metadata": {
        "id": "-SE915mQ887-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class SubstructureDataset(Dataset):\n",
        "  \"\"\"\n",
        "    Custom Pytorch Dataset \n",
        "  \"\"\"\n",
        "  def __init__(self, dataframe, transforms):\n",
        "    self.dataframe = dataframe\n",
        "    self.transforms = transforms\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.dataframe)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    image, label = self.dataframe.iloc[idx]\n",
        "\n",
        "    if self.transforms is not None:\n",
        "      image = self.transforms(image[0])\n",
        "    else:\n",
        "      image = torch.from_numpy(image[0])\n",
        "\n",
        "    return image, torch.tensor(label)\n",
        "    \n"
      ],
      "metadata": {
        "id": "PQlVpytG7WHS"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Dict\n",
        "dataset_dict = {\n",
        "    \"train\": SubstructureDataset(substrc_data.train_df, data_transformations[\"train\"]),\n",
        "    \"val\": SubstructureDataset(substrc_data.val_df, data_transformations[\"val\"]),\n",
        "    \"test\": SubstructureDataset(substrc_data.test_df, data_transformations[\"test\"])\n",
        "}"
      ],
      "metadata": {
        "id": "C4yCL0AW82hs"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DataLoaders"
      ],
      "metadata": {
        "id": "AR2NDCF09tLo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Upon iterating over several batch sizes, the batch size of 64 seems to be giving an optimal trade off between training time and classification metrics."
      ],
      "metadata": {
        "id": "AwBA-0LxBf7m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize dataloaders\n",
        "train_dataloader = DataLoader(dataset_dict[\"train\"], batch_size=64, num_workers=2, shuffle=True, pin_memory=True)\n",
        "val_dataloader = DataLoader(dataset_dict[\"val\"], batch_size=64, num_workers=2, shuffle=False, pin_memory=True)\n",
        "test_dataloader = DataLoader(dataset_dict[\"test\"], batch_size=64, num_workers=2, shuffle=False)"
      ],
      "metadata": {
        "id": "TEgo_vKN9soS"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model\n",
        "- Model Definition\n",
        "- Loss Function\n",
        "- Optimizer\n",
        "- Training"
      ],
      "metadata": {
        "id": "xsIUzg6t945g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Definition"
      ],
      "metadata": {
        "id": "Hai3m-WF-Lnt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Resnet18](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html) is used as the backbone model. Resnet and resnet like architectures have shown significant performance when it comes to image classification, and hence I am using this as a feature extractor into the dense layer. I chose resnet18 over other deeper networks because of lesser training time and limited GPU resource. The fully connected layer of resnet is modified by adding a dense layer for classification with 3 neuron output, one for each class. The input layer of the resnet is modified to take in single channel images.\n",
        "\n",
        ">**NOTE**: Softmax layer is not added in the definition of the model since we are using [nn.CrossEntropyLoss](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html). According to the pytorch docs, this loss function applies the log softmax layer intrinsicly and computes the NLL loss.\n",
        "\n",
        "One clever optimization of the model would be to freeze the initial base layers upto a depth of 5 or less, and then fine tune the model to this task. This way we can reduce the training time and also leverage the resnets early layers that capture basic information like curves/edges in an image."
      ],
      "metadata": {
        "id": "nWJdV7scSaye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "\n",
        "# PyTorch models inherit from torch.nn.Module\n",
        "class SubstructureClassifier(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(SubstructureClassifier, self).__init__()\n",
        "        self.resnet = torchvision.models.resnet18(weights=None)\n",
        "        # Modifying first layer to accpet single channel input\n",
        "        self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
        "        layers = []\n",
        "        layers += [nn.Linear(512, 256, bias=True), nn.ReLU(inplace=True)]\n",
        "        layers += [nn.Linear(256, 16, bias=True), nn.ReLU(inplace=True)]\n",
        "        layers += [nn.Linear(16,3)]\n",
        "        self.resnet.fc = nn.Sequential(*layers)\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.resnet(x)\n",
        "\n",
        "model = SubstructureClassifier()\n",
        "model.to(device=device)"
      ],
      "metadata": {
        "id": "rGrVj9FT-CoC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be842c77-4be6-4246-f345-f0898833bd93"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SubstructureClassifier(\n",
              "  (resnet): ResNet(\n",
              "    (conv1): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (relu): ReLU(inplace=True)\n",
              "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "    (layer1): Sequential(\n",
              "      (0): BasicBlock(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicBlock(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (layer2): Sequential(\n",
              "      (0): BasicBlock(\n",
              "        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "      )\n",
              "      (1): BasicBlock(\n",
              "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (layer3): Sequential(\n",
              "      (0): BasicBlock(\n",
              "        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "      )\n",
              "      (1): BasicBlock(\n",
              "        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (layer4): Sequential(\n",
              "      (0): BasicBlock(\n",
              "        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "      )\n",
              "      (1): BasicBlock(\n",
              "        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "    (fc): Sequential(\n",
              "      (0): Linear(in_features=512, out_features=256, bias=True)\n",
              "      (1): ReLU(inplace=True)\n",
              "      (2): Linear(in_features=256, out_features=16, bias=True)\n",
              "      (3): ReLU(inplace=True)\n",
              "      (4): Linear(in_features=16, out_features=3, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loss Function"
      ],
      "metadata": {
        "id": "O4a_fWka-zpL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "1geM6hPW-waG"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Optimizer"
      ],
      "metadata": {
        "id": "Iezo2PSfH-K2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I chose the Adam optimizer according to the papers [[2,3]]() where the resnet acheived a remarkable training accuracy of ~97%. I initialized them according to the values in the paper. I also used a one cycle lr scheduler [[4]](https://arxiv.org/abs/1803.09820v2) in place of a cyclic scheduler as mentioned in the paper [[3]]() as it performed relatively better."
      ],
      "metadata": {
        "id": "2m0VvNTWetLG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
        "scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, \n",
        "                       max_lr = 1e-2,\n",
        "                       steps_per_epoch = len(train_dataloader), \n",
        "                       epochs = 100,\n",
        "                       anneal_strategy = 'cos')"
      ],
      "metadata": {
        "id": "FArJkqFoH7UJ"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "lXDrI6NpISNm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Strategy\n",
        "\n",
        "\n",
        "- Early Stopping: The training loop is set in a way that if the val loss does not improve for the next 15 epochs, then we stop and save the best model with maximum val acc according to the paper [[3]](https://arxiv.org/pdf/2112.12121.pdf).\n",
        "\n",
        "- Model Checkpoints: The model checkpointing starts after 25 epochs where the model is saved if the validation acc increases above the max validation acc.\n"
      ],
      "metadata": {
        "id": "cH7S3GbWB0Ff"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class TrainWrapper:\n",
        "  \"\"\"\n",
        "    Train Wrapper for training\n",
        "  \"\"\"\n",
        "  def __init__(self, epochs):\n",
        "    self.epochs = epochs\n",
        "    self.threshold_epoch = 15\n",
        "    self.initialize_tracking()\n",
        "\n",
        "  \n",
        "  def initialize_tracking(self):\n",
        "    self.accuracy_per_epoch = {}\n",
        "    self.loss_per_epoch = {}\n",
        "\n",
        "    self.accuracy_per_epoch[\"train\"] = []\n",
        "    self.accuracy_per_epoch[\"val\"] = []\n",
        "\n",
        "    self.loss_per_epoch[\"train\"] = []\n",
        "    self.loss_per_epoch[\"val\"] = []\n",
        "\n",
        "\n",
        "  def get_accuracy(self, outputs, labels):\n",
        "\n",
        "    return (outputs.argmax(dim=1) == labels).float().sum().item()\n",
        "    \n",
        "\n",
        "  def run_epoch(self, data_loader, train):\n",
        "    start_time = time.time()\n",
        "\n",
        "    running_loss = 0.\n",
        "    total_samples = 0\n",
        "    correct_pred = 0\n",
        "\n",
        "    if train:\n",
        "      model.train()\n",
        "    else:\n",
        "      model.eval()\n",
        "\n",
        "    self.min_val_loss = float(\"inf\")\n",
        "    self.stop_counter = 0\n",
        "\n",
        "\n",
        "    for i, data in enumerate(data_loader):\n",
        "        inputs, labels = data\n",
        "        inputs, labels = inputs.to(device=device), labels.to(device=device)\n",
        "        outputs = model(inputs)\n",
        "        loss = loss_fn(outputs, labels)\n",
        "\n",
        "        if train:\n",
        "          optimizer.zero_grad()\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "\n",
        "\n",
        "        running_loss += loss.item()\n",
        "        total_samples += labels.size(0)\n",
        "        correct_pred += self.get_accuracy(outputs, labels)\n",
        "\n",
        "    return running_loss/len(data_loader), correct_pred/total_samples\n",
        "\n",
        "  def train(self):\n",
        "\n",
        "    self.max_val_acc = 0\n",
        "    self.stop_counter = 0\n",
        "\n",
        "    for ep in tqdm(range(self.epochs), desc='Training loop'):\n",
        "      start_time = time.time()\n",
        "\n",
        "      # Train one epoch\n",
        "      # Validate one epoch\n",
        "      # LR Scheduler Step\n",
        "      train_loss, train_acc = self.run_epoch(train_dataloader, train=True)\n",
        "      val_loss, val_acc = self.run_epoch(val_dataloader, train=False)\n",
        "      scheduler.step()\n",
        "\n",
        "      self.loss_per_epoch[\"train\"].append(train_loss)\n",
        "      self.loss_per_epoch[\"val\"].append(val_loss)\n",
        "\n",
        "      self.accuracy_per_epoch[\"train\"].append(train_acc)\n",
        "      self.accuracy_per_epoch[\"val\"].append(val_acc)\n",
        "\n",
        "      self.checkpoint(ep+1, val_acc)\n",
        "\n",
        "      print(\"Epoch: {} - Train loss: {}, Val Loss: {}, Train Acc: {}, Val Acc: {}, Time Elapsed: {}\".format(ep+1, train_loss, val_loss, train_acc, val_acc, time.time() - start_time))\n",
        "\n",
        "      if self.stop_early():\n",
        "        print(\"Stopping Training since Val Acc has not improved for {} epochs\".format(self.threshold_epoch))\n",
        "        break\n",
        "\n",
        "  def test(self):\n",
        "    model.eval()\n",
        "    test_loss, test_acc = self.run_epoch(test_dataloader, train=False)\n",
        "    print(\"Test Loss: {}, Test Acc: {}\".format(test_loss, test_acc))\n",
        "\n",
        "  def checkpoint(self, epoch_num, val_acc):\n",
        "    if epoch_num >= 25:\n",
        "      if self.max_val_acc < val_acc:\n",
        "        self.save()\n",
        "        print(\"--- Saving model ---\")\n",
        "        self.max_val_acc = val_acc\n",
        "        self.stop_counter = 0\n",
        "      else:\n",
        "        self.stop_counter += 1\n",
        "\n",
        "\n",
        "  def stop_early(self):\n",
        "    return self.stop_counter == self.threshold_epoch\n",
        "\n",
        "  def get_test_preds_labels(self):\n",
        "\n",
        "    y_true = None\n",
        "    y_test = None\n",
        "    with torch.no_grad():\n",
        "      for i, data in enumerate(test_dataloader):\n",
        "        inputs, labels = data[0], data[1]\n",
        "        inputs = inputs.to(device=device)\n",
        "        logits = model(inputs)\n",
        "        y_prob = F.softmax(logits, dim=1)\n",
        "        y = F.one_hot(labels, num_classes=3)\n",
        "\n",
        "        if y_true is None:\n",
        "          y_true = y\n",
        "        else:\n",
        "          y_true = torch.cat([y_true, y], 0)\n",
        "        \n",
        "        if y_test is None:\n",
        "          y_test = y_prob\n",
        "        else:\n",
        "          y_test = torch.cat([y_test, y_prob], 0)\n",
        "        \n",
        "        del inputs\n",
        "    return y_true.numpy(), y_test.cpu().detach().numpy()\n",
        "  \n",
        "  def save(self):\n",
        "    classification_model_name = \"SubstructureClassification\"\n",
        "    self.model_save_path = F\"/content/drive/MyDrive/2023 ML/Model Weights/{classification_model_name}\"\n",
        "    torch.save(model.state_dict(), self.model_save_path)\n",
        "\n",
        "  def load(self):\n",
        "    classification_model_name = \"SubstructureClassification\"\n",
        "    self.model_save_path = F\"/content/drive/MyDrive/2023 ML/Model Weights/{classification_model_name}\"\n",
        "    if torch.cuda.is_available():\n",
        "      model.load_state_dict(torch.load(self.model_save_path))\n",
        "    else:\n",
        "      model.load_state_dict(torch.load(self.model_save_path, map_location=torch.device('cpu')))\n",
        "\n",
        "  def plot_roc_curves(self):\n",
        "    y_true, y_pred = trainer.get_test_preds_labels()\n",
        "    fig, ax = plt.subplots(figsize=(6, 6))\n",
        "    class_dict = {\n",
        "        0: \"NoSub\",\n",
        "        1: \"Vortex\",\n",
        "        2: \"Sphere\"\n",
        "    }\n",
        "    n_classes = 3\n",
        "\n",
        "    self.plot_macro_roc(y_true, y_pred, n_classes)\n",
        "    colors = cycle([\"aqua\", \"darkorange\", \"cornflowerblue\"])\n",
        "    for class_id, color in zip(range(n_classes), colors):\n",
        "        RocCurveDisplay.from_predictions(\n",
        "            y_true[:, class_id],\n",
        "            y_pred[:, class_id],\n",
        "            name=f\"ROC {class_dict[class_id]}\",\n",
        "            color=color,\n",
        "            ax=ax,\n",
        "        )\n",
        "    \n",
        "    plt.plot([0, 1], [0, 1], \"k--\", label=\"Baseline (AUC = 0.5)\")\n",
        "    plt.axis(\"square\")\n",
        "    plt.xlabel(\"False Positive Rate\")\n",
        "    plt.ylabel(\"True Positive Rate\")\n",
        "    plt.title(\"One-vs-Rest ROC curves\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "  \n",
        "  def plot_macro_roc(self, y_true, y_pred, n_classes):\n",
        "    fpr, tpr, roc_auc = dict(), dict(), dict()\n",
        "    for i in range(n_classes):\n",
        "      fpr[i], tpr[i], _ = roc_curve(y_true[:, i], y_pred[:, i])\n",
        "      roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "    fpr_grid = np.linspace(0.0, 1.0, 1000)\n",
        "\n",
        "    # Interpolate all ROC curves at these points\n",
        "    mean_tpr = np.zeros_like(fpr_grid)\n",
        "\n",
        "    for i in range(n_classes):\n",
        "        mean_tpr += np.interp(fpr_grid, fpr[i], tpr[i])  # linear interpolation\n",
        "\n",
        "    # Average it and compute AUC\n",
        "    mean_tpr /= n_classes\n",
        "\n",
        "    fpr[\"macro\"] = fpr_grid\n",
        "    tpr[\"macro\"] = mean_tpr\n",
        "    roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
        "\n",
        "\n",
        "    plt.plot(\n",
        "      fpr[\"macro\"],\n",
        "      tpr[\"macro\"],\n",
        "      label=f\"Macro-Average ROC (AUC = {roc_auc['macro']:.2f})\",\n",
        "      color=\"navy\",\n",
        "      linestyle=\":\",\n",
        "      linewidth=4,\n",
        "    )\n",
        "\n",
        "  \n",
        "  def classification_report(self):\n",
        "    y_true, y_pred = self.get_test_preds_labels()\n",
        "    y_true = np.argmax(y_true, axis=1)\n",
        "    y_pred = np.argmax(y_pred, axis=1)\n",
        "    print(classification_report(y_true, y_pred))\n",
        "\n",
        "  def plot_confusion_matrix(self):\n",
        "    plt.figure(figsize=(10,6))\n",
        "    y_true, y_pred = self.get_test_preds_labels()\n",
        "    y_true = np.argmax(y_true, axis=1)\n",
        "    y_pred = np.argmax(y_pred, axis=1)\n",
        "    fx=sns.heatmap(confusion_matrix(y_true, y_pred), annot=True, fmt=\".2f\",cmap=\"GnBu\")\n",
        "    fx.set_title('Confusion Matrix \\n');\n",
        "    fx.set_xlabel('\\n Predicted Values\\n')\n",
        "    fx.set_ylabel('Actual Values\\n');\n",
        "    fx.xaxis.set_ticklabels(['NoSub','Vortex','Sphere'])\n",
        "    fx.yaxis.set_ticklabels(['NoSub','Vortex','Sphere'])\n",
        "    plt.show()\n",
        "\n",
        "  def plot_metrics(self):\n",
        "    imagefig, axes = plt.subplots(1,2, figsize=(30,10))\n",
        "    axes[0].set_title(\"Loss\")\n",
        "    axes[0].plot(np.arange(len(self.loss_per_epoch[\"train\"])+1)[1:], self.loss_per_epoch[\"train\"], label=\"train\")\n",
        "    axes[0].plot(np.arange(len(self.loss_per_epoch[\"val\"])+1)[1:], self.loss_per_epoch[\"val\"], label=\"val\")\n",
        "    axes[0].legend()\n",
        "\n",
        "    axes[1].set_title(\"Accuracy\")\n",
        "    axes[1].plot(np.arange(len(self.accuracy_per_epoch[\"train\"])+1)[1:], self.accuracy_per_epoch[\"train\"], label=\"train\")\n",
        "    axes[1].plot(np.arange(len(self.accuracy_per_epoch[\"train\"])+1)[1:], self.accuracy_per_epoch[\"val\"], label=\"val\")\n",
        "    axes[1].legend()"
      ],
      "metadata": {
        "id": "viQaxirhIUSQ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Train Wrapper\n",
        "trainer = TrainWrapper(100)"
      ],
      "metadata": {
        "id": "U2HfKgIZbxt9"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Start Training\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "outputId": "825e4c3b-f99f-4b83-fc0e-8723b53001ea"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training loop:   0%|          | 0/100 [00:00<?, ?it/s]"
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          "text": [
            "Epoch: 1 - Train loss: 1.1002105256957466, Val Loss: 1.0985089055562423, Train Acc: 0.3380740740740741, Val Acc: 0.3348, Time Elapsed: 53.40833020210266\n",
            "Epoch: 2 - Train loss: 1.09909858438076, Val Loss: 1.098329708737842, Train Acc: 0.34185185185185185, Val Acc: 0.34826666666666667, Time Elapsed: 53.804832220077515\n",
            "Epoch: 3 - Train loss: 1.098930211711269, Val Loss: 1.0982883774628074, Train Acc: 0.3385185185185185, Val Acc: 0.334, Time Elapsed: 52.63376569747925\n",
            "Epoch: 4 - Train loss: 1.0983028423164694, Val Loss: 1.1221040239778615, Train Acc: 0.34285185185185185, Val Acc: 0.3337333333333333, Time Elapsed: 54.00041747093201\n",
            "Epoch: 5 - Train loss: 1.096642150133142, Val Loss: 1.0965702250852423, Train Acc: 0.3546666666666667, Val Acc: 0.3505333333333333, Time Elapsed: 53.726314544677734\n",
            "Epoch: 6 - Train loss: 1.0964461759933364, Val Loss: 1.1054431710202814, Train Acc: 0.350962962962963, Val Acc: 0.3333333333333333, Time Elapsed: 54.237448930740356\n",
            "Epoch: 7 - Train loss: 1.092013987602216, Val Loss: 1.150310362294569, Train Acc: 0.3685185185185185, Val Acc: 0.3336, Time Elapsed: 53.834526777267456\n",
            "Epoch: 8 - Train loss: 1.0826665465300682, Val Loss: 1.100396532628496, Train Acc: 0.385037037037037, Val Acc: 0.3684, Time Elapsed: 54.33319401741028\n",
            "Epoch: 9 - Train loss: 1.062925465864028, Val Loss: 1.0821943616462966, Train Acc: 0.4116296296296296, Val Acc: 0.38106666666666666, Time Elapsed: 54.67531728744507\n",
            "Epoch: 10 - Train loss: 1.039188284444583, Val Loss: 1.0638324188984047, Train Acc: 0.4291111111111111, Val Acc: 0.4004, Time Elapsed: 54.409820556640625\n",
            "Epoch: 11 - Train loss: 1.0064872845089265, Val Loss: 1.0895266936997237, Train Acc: 0.457, Val Acc: 0.4209333333333333, Time Elapsed: 54.53448009490967\n",
            "Epoch: 12 - Train loss: 0.9855040645147387, Val Loss: 1.2847935047190069, Train Acc: 0.46725925925925926, Val Acc: 0.3565333333333333, Time Elapsed: 54.98052144050598\n",
            "Epoch: 13 - Train loss: 0.9606588197545418, Val Loss: 1.0377946248499013, Train Acc: 0.4863703703703704, Val Acc: 0.4676, Time Elapsed: 54.546621561050415\n",
            "Epoch: 14 - Train loss: 0.9431359790512736, Val Loss: 1.2042540463350586, Train Acc: 0.5032222222222222, Val Acc: 0.42906666666666665, Time Elapsed: 54.294217586517334\n",
            "Epoch: 15 - Train loss: 0.9272046165443709, Val Loss: 1.0778176976462541, Train Acc: 0.5224814814814814, Val Acc: 0.438, Time Elapsed: 54.60178995132446\n",
            "Epoch: 16 - Train loss: 0.8944349353912318, Val Loss: 0.8996776286828316, Train Acc: 0.5531481481481482, Val Acc: 0.5449333333333334, Time Elapsed: 54.530991554260254\n",
            "Epoch: 17 - Train loss: 0.8743478184062723, Val Loss: 0.997709320005724, Train Acc: 0.5726666666666667, Val Acc: 0.4856, Time Elapsed: 54.33864116668701\n",
            "Epoch: 18 - Train loss: 0.8515852184092264, Val Loss: 0.946961121286376, Train Acc: 0.5878888888888889, Val Acc: 0.47386666666666666, Time Elapsed: 54.906288623809814\n",
            "Epoch: 19 - Train loss: 0.8285711705684662, Val Loss: 1.4065602166672884, Train Acc: 0.6059629629629629, Val Acc: 0.40546666666666664, Time Elapsed: 54.42872452735901\n",
            "Epoch: 20 - Train loss: 0.808499814915996, Val Loss: 1.2017810165376988, Train Acc: 0.6151481481481481, Val Acc: 0.45613333333333334, Time Elapsed: 53.95767903327942\n",
            "Epoch: 21 - Train loss: 0.7913322008051579, Val Loss: 1.1660289994235766, Train Acc: 0.6285185185185185, Val Acc: 0.47613333333333335, Time Elapsed: 54.41618537902832\n",
            "Epoch: 22 - Train loss: 0.7804999314778224, Val Loss: 0.8302379856675358, Train Acc: 0.6373703703703704, Val Acc: 0.6108, Time Elapsed: 54.51794624328613\n",
            "Epoch: 23 - Train loss: 0.7603228429764933, Val Loss: 0.9390490221775184, Train Acc: 0.6466666666666666, Val Acc: 0.5697333333333333, Time Elapsed: 54.44375133514404\n",
            "Epoch: 24 - Train loss: 0.7402931908013132, Val Loss: 2.713272024135468, Train Acc: 0.6581111111111111, Val Acc: 0.34613333333333335, Time Elapsed: 54.484020709991455\n",
            "--- Saving model ---\n",
            "Epoch: 25 - Train loss: 0.7250376309828735, Val Loss: 0.8562002305762243, Train Acc: 0.6625555555555556, Val Acc: 0.5985333333333334, Time Elapsed: 54.376681089401245\n",
            "--- Saving model ---\n",
            "Epoch: 26 - Train loss: 0.7054215517089265, Val Loss: 0.7511127212290036, Train Acc: 0.6763333333333333, Val Acc: 0.6537333333333334, Time Elapsed: 53.94025254249573\n",
            "--- Saving model ---\n",
            "Epoch: 27 - Train loss: 0.6811902910047233, Val Loss: 0.7213798448190851, Train Acc: 0.6895185185185185, Val Acc: 0.6598666666666667, Time Elapsed: 55.00037884712219\n",
            "Epoch: 28 - Train loss: 0.6701017944451192, Val Loss: 0.970392182343087, Train Acc: 0.6972962962962963, Val Acc: 0.5866666666666667, Time Elapsed: 54.42753577232361\n",
            "Epoch: 29 - Train loss: 0.6430769938428255, Val Loss: 0.9036096283944987, Train Acc: 0.7120740740740741, Val Acc: 0.5956, Time Elapsed: 54.315351724624634\n",
            "Epoch: 30 - Train loss: 0.6190555257396111, Val Loss: 0.7773297806665048, Train Acc: 0.725, Val Acc: 0.6572, Time Elapsed: 54.84781241416931\n",
            "--- Saving model ---\n",
            "Epoch: 31 - Train loss: 0.5966688916440258, Val Loss: 0.7194999474084983, Train Acc: 0.7342592592592593, Val Acc: 0.6613333333333333, Time Elapsed: 55.01952242851257\n",
            "--- Saving model ---\n",
            "Epoch: 32 - Train loss: 0.5691104640751653, Val Loss: 0.6007401190571866, Train Acc: 0.7517777777777778, Val Acc: 0.7433333333333333, Time Elapsed: 54.70214295387268\n",
            "Epoch: 33 - Train loss: 0.5408604585022723, Val Loss: 0.6566635443497513, Train Acc: 0.7647777777777778, Val Acc: 0.7053333333333334, Time Elapsed: 54.31816816329956\n",
            "Epoch: 34 - Train loss: 0.5082045771640624, Val Loss: 0.6005836778273017, Train Acc: 0.7804074074074074, Val Acc: 0.7329333333333333, Time Elapsed: 54.51151371002197\n",
            "--- Saving model ---\n",
            "Epoch: 35 - Train loss: 0.48822593145178395, Val Loss: 0.5769585514977827, Train Acc: 0.7887777777777778, Val Acc: 0.7564, Time Elapsed: 54.294909954071045\n",
            "Epoch: 36 - Train loss: 0.4675343707556973, Val Loss: 0.7671019750631461, Train Acc: 0.8015925925925926, Val Acc: 0.6818666666666666, Time Elapsed: 54.478055000305176\n",
            "--- Saving model ---\n",
            "Epoch: 37 - Train loss: 0.4419192279472735, Val Loss: 0.5312260511189194, Train Acc: 0.8168148148148148, Val Acc: 0.78, Time Elapsed: 53.07634401321411\n",
            "Epoch: 38 - Train loss: 0.42744515137084854, Val Loss: 0.5344831476272163, Train Acc: 0.8227407407407408, Val Acc: 0.7677333333333334, Time Elapsed: 53.57981848716736\n",
            "Epoch: 39 - Train loss: 0.40092721087107724, Val Loss: 0.5630425238445148, Train Acc: 0.8352222222222222, Val Acc: 0.7744, Time Elapsed: 53.57484984397888\n",
            "--- Saving model ---\n",
            "Epoch: 40 - Train loss: 0.392608871250921, Val Loss: 0.5083448523813385, Train Acc: 0.8405185185185186, Val Acc: 0.7858666666666667, Time Elapsed: 54.3673312664032\n",
            "Epoch: 41 - Train loss: 0.37132707979724305, Val Loss: 0.6182357855772568, Train Acc: 0.8472222222222222, Val Acc: 0.7473333333333333, Time Elapsed: 53.27286076545715\n",
            "--- Saving model ---\n",
            "Epoch: 42 - Train loss: 0.35191332513545925, Val Loss: 0.3875397167215913, Train Acc: 0.8597037037037037, Val Acc: 0.8421333333333333, Time Elapsed: 53.27880525588989\n",
            "Epoch: 43 - Train loss: 0.34088555179642277, Val Loss: 0.44252812410941567, Train Acc: 0.8633703703703703, Val Acc: 0.8232, Time Elapsed: 53.675230979919434\n",
            "Epoch: 44 - Train loss: 0.3250979531503402, Val Loss: 0.41812402994956, Train Acc: 0.8706666666666667, Val Acc: 0.8325333333333333, Time Elapsed: 53.51250147819519\n",
            "--- Saving model ---\n",
            "Epoch: 45 - Train loss: 0.31986451502094904, Val Loss: 0.36653928976442857, Train Acc: 0.8727777777777778, Val Acc: 0.8564, Time Elapsed: 53.50618529319763\n",
            "--- Saving model ---\n",
            "Epoch: 46 - Train loss: 0.30854374661123585, Val Loss: 0.33332342963855144, Train Acc: 0.8797407407407407, Val Acc: 0.8654666666666667, Time Elapsed: 52.76752543449402\n",
            "Epoch: 47 - Train loss: 0.30013732386143854, Val Loss: 0.35716559169656137, Train Acc: 0.8821111111111111, Val Acc: 0.8576, Time Elapsed: 53.68543314933777\n",
            "Epoch: 48 - Train loss: 0.28974566889388303, Val Loss: 0.5188853207786205, Train Acc: 0.8861481481481481, Val Acc: 0.7842666666666667, Time Elapsed: 53.75920629501343\n",
            "Epoch: 49 - Train loss: 0.287670285215875, Val Loss: 0.4198654494169405, Train Acc: 0.8881481481481481, Val Acc: 0.8318666666666666, Time Elapsed: 53.58061456680298\n",
            "--- Saving model ---\n",
            "Epoch: 50 - Train loss: 0.27545261033479634, Val Loss: 0.29818488935292775, Train Acc: 0.8942962962962963, Val Acc: 0.8804, Time Elapsed: 52.96794867515564\n",
            "Epoch: 51 - Train loss: 0.2698540467398144, Val Loss: 0.39142294205977757, Train Acc: 0.8947407407407407, Val Acc: 0.8496, Time Elapsed: 53.97066283226013\n",
            "Epoch: 52 - Train loss: 0.26442594850939033, Val Loss: 0.3844865876865589, Train Acc: 0.8965185185185185, Val Acc: 0.8406666666666667, Time Elapsed: 53.696699380874634\n",
            "--- Saving model ---\n",
            "Epoch: 53 - Train loss: 0.2630383688868222, Val Loss: 0.3068872392177582, Train Acc: 0.899, Val Acc: 0.8808, Time Elapsed: 54.34735918045044\n",
            "--- Saving model ---\n",
            "Epoch: 54 - Train loss: 0.25425768163390633, Val Loss: 0.2920581019657143, Train Acc: 0.9017037037037037, Val Acc: 0.8849333333333333, Time Elapsed: 52.79011011123657\n",
            "Epoch: 55 - Train loss: 0.24523686515211493, Val Loss: 0.3029242546250254, Train Acc: 0.905074074074074, Val Acc: 0.8833333333333333, Time Elapsed: 53.46973419189453\n",
            "Epoch: 56 - Train loss: 0.24392784199725961, Val Loss: 0.3711428725618427, Train Acc: 0.907074074074074, Val Acc: 0.8537333333333333, Time Elapsed: 53.45844841003418\n",
            "Epoch: 57 - Train loss: 0.235390551706061, Val Loss: 0.31679878434387304, Train Acc: 0.9100370370370371, Val Acc: 0.8774666666666666, Time Elapsed: 53.388049602508545\n",
            "Epoch: 58 - Train loss: 0.23800758023468238, Val Loss: 0.30114830070633, Train Acc: 0.9093333333333333, Val Acc: 0.8813333333333333, Time Elapsed: 52.79591178894043\n",
            "Epoch: 59 - Train loss: 0.2326500793804207, Val Loss: 0.31936202962267196, Train Acc: 0.9118888888888889, Val Acc: 0.8829333333333333, Time Elapsed: 53.60039281845093\n",
            "--- Saving model ---\n",
            "Epoch: 60 - Train loss: 0.23244944147730326, Val Loss: 0.26283160003565126, Train Acc: 0.910962962962963, Val Acc: 0.8988, Time Elapsed: 53.78903675079346\n",
            "Epoch: 61 - Train loss: 0.2218530193253716, Val Loss: 0.33793444221176333, Train Acc: 0.9168518518518518, Val Acc: 0.8829333333333333, Time Elapsed: 53.54417586326599\n",
            "Epoch: 62 - Train loss: 0.2187561589377045, Val Loss: 0.28940176086153013, Train Acc: 0.9169259259259259, Val Acc: 0.8912, Time Elapsed: 52.83029079437256\n",
            "--- Saving model ---\n",
            "Epoch: 63 - Train loss: 0.2206276910406012, Val Loss: 0.24992598978392147, Train Acc: 0.9179259259259259, Val Acc: 0.9046666666666666, Time Elapsed: 53.924039363861084\n",
            "Epoch: 64 - Train loss: 0.2132534015376421, Val Loss: 0.2898971940059278, Train Acc: 0.9195555555555556, Val Acc: 0.8936, Time Elapsed: 53.41266679763794\n",
            "Epoch: 65 - Train loss: 0.2137516663096386, Val Loss: 0.3477932412366746, Train Acc: 0.920074074074074, Val Acc: 0.862, Time Elapsed: 53.8660089969635\n",
            "Epoch: 66 - Train loss: 0.2077786134141034, Val Loss: 0.2762194514527159, Train Acc: 0.9206666666666666, Val Acc: 0.8968, Time Elapsed: 53.01237082481384\n",
            "Epoch: 67 - Train loss: 0.19578060178501064, Val Loss: 0.3040653261024568, Train Acc: 0.9265555555555556, Val Acc: 0.8957333333333334, Time Elapsed: 53.922179222106934\n",
            "Epoch: 68 - Train loss: 0.20171062943141607, Val Loss: 0.314950327972992, Train Acc: 0.923037037037037, Val Acc: 0.8777333333333334, Time Elapsed: 53.89667773246765\n",
            "Epoch: 69 - Train loss: 0.1978086763154274, Val Loss: 0.4178479870749732, Train Acc: 0.926074074074074, Val Acc: 0.84, Time Elapsed: 53.70490908622742\n",
            "Epoch: 70 - Train loss: 0.1938756474280527, Val Loss: 0.26789859132999083, Train Acc: 0.9251481481481482, Val Acc: 0.8973333333333333, Time Elapsed: 52.60781979560852\n",
            "Epoch: 71 - Train loss: 0.1944290823722479, Val Loss: 0.28529827419857856, Train Acc: 0.9278888888888889, Val Acc: 0.8901333333333333, Time Elapsed: 53.88691997528076\n",
            "--- Saving model ---\n",
            "Epoch: 72 - Train loss: 0.1904684790538103, Val Loss: 0.26142511480503666, Train Acc: 0.9287407407407408, Val Acc: 0.9113333333333333, Time Elapsed: 53.95715045928955\n",
            "Epoch: 73 - Train loss: 0.1875921008199171, Val Loss: 0.2563931888000945, Train Acc: 0.9303333333333333, Val Acc: 0.9074666666666666, Time Elapsed: 53.95113921165466\n",
            "Epoch: 74 - Train loss: 0.19053390703353837, Val Loss: 0.27982344020600036, Train Acc: 0.9298518518518518, Val Acc: 0.8906666666666667, Time Elapsed: 54.3094117641449\n",
            "Epoch: 75 - Train loss: 0.1771713268823957, Val Loss: 0.2601265332077519, Train Acc: 0.9330740740740741, Val Acc: 0.9026666666666666, Time Elapsed: 54.0084912776947\n",
            "Epoch: 76 - Train loss: 0.18359484354925665, Val Loss: 0.25950698592400145, Train Acc: 0.9298518518518518, Val Acc: 0.9062666666666667, Time Elapsed: 54.44071936607361\n",
            "Epoch: 77 - Train loss: 0.17745610340652873, Val Loss: 0.3743763288514594, Train Acc: 0.9327037037037037, Val Acc: 0.8493333333333334, Time Elapsed: 54.5293493270874\n",
            "Epoch: 78 - Train loss: 0.17406177202071907, Val Loss: 0.29839599700802466, Train Acc: 0.9358888888888889, Val Acc: 0.8874666666666666, Time Elapsed: 54.42753887176514\n",
            "Epoch: 79 - Train loss: 0.171288601939359, Val Loss: 0.2607878514139329, Train Acc: 0.9367777777777778, Val Acc: 0.9009333333333334, Time Elapsed: 54.605177879333496\n",
            "Epoch: 80 - Train loss: 0.1722381949530676, Val Loss: 0.30015072868189824, Train Acc: 0.9356296296296296, Val Acc: 0.902, Time Elapsed: 54.678327322006226\n",
            "Epoch: 81 - Train loss: 0.16939012247239243, Val Loss: 0.26524568361751105, Train Acc: 0.9377777777777778, Val Acc: 0.9034666666666666, Time Elapsed: 54.016082763671875\n",
            "Epoch: 82 - Train loss: 0.1625340896971983, Val Loss: 0.2568697568376438, Train Acc: 0.9375555555555556, Val Acc: 0.9065333333333333, Time Elapsed: 54.2041711807251\n",
            "Epoch: 83 - Train loss: 0.16391609804660648, Val Loss: 0.23740342129968992, Train Acc: 0.9400740740740741, Val Acc: 0.9088, Time Elapsed: 54.75468993186951\n",
            "--- Saving model ---\n",
            "Epoch: 84 - Train loss: 0.1640335058669838, Val Loss: 0.2239506053975073, Train Acc: 0.9381851851851852, Val Acc: 0.914, Time Elapsed: 54.96585130691528\n",
            "--- Saving model ---\n",
            "Epoch: 85 - Train loss: 0.16072360676894257, Val Loss: 0.25288144991559497, Train Acc: 0.9402592592592592, Val Acc: 0.9146666666666666, Time Elapsed: 54.85245966911316\n",
            "Epoch: 86 - Train loss: 0.1625395201817508, Val Loss: 0.235156345165382, Train Acc: 0.9393333333333334, Val Acc: 0.9109333333333334, Time Elapsed: 54.756327390670776\n",
            "Epoch: 87 - Train loss: 0.15453412931995086, Val Loss: 0.24931837864598985, Train Acc: 0.943037037037037, Val Acc: 0.9074666666666666, Time Elapsed: 54.189451456069946\n",
            "Epoch: 88 - Train loss: 0.15649990755976376, Val Loss: 0.2287532312448247, Train Acc: 0.942, Val Acc: 0.9132, Time Elapsed: 54.34055042266846\n",
            "--- Saving model ---\n",
            "Epoch: 89 - Train loss: 0.1486316998069885, Val Loss: 0.20500538046708552, Train Acc: 0.9448888888888889, Val Acc: 0.9228, Time Elapsed: 54.697306871414185\n",
            "Epoch: 90 - Train loss: 0.15018228458249455, Val Loss: 0.30927532086527704, Train Acc: 0.9437037037037037, Val Acc: 0.898, Time Elapsed: 54.683356523513794\n",
            "Epoch: 91 - Train loss: 0.1481054414690459, Val Loss: 0.2930350517064838, Train Acc: 0.9461481481481482, Val Acc: 0.8892, Time Elapsed: 54.19490957260132\n",
            "Epoch: 92 - Train loss: 0.14711291646205277, Val Loss: 0.22861600199998436, Train Acc: 0.9456666666666667, Val Acc: 0.9156, Time Elapsed: 54.575870513916016\n",
            "Epoch: 93 - Train loss: 0.14633047864088233, Val Loss: 0.24758969286803978, Train Acc: 0.9453703703703704, Val Acc: 0.9157333333333333, Time Elapsed: 54.83979821205139\n",
            "Epoch: 94 - Train loss: 0.14326241862037664, Val Loss: 0.2543494878810341, Train Acc: 0.9478518518518518, Val Acc: 0.9109333333333334, Time Elapsed: 53.864956855773926\n",
            "Epoch: 95 - Train loss: 0.14173629975732044, Val Loss: 0.24450557691566013, Train Acc: 0.9475925925925925, Val Acc: 0.9036, Time Elapsed: 54.33487915992737\n",
            "Epoch: 96 - Train loss: 0.14833686880820313, Val Loss: 0.2087718036215184, Train Acc: 0.9451111111111111, Val Acc: 0.9221333333333334, Time Elapsed: 54.20440149307251\n",
            "--- Saving model ---\n",
            "Epoch: 97 - Train loss: 0.1383358250985637, Val Loss: 0.2041889763105724, Train Acc: 0.9492592592592592, Val Acc: 0.9261333333333334, Time Elapsed: 54.62347745895386\n",
            "--- Saving model ---\n",
            "Epoch: 98 - Train loss: 0.14240439559257992, Val Loss: 0.2055456891529641, Train Acc: 0.9472962962962963, Val Acc: 0.9274666666666667, Time Elapsed: 55.053794384002686\n",
            "Epoch: 99 - Train loss: 0.13981285866049795, Val Loss: 0.21391841785003574, Train Acc: 0.9474074074074074, Val Acc: 0.9204, Time Elapsed: 55.23759698867798\n",
            "Epoch: 100 - Train loss: 0.13744552757908807, Val Loss: 0.2091413611956572, Train Acc: 0.9498888888888889, Val Acc: 0.9229333333333334, Time Elapsed: 53.96821141242981\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        "# Plot loss and accuracy curves\n",
        "trainer.plot_metrics()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "Wq0hDETaQAu5",
        "outputId": "e041c33f-d49b-4e4d-de7d-3740036cf143"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f2835e73e20>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Inference on the test data\n",
        "trainer.test()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDZqrykajRQK",
        "outputId": "80b104f9-9938-4260-c959-5f6ebfe72b52"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.21003351217888772, Test Acc: 0.9206666666666666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ROC/AUC"
      ],
      "metadata": {
        "id": "4Jy7HxBHVQsh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is a link to my [model](https://drive.google.com/file/d/1cHz89YnG22v7lUcOdKtBhbPSbFRhGx14/view?usp=share_link), you can just call the load function here as the path to my model in drive is defined inside the load method."
      ],
      "metadata": {
        "id": "5dHxDXddhtsJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading saved model\n",
        "trainer.load()"
      ],
      "metadata": {
        "id": "JO6Ro-TQjkTb"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting One vs Rest ROC curves\n",
        "trainer.plot_roc_curves()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "K7lR5xDTLMek",
        "outputId": "d157a1d5-f87c-46b5-c539-951e91fa8b31"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "trainer.plot_confusion_matrix()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "7iDv4GD59LXt",
        "outputId": "f0701f1d-a526-456e-c1ae-0a989282430f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report\n",
        "trainer.classification_report()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0t-P127P9QgS",
        "outputId": "11bcaa69-0505-4075-ccdf-fdc778dd83f9"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.95      0.94      1000\n",
            "           1       0.95      0.92      0.94      1000\n",
            "           2       0.89      0.91      0.90      1000\n",
            "\n",
            "    accuracy                           0.93      3000\n",
            "   macro avg       0.93      0.93      0.93      3000\n",
            "weighted avg       0.93      0.93      0.93      3000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion"
      ],
      "metadata": {
        "id": "eZPdD0_WEAmu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We can see that resnet has performed relatively well, and has acheived a decent macro average AUC score of 0.99. \n",
        "\n",
        "- One problem we can see is the spikes in validation loss and validation accuracy, which can be mitigated if we apply proper regularization which includes, \n",
        " - Ridge/Lasso Regression (L2/L1 Regularization)\n",
        "\n",
        " - Adding drop out layers in the model\n",
        "\n",
        " - Better data augmentation\n",
        "\n",
        "- Things that I missed/couldn't include because of time constraint:\n",
        "\n",
        " - Initializing weights using Kaiming Initialization for better convergence. [Weight Initializaiton](https://adityassrana.github.io/blog/theory/2020/08/26/Weight-Init.html#Solution:-Let's-Compare-Differrent-Initialization-Strategies)\n",
        "\n",
        " - Cross validation for getting the most optimal data split into the network. [Cross Validation](https://www.cs.cmu.edu/~schneide/tut5/node42.html)\n",
        " - Adding Proxy Normalizatin Layers instead of Batch Normalization [Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence\n",
        "](https://arxiv.org/pdf/2106.03743.pdf)\n",
        " - Adapting to [Pytorch Lightning](https://lightning.ai/docs/pytorch/stable/)"
      ],
      "metadata": {
        "id": "_9dF0XO_4x8u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# References\n",
        "\n",
        "1. [Decoding Dark Matter Substructure without Supervision](https://arxiv.org/pdf/2008.12731.pdf)\n",
        "\n",
        "2. [Deep Learning the Morphology of Dark Matter Substructure](https://arxiv.org/pdf/1909.07346.pdf)\n",
        "\n",
        "3. [Domain Adaptation for Simulation-Based Dark Matter Searches\n",
        "Using Strong Gravitational Lensing](https://arxiv.org/pdf/2112.12121.pdf)\n",
        "\n",
        "4. [A DISCIPLINED APPROACH TO NEURAL NETWORK\n",
        "HYPER-PARAMETERS: PART 1 – LEARNING RATE,\n",
        "BATCH SIZE, MOMENTUM, AND WEIGHT DECAY](https://arxiv.org/abs/1803.09820v2)\n",
        "\n",
        "5. [A Visual Guide to Learning Rate Schedulers in PyTorch](https://towardsdatascience.com/a-visual-guide-to-learning-rate-schedulers-in-pytorch-24bbb262c863)\n",
        "\n",
        "6. [Deep transfer learning for image classification: a survey\n",
        "](https://arxiv.org/pdf/2205.09904.pdf)\n",
        "\n",
        "7. [FineTuning TorchVision Models](https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html)\n",
        "\n",
        "8. [The Effect of the Normalization Method Used in Different Sample Sizes on\n",
        "the Success of Artificial Neural Network Model](https://files.eric.ed.gov/fulltext/EJ1246486.pdf)\n",
        "\n",
        "9. [Classification: ROC Curve and AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc#:~:text=An%20ROC%20curve%20(receiver%20operating,False%20Positive%20Rate)"
      ],
      "metadata": {
        "id": "ioON_yblED0l"
      }
    }
  ]
}