CelebA_Gender_Young.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import copy\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "import torchvision\n",
    "from torchvision import models\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda cpu\n"
     ]
    }
   ],
   "source": [
    "device_cuda = torch.device(\"cuda\")\n",
    "device_cpu = torch.device(\"cpu\")\n",
    "print(device_cuda, device_cpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_folder = os.getcwd()\n",
    "data_folder = os.path.join(project_folder, \"Data\\\\celeba\")\n",
    "img_folder = os.path.join(data_folder, \"img_align_celeba\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>image_id</th>\n",
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      ],
      "text/plain": [
       "          image_id  5_o_Clock_Shadow  Arched_Eyebrows  Attractive  \\\n",
       "0       000001.jpg                -1                1           1   \n",
       "1       000002.jpg                -1               -1          -1   \n",
       "2       000003.jpg                -1               -1          -1   \n",
       "3       000004.jpg                -1               -1           1   \n",
       "4       000005.jpg                -1                1           1   \n",
       "...            ...               ...              ...         ...   \n",
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       "202595  202596.jpg                -1               -1          -1   \n",
       "202596  202597.jpg                -1               -1          -1   \n",
       "202597  202598.jpg                -1                1           1   \n",
       "202598  202599.jpg                -1                1           1   \n",
       "\n",
       "        Bags_Under_Eyes  Bald  Bangs  Big_Lips  Big_Nose  Black_Hair  ...  \\\n",
       "0                    -1    -1     -1        -1        -1          -1  ...   \n",
       "1                     1    -1     -1        -1         1          -1  ...   \n",
       "2                    -1    -1     -1         1        -1          -1  ...   \n",
       "3                    -1    -1     -1        -1        -1          -1  ...   \n",
       "4                    -1    -1     -1         1        -1          -1  ...   \n",
       "...                 ...   ...    ...       ...       ...         ...  ...   \n",
       "202594               -1    -1     -1         1        -1          -1  ...   \n",
       "202595               -1    -1      1         1        -1          -1  ...   \n",
       "202596               -1    -1     -1        -1        -1           1  ...   \n",
       "202597               -1    -1     -1         1        -1           1  ...   \n",
       "202598               -1    -1     -1        -1        -1          -1  ...   \n",
       "\n",
       "        Sideburns  Smiling  Straight_Hair  Wavy_Hair  Wearing_Earrings  \\\n",
       "0              -1        1              1         -1                 1   \n",
       "1              -1        1             -1         -1                -1   \n",
       "2              -1       -1             -1          1                -1   \n",
       "3              -1       -1              1         -1                 1   \n",
       "4              -1       -1             -1         -1                -1   \n",
       "...           ...      ...            ...        ...               ...   \n",
       "202594         -1       -1             -1         -1                -1   \n",
       "202595         -1        1              1         -1                -1   \n",
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       "202597         -1        1             -1          1                 1   \n",
       "202598         -1       -1             -1          1                -1   \n",
       "\n",
       "        Wearing_Hat  Wearing_Lipstick  Wearing_Necklace  Wearing_Necktie  \\\n",
       "0                -1                 1                -1               -1   \n",
       "1                -1                -1                -1               -1   \n",
       "2                -1                -1                -1               -1   \n",
       "3                -1                 1                 1               -1   \n",
       "4                -1                 1                -1               -1   \n",
       "...             ...               ...               ...              ...   \n",
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       "202597           -1                 1                -1               -1   \n",
       "202598           -1                 1                -1               -1   \n",
       "\n",
       "        Young  \n",
       "0           1  \n",
       "1           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  \n",
       "...       ...  \n",
       "202594      1  \n",
       "202595      1  \n",
       "202596      1  \n",
       "202597      1  \n",
       "202598      1  \n",
       "\n",
       "[202599 rows x 41 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(os.path.join(data_folder, 'list_attr_celeba.csv'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Male  Young\n",
       "0     0      1\n",
       "1     0      1\n",
       "2     1      1\n",
       "3     0      1\n",
       "4     0      1"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(os.path.join(data_folder, 'list_attr_celeba.csv'), usecols=['Male','Young'])\n",
    "df1.loc[df1['Male'] == -1, 'Male'] = 0\n",
    "df1.loc[df1['Young'] == -1, 'Young'] = 0\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>202595</th>\n",
       "      <td>202596.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202596</th>\n",
       "      <td>202597.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202597</th>\n",
       "      <td>202598.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202598</th>\n",
       "      <td>202599.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202599 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Filename  Partition\n",
       "0       000001.jpg          0\n",
       "1       000002.jpg          0\n",
       "2       000003.jpg          0\n",
       "3       000004.jpg          0\n",
       "4       000005.jpg          0\n",
       "...            ...        ...\n",
       "202594  202595.jpg          2\n",
       "202595  202596.jpg          2\n",
       "202596  202597.jpg          2\n",
       "202597  202598.jpg          2\n",
       "202598  202599.jpg          2\n",
       "\n",
       "[202599 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(os.path.join(data_folder, 'list_eval_partition.csv'))\n",
    "\n",
    "df2.columns = ['Filename', 'Partition']\n",
    "#df2 = df2.set_index('Filename')\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Male</th>\n",
       "      <th>Young</th>\n",
       "      <th>Partition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000001.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000003.jpg</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000004.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202595.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202596.jpg</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202597.jpg</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202598.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202599.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202599 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Male  Young  Partition\n",
       "Filename                          \n",
       "000001.jpg     0      1          0\n",
       "000002.jpg     0      1          0\n",
       "000003.jpg     1      1          0\n",
       "000004.jpg     0      1          0\n",
       "000005.jpg     0      1          0\n",
       "...          ...    ...        ...\n",
       "202595.jpg     0      1          2\n",
       "202596.jpg     1      1          2\n",
       "202597.jpg     1      1          2\n",
       "202598.jpg     0      1          2\n",
       "202599.jpg     0      1          2\n",
       "\n",
       "[202599 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2\n",
    "\n",
    "#df1의 인덱스를 기준으로 df1과 df2 병합하기\n",
    "\n",
    "df = df1.join(df2)\n",
    "df = df.set_index('Filename')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Male</th>\n",
       "      <th>Young</th>\n",
       "      <th>Filename</th>\n",
       "      <th>Partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>000001.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>000002.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>000003.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>000004.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>000005.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Male  Young    Filename  Partition\n",
       "0     0      1  000001.jpg          0\n",
       "1     0      1  000002.jpg          0\n",
       "2     1      1  000003.jpg          0\n",
       "3     0      1  000004.jpg          0\n",
       "4     0      1  000005.jpg          0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df1.merge(df2, left_index=True, right_index=True)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Male</th>\n",
       "      <th>Young</th>\n",
       "      <th>Partition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000001.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000003.jpg</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000004.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Male  Young  Partition\n",
       "Filename                          \n",
       "000001.jpg     0      1          0\n",
       "000002.jpg     0      1          0\n",
       "000003.jpg     1      1          0\n",
       "000004.jpg     0      1          0\n",
       "000005.jpg     0      1          0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv('celeba-gender-partitions.csv')\n",
    "df4 = pd.read_csv('celeba-gender-partitions.csv', index_col=0)\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Male</th>\n",
       "      <th>Young</th>\n",
       "      <th>Partition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000006.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000016.jpg</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000027.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000029.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000037.jpg</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202534.jpg</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202544.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202554.jpg</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202568.jpg</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202578.jpg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20260 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Male  Young  Partition\n",
       "Filename                          \n",
       "000006.jpg     0      1          0\n",
       "000016.jpg     1      1          0\n",
       "000027.jpg     0      1          0\n",
       "000029.jpg     0      1          0\n",
       "000037.jpg     1      1          0\n",
       "...          ...    ...        ...\n",
       "202534.jpg     1      1          2\n",
       "202544.jpg     0      1          2\n",
       "202554.jpg     1      1          2\n",
       "202568.jpg     1      0          2\n",
       "202578.jpg     0      1          2\n",
       "\n",
       "[20260 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "remove_n = int(len(df4)*0.7)\n",
    "drop_indices = np.random.choice(df4.index, remove_n, replace=False)\n",
    "df_subset = df4.drop(drop_indices)\n",
    "df_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_subset.loc[df4['Partition'] == 0].to_csv('celeba-old-partitions_train.csv')\n",
    "df_subset.loc[df4['Partition'] == 1].to_csv('celeba-old-partitions_valid.csv')\n",
    "df_subset.loc[df4['Partition'] == 2].to_csv('celeba-old-partitions_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_train = pd.read_csv('celeba-old-partitions_train.csv')\n",
    "label_valid = pd.read_csv('celeba-old-partitions_valid.csv')\n",
    "label_test = pd.read_csv('celeba-old-partitions_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Young : 12714\n",
      "Train Old : 3688\n",
      "Valid Young : 1459\n",
      "Valid Old : 478\n",
      "Test Young : 1451\n",
      "Test Old : 470\n"
     ]
    }
   ],
   "source": [
    "print('Train Young : ' +str(len(label_train.loc[label_train['Young']==1])))\n",
    "print('Train Old : ' +str(len(label_train.loc[label_train['Young']==0])))\n",
    "print('Valid Young : ' +str(len(label_valid.loc[label_valid['Young']==1])))\n",
    "print('Valid Old : ' +str(len(label_valid.loc[label_valid['Young']==0])))\n",
    "print('Test Young : ' +str(len(label_test.loc[label_test['Young']==1])))\n",
    "print('Test Old : ' +str(len(label_test.loc[label_test['Young']==0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Young Male : 4349\n",
      "Old Male : 2558\n",
      "Young Female : 8365\n",
      "Old Female : 1130\n"
     ]
    }
   ],
   "source": [
    "print('Young Male : '+ str(len(label_train.loc[(label_train['Male'] == 1) & (label_train['Young']==1)])))\n",
    "print('Old Male : '+ str(len(label_train.loc[(label_train['Male'] == 1) & (label_train['Young']==0)])))\n",
    "print('Young Female : '+ str(len(label_train.loc[(label_train['Male'] == 0) & (label_train['Young']==1)])))\n",
    "print('Old Female : '+ str(len(label_train.loc[(label_train['Male'] == 0) & (label_train['Young']==0)])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Male</th>\n",
       "      <th>Young</th>\n",
       "      <th>Partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16297.000000</td>\n",
       "      <td>16297.000000</td>\n",
       "      <td>16297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.417071</td>\n",
       "      <td>0.776891</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.493090</td>\n",
       "      <td>0.416343</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Male         Young  Partition\n",
       "count  16297.000000  16297.000000    16297.0\n",
       "mean       0.417071      0.776891        0.0\n",
       "std        0.493090      0.416343        0.0\n",
       "min        0.000000      0.000000        0.0\n",
       "25%        0.000000      1.000000        0.0\n",
       "50%        0.000000      1.000000        0.0\n",
       "75%        1.000000      1.000000        0.0\n",
       "max        1.000000      1.000000        0.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Male</th>\n",
       "      <th>Young</th>\n",
       "      <th>Partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.385657</td>\n",
       "      <td>0.760281</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486872</td>\n",
       "      <td>0.427019</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Male        Young  Partition\n",
       "count  1994.000000  1994.000000     1994.0\n",
       "mean      0.385657     0.760281        2.0\n",
       "std       0.486872     0.427019        0.0\n",
       "min       0.000000     0.000000        2.0\n",
       "25%       0.000000     1.000000        2.0\n",
       "50%       0.000000     1.000000        2.0\n",
       "75%       1.000000     1.000000        2.0\n",
       "max       1.000000     1.000000        2.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Male</th>\n",
       "      <th>Young</th>\n",
       "      <th>Partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>000051.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>000067.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>000127.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>000156.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>000193.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16392</th>\n",
       "      <td>162697.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16394</th>\n",
       "      <td>162718.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16395</th>\n",
       "      <td>162728.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16398</th>\n",
       "      <td>162736.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16400</th>\n",
       "      <td>162761.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3688 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Filename  Male  Young  Partition\n",
       "7      000051.jpg     1      0          0\n",
       "12     000067.jpg     1      0          0\n",
       "15     000127.jpg     1      0          0\n",
       "18     000156.jpg     0      0          0\n",
       "23     000193.jpg     1      0          0\n",
       "...           ...   ...    ...        ...\n",
       "16392  162697.jpg     1      0          0\n",
       "16394  162718.jpg     0      0          0\n",
       "16395  162728.jpg     1      0          0\n",
       "16398  162736.jpg     0      0          0\n",
       "16400  162761.jpg     0      0          0\n",
       "\n",
       "[3688 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_train.loc[label_train['Young']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "AUGMENTATION = False\n",
    "\n",
    "if AUGMENTATION == True:\n",
    "    \n",
    "    transform_aug = transforms.Compose(\n",
    "                    [transforms.RandomHorizontalFlip(p = 0.5),\n",
    "                     transforms.RandomAffine(degrees = 360)])\n",
    "                    #transforms.RandomRotation(degrees = 30),\n",
    "\n",
    "    output_folder = os.path.join(data_folder, \"aug_old\")\n",
    "    aug_train = copy.deepcopy(label_train)    \n",
    "\n",
    "    for file in label_train.loc[label_train['Young']==0]['Filename']:\n",
    "        image = Image.open(os.path.join(img_folder, file))\n",
    "        image.save(os.path.join(output_folder,file))\n",
    "        new = copy.deepcopy(label_train.loc[label_train['Filename']==file])\n",
    "        \n",
    "        file2 = file[:-4]+'_2.jpg'\n",
    "        new['Filename'] = file2\n",
    "        aug_train = aug_train.append(new, ignore_index=True)\n",
    "        \n",
    "        aug_img1 = transform_aug(image)\n",
    "        aug_img1.save(os.path.join(output_folder,file2))\n",
    "        \n",
    "        file3 = file[:-4]+'_3.jpg'\n",
    "        new['Filename'] = file3\n",
    "        aug_train = aug_train.append(new, ignore_index=True)\n",
    "        \n",
    "        aug_img2 = transform_aug(image)\n",
    "        aug_img2.save(os.path.join(output_folder,file3))\n",
    "        \n",
    "    for file in label_train.loc[label_train['Young']==1]['Filename']:\n",
    "        image = Image.open(os.path.join(img_folder, file))\n",
    "        image.save(os.path.join(output_folder,file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "aug_train.set_index('Filename').to_csv('celeba-old-augmentations_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Image.open(aug_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CelebaDataset(Dataset):\n",
    "    \"\"\"Custom Dataset for loading CelebA face images\"\"\"\n",
    "\n",
    "    def __init__(self, csv_path, img_dir, transform=None):\n",
    "    \n",
    "        df = pd.read_csv(csv_path, index_col=0)\n",
    "        self.img_dir = img_dir\n",
    "        self.csv_path = csv_path\n",
    "        self.img_names = df.index.values\n",
    "        self.y = df['Male'].values\n",
    "        self.att = df['Young'].values\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = Image.open(os.path.join(self.img_dir,\n",
    "                                      self.img_names[index]))\n",
    "        \n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        \n",
    "        label = self.y[index]\n",
    "        attribute = self.att[index]\n",
    "        return img, label, attribute\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.y.shape[0]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 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): Linear(in_features=512, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Resnet18 = copy.deepcopy(models.resnet18(pretrained = True))\n",
    "Resnet18.fc = nn.Linear(512, 1)\n",
    "Resnet18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Train(model, optimizer, loss_fn, n_epochs, scheduler = None, OVER = False):\n",
    "    \n",
    "    torch.cuda.empty_cache()    \n",
    "    train_losses = []\n",
    "    train_accs = []\n",
    "    val_losses = []\n",
    "    val_accs = []\n",
    "    best_loss = 10 ** 3\n",
    "    patience_limit = 10\n",
    "    patience_check = 0   \n",
    "    \n",
    "    for epoch in range(n_epochs):\n",
    "        running_loss = 0\n",
    "        running_acc = 0\n",
    "        \n",
    "        model.train()\n",
    "        if OVER == True:\n",
    "            loader = aug_train_loader\n",
    "            dataset = aug_train_dataset\n",
    "        else:\n",
    "            loader = train_loader\n",
    "            dataset = train_dataset\n",
    "        \n",
    "        for x, y_t, att in loader:\n",
    "            x, y_t, att = x.to(device_cuda), y_t.to(device_cuda, dtype=torch.float32), att.to(device_cuda, dtype=torch.float32)\n",
    "            output = torch.sigmoid(model(x).flatten())\n",
    "            \n",
    "            ones_like_y = torch.ones_like(output, dtype = torch.int).to(device_cuda)\n",
    "            zeros_like_y = torch.zeros_like(output, dtype = torch.int).to(device_cuda)\n",
    "            y_pred = torch.where(output >= ones_like_y * 0.5, ones_like_y, zeros_like_y)\n",
    "            \n",
    "            criterion = loss_fn\n",
    "            loss = criterion(output, y_t)\n",
    "            optimizer.zero_grad() \n",
    "            loss.backward() \n",
    "            optimizer.step()\n",
    "                        \n",
    "            running_loss += loss.item()\n",
    "            running_acc += torch.sum(y_pred == y_t)\n",
    "            \n",
    "            del x\n",
    "            torch.cuda.empty_cache()\n",
    "            \n",
    "        if scheduler is not None:\n",
    "            scheduler.step()\n",
    "        \n",
    "        model.eval()\n",
    "        val_loss = 0\n",
    "        val_acc = 0\n",
    "        with torch.no_grad():\n",
    "            for x, y_t, att in valid_loader:\n",
    "                x, y_t, att = x.to(device_cuda), y_t.to(device_cuda, dtype=torch.float32), att.to(device_cuda, dtype=torch.float32)\n",
    "                \n",
    "                output = torch.sigmoid(model(x).flatten())\n",
    "                \n",
    "                ones_like_y = torch.ones_like(output, dtype = torch.int).to(device_cuda)\n",
    "                zeros_like_y = torch.zeros_like(output, dtype = torch.int).to(device_cuda)\n",
    "                y_pred = torch.where(output >= ones_like_y * 0.5, ones_like_y, zeros_like_y)\n",
    "                \n",
    "                # evaluation metrics\n",
    "                criterion = loss_fn\n",
    "                loss = criterion(output, y_t)         \n",
    "            \n",
    "                val_loss += loss.item()\n",
    "                val_acc += torch.sum(y_pred == y_t)\n",
    "                \n",
    "                del x\n",
    "                torch.cuda.empty_cache()\n",
    "                                \n",
    "            if val_loss > best_loss:\n",
    "                patience_check += 1\n",
    "                print(patience_check)\n",
    "                \n",
    "            else: # loss가 개선된 경우\n",
    "                best_loss = val_loss\n",
    "                patience_check = 0\n",
    "                best_model, best_epoch = copy.deepcopy(model), epoch\n",
    "\n",
    "            if patience_check >= patience_limit: # early stopping\n",
    "                print('Early stopped')\n",
    "                break\n",
    "\n",
    "        # calculate mean for each batch\n",
    "        train_losses.append(running_loss / len(loader))\n",
    "        train_accs.append(running_acc / len(dataset))\n",
    "        val_losses.append(val_loss / len(valid_loader))\n",
    "        val_accs.append(val_acc / len(valid_dataset))\n",
    "\n",
    "        \n",
    "        print(' ')\n",
    "        print(\"Epoch:{}/{}..\".format(epoch + 1, n_epochs),\n",
    "                \"Train Loss: {:.4f}..\".format(running_loss / len(loader)),\n",
    "                \"Train Acc: {:.4f}..\".format(running_acc / len(dataset)),\n",
    "                \"Val Loss: {:.4f}..\".format(val_loss / len(valid_loader)),\n",
    "                \"Val Acc: {:.4f}..\".format(val_acc / len(valid_dataset)),\n",
    "             )\n",
    "\n",
    "    history = {'train_loss': train_losses, 'train_acc': train_accs,\n",
    "               'val_loss': val_losses, 'val_acc': val_accs}\n",
    "    \n",
    "    save_name = os.path.join(model_save_folder, model_name)\n",
    "    torch.save(best_model.state_dict(), save_name)\n",
    "    \n",
    "    model = best_model\n",
    "    \n",
    "    return history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cc\n",
    "def Test(model, loss_fn):\n",
    "    \n",
    "    criterion = loss_fn\n",
    "    # enter evaluation mode\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    with torch.no_grad():\n",
    "        \n",
    "        pred = []\n",
    "        t = []\n",
    "        for x, y_t, att in test_loader:\n",
    "            x, y_t, att = x.to(device_cuda), y_t.to(device_cuda, dtype=torch.float32), att.to(device_cuda, dtype=torch.float32)\n",
    "\n",
    "            output = torch.sigmoid(model(x).flatten())\n",
    "            test_loss += criterion(output, y_t) # sum up batch loss\n",
    "\n",
    "            ones_like_y = torch.ones_like(output, dtype = torch.int).to(device_cuda)\n",
    "            zeros_like_y = torch.zeros_like(output, dtype = torch.int).to(device_cuda)\n",
    "\n",
    "            y_pred = torch.where(output >= ones_like_y * 0.5, ones_like_y, zeros_like_y)\n",
    "            pred.extend(y_pred.cpu().numpy())\n",
    "            t.extend(y_t.cpu().numpy())\n",
    "        \n",
    "        cf_matrix = confusion_matrix(t, pred)\n",
    "\n",
    "        TN = cf_matrix[0,0]\n",
    "        FP = cf_matrix[0,1]\n",
    "        FN = cf_matrix[1,0]\n",
    "        TP = cf_matrix[1,1]\n",
    "\n",
    "        Acc = (TP + TN) / (TP + TN + FP + FN)\n",
    "        PPV = TP / (TP + FP)\n",
    "        NPV = TN / (TN + FN)\n",
    "        Sens = TP / (TP + FN)\n",
    "        F1 = (2 * PPV * Sens) / (PPV + Sens)\n",
    "        Spec = TN / (TN + FP)\n",
    "\n",
    "        test_loss /= len(test_loader)\n",
    "        print('(Accuracy, PPV, NPV, Sensitivity, Specificity, F1, Loss) = %.3f, %.3f, %.3f, %.3f, %.3f, %.3f, %.3f' \n",
    "                          %(Acc, PPV, NPV, Sens, Spec, F1, test_loss))\n",
    "\n",
    "        return Acc, Sens, Spec, PPV, NPV, F1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 128\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.CenterCrop((178, 178)),\n",
    "                                transforms.Resize((224,224))\n",
    "                               ])\n",
    "\n",
    "train_dataset = CelebaDataset(csv_path='celeba-old-partitions_train.csv', # male\n",
    "                              img_dir=img_folder,\n",
    "                              transform=transform)\n",
    "\n",
    "valid_dataset = CelebaDataset(csv_path='celeba-old-partitions_valid.csv',\n",
    "                              img_dir=img_folder,\n",
    "                              transform=transform)\n",
    "\n",
    "test_dataset = CelebaDataset(csv_path='celeba-old-partitions_test.csv',\n",
    "                             img_dir=img_folder,\n",
    "                             transform=transform)\n",
    "\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset,\n",
    "                          batch_size=BATCH_SIZE,\n",
    "                          shuffle=True,\n",
    "                          num_workers=0)\n",
    "\n",
    "valid_loader = DataLoader(dataset=valid_dataset,\n",
    "                          batch_size=BATCH_SIZE,\n",
    "                          shuffle=False,\n",
    "                          num_workers=0)\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset,\n",
    "                         batch_size=BATCH_SIZE,\n",
    "                         shuffle=False,\n",
    "                         num_workers=0)\n",
    "\n",
    "# AUGMENTATION\n",
    "\n",
    "aug_folder = os.path.join(data_folder, \"aug_old\")\n",
    "aug_train_dataset = CelebaDataset(csv_path='celeba-old-augmentations_train.csv', # male\n",
    "                              img_dir=aug_folder,\n",
    "                              transform=transform)\n",
    "\n",
    "aug_train_loader = DataLoader(dataset=aug_train_dataset,\n",
    "                          batch_size=BATCH_SIZE,\n",
    "                          shuffle=True,\n",
    "                          num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FCBisEyQgZiDYc2FOMBIRkCjzBPnclfkSKHsVmbTh0l/ikMIYxxH39w948+YNbm5ucDwespqCSmWRdSrSycjSPMHyRCm2fjrjnUgELE17cj9RTFGcNCiJwEl+P3vmxbjX75cT3/gQG++6PYYATnfr+TmKkMFJWyKBc9/JNebf1QIAAGIEUkiI8QhWFSCEgI++/gE2m3V1HTo5h431iTqzVAf0M0YhXC3WwRZ9VvX4NwkSMB0N6gBTa0cEI0FMPCcQOxBTWRzGRysRUkxgMgkSR6RkJkHbuQik1xWSBiCFWqLLq2lSpZPAN7Pvy705BhgieGwhJWY44sq0hrxdqF+N3rfspiZ0xOvU4gQE6jEAB687BtSZJCJMAcwO4xjFBfjNW9ze3ONwGMBM2XVYJoY55HC5D1ro0iliSiVQaq4D2yNUnmlU3GCdc9IHOj6JWT0hKXMbOY4CZUxOFzry+eXY093ciNflBlBQi31u3nr1iq5dac/PwuXnModOj8mLtrqXWbwbQWMOJqT0gOE4oet6EHls1j1c24BZPDApz10HdpVuwSKcSdeFLGrtM3IqcLVvVRjImBcOxjuHeQ+ftvdCCBAYjqMsJDhxDoJB6bKjsRJylBLgEhw12ckC0F2TAEbKUlKXE1IKCGJjACpoJzs0KemWACPHHME1LSyEwQQQqdnPBt6i8uQWEkJKQCpmPl9OAAdG17R5gaVkC10XF8lgKm8Hhjh6MBFIdXIwY5wmjMMIZo/hGHHz9h43N/cYhgkSuFJ8yK1vzJRp95IwjwsuE2hus1+aSgHAuRHCDfjsTVkz256kT2IMKpBSJi7NrCoIpYbd9W5dL/5yj7UAMAGfhXx29S2zat6WQmL+7RktdHbcue/z4tfLscZOMWRzAEQQHA8R03BA27wGJw//0dewXvUgTkhTQIgBlBpQS4WTgjia2TiWbU6RmI6HSwkRETFNamYWwjaTmU5MiU9pRO+FEJAmwNd0aAuUqHek2QSFkCo2EctZ5kw2zyaU2FX9wtRDleRd3otTJ4xTtYIql1YRIDUFURh/W/AJzjs4ArxvQOQQU9QIveJzEDUOIbG+ZwCJZnbpcZwwjBOGwwEP93vc3t5lE6ARjbVX5HJxS18ser/S++vPcj9WEFusEsXt17wUay+3QkiZ663DOI7VfVgfL2G9KUDWj8jPdP5ei0qGiu1fLtp6MZvKea7NP+ZHPl/0Xz136ERxEUEfI27vHkDOw3kJBb68WKNpGoj3YK12JXX3ZVjouCOXg4TOXdauaufJbtmquj0m5ID3SAgsSZ6Z3/SCtJtbCBaCgmOl80pbklpLh5vleYxlFTdO8fWuJ15mZ7mQbYU9X3AHlW8AWPRzGXgHRLPzxxyvH2KSnX4U33PTR2zRhRA1Vn3A7e0D3ry+xf39PYZhAHBqjjtvAjtd6PYbs+fX51j2ozXjVmq//xK96dB17czrzXuPaZpmY/hYq8ePiNCqmas2L54+Q2HCvwzBe17358Vxj3MZS57ABJH9nzA/33EYcHt7C1XqgA+/hsvLC7Sth3Ns8A/MZiFKeS6Smy/5rBrrxYywrueomBZF6DzVH++HEKgWTr1Q68CdJam2JJHqhUY4dUstUFYcM+x8S3a6dgOem2709NbJUXasGg3YJCjH1/pzCQSxHSspPC52/qR+/DLpowYMTVPA8ThgHCccj0fsdnuJCLzf4eFun2MGlqhpucDt/h8fhvNkW/3s597bvw2tyAJmhFAEao1G6n49R9DWx9i564AyO6eNoagZovoJylrmGTi3sEt8xfLvsj/q+1uSzPNzAlkImE6v52XILj4MI968eYswjYgx4Otf/wDPn11XPixzFYlZ+BVDtDWJTih6P7ywR0tCvJ73j7X3QggQCuO8FAA13D+3o5+bsOcWbv15ycATT3Y9g+71wi0Lm1FnbREzGKrfS9BIfa9y7cpioNedpoBJF78cV2LEhXAjhOOA/X6P7XaH7XaH/X6P3W6P4ThiHCfEIH4HOXsQ5u6sNeP+1K67PK5GSGfH64nP65f1rQm5EEoCFLuGPf8yYKZGbBYWvXyu+aIVMrFEiJZjT4XB4ybFpepTP+9pf2qAW3bBroU/kHdu/WPPMwwT3tzcIXv7AHj+7Bpd14hLNyNT47WGyikhqTAQIloIaZComcmRbgbzezaU+lh7P4QAlfDeZeTUfDHOf7M8FoA6Cp3fvc9NXvtu6appk0isBCYIGEADImPRIbA+D1i1y+sOZfdpobgpJYQpYBxGxGSWAfERsKjE7cMDdrsdjsOA7W6PhwcJ/DkeD+L7H1VXhAeYTgTZud3vqV38XN8+9t2539dtLsQpIwTJkhQhEXLFact+Y77zpkbZe4PFNZpYogr5vdxTHapsf5eL+bF2Kljmz7sUqPZ8FkcBQwMs/1Ct0c5SVEYn+v7d/QN8YwiS8ezZFbq+k+xYJJyS5RAAxOpEdi9OSHRSlRPkVB2TGyjzWSwQT7XfsBAgoh8H8K8A+Dpke/yTzPy/I6J/CsA/CuALPfSPseQWeOpcaNt2pp8vBYEJinqQlkKAWcKOmeZEiEXZLRfJ0pfddq9653AWr62IwKtD0GxRGcs/W2hzgoZIjJEpCAqIMemkEfQwTQGH/RGHYcJnn3+ON6/fICVGSAmTcgQxJDW9VURSdd136f9P9b8erc+9/B4ATOcsnnTMgMSzI0/AAj8JTWNmylOiz9Qn8bD0M/7AOafBN07Rgtjal+7GhiqWG0FNii77Qf4Kx2LP+S6heO489bwEWWCS9NOcsSuSQXIzMrwjOO8QQ8T9/YPMLycL+4quADJuQ7kCPZnFEADics4wxOpBjuES1AxunrVqFkZ6ch78IEggAPgnmfk/IaIrAP8xEf0F/e5fYOZ/9sue6NwCn3Xyoi0XdI0ULFgiC4XKRGat9tqrYeOSe4gxwrvT5CIFCtasfkmsuZyQ2Ude8/ZFDTkl78GJMY4THu63uLm5xcNuj5vbW+x2e00o0sju6RiBEpjVzBZlIme7MVAtwFOznvXp8v1SMM6DomqeQ/4WdQf5rzDY5uYsf1XJy5YKyXlQn1tO0DQNuq6bLfpaENj9D8M4U9/mpsmS1KTui1oI12Muz3NeR67RQI0O63OfICfn1Axn1yaB76YWWN8hR1yBBLBiGAJubx8qwSdZnNquRdNIYhhAktZKvkpxCEpqkpYoUla1wKtws/tzkKQ3AT8SIcDM3wPwPX3/QES/CEk1/v03KmatpaS1thzIc6RSvcOcO4cdf26nqL+vmzHyxQvQUAoQQk1MiVVinvJMriEZiBJiiAiTqBJN08A7jzFMOOyPePPmLb73vU9xc3ePtutweXkFgBASYxiOmKaAcQwYR039RZwXpt37OaJL+spVaOZUwBoCiidhp7ZgZkOVX1wR1PX1RDgSiEKG9d77GV9h127bFqvVKqO1epcvlpeUcyUAJfa+67osOGoBUS/aIgTqseW8W9rz1y1vKJUQOBcKnfvSOgbaH2y4YD7/2rYFpyjzILKkaGMRBPf3O6zXd+j7NZqmFVOickP5maqNxp5BBGsS1fDMpnnunpfth8IJENFPAvivAvhLAH43gD9MRP8wgL8CQQs3T5+hDGIN86zVpqhahwcq3d12aU8ZYte/WQoRMzkVnR35u7pZII90vtNdzzLOpgzLRXVQboCRdz1DDSZMHBzW6zVSShLv//oNXr9+g7dvb7Db7bFarbDZXKBpWhyPIw77Pfb7g6avtgVSdmBwhC3UJSx+dNIuEAGR/Z5A1MxY/pqNF2FWp/wqHn+1gDTrQPbSlH/JcU7i57uuQ0oJfdehX3UAS4q5KUySrw8JUxgBAKP63zdNSbm2XPz2nLUvQj1PLJPPOXXn1IfCHK1ON4slScjMM+RvfMA5Ik5URsp1JNSggQTGMIy4ub3ThS+JRbzzEP1eSMAYJW9Bv2pRXINRjRNAXAfIOeVj4tn7sfYDCwEiugTwZwH8z5j5noj+RQB/XK/6xwH8cwD+4Jnf5boDFxfrE1iuxwA4lezLHa8WGp484MqxdbjqCdQ/4wO/bDEJBK93BD066+ZF4qsAiBb8OycjGyf+AdMUsN3u8N1PvosvvniD3W6HcRyxWq1wcXUN5zzGccQ4TTgeBgzDgGmSxW47ugw0wEndm+m8taTe1R6zptTHmh4v/y6klLkdm7CQ/prr+CYQnC50wHzbBSFRL4K3bRt0XYsYI5pWdjtzfU0pgNHAkrcyy1hJXYUm54wUp6mxoDDMOZ16l6/VvDlvc548rbmCx5DpyfFkHA0yCgCVcxEYMZi66MCIOb2bY0n+cTgccXd3j83mApv1Bl3Xoc5IbXkGbQyUD9RwdNsQa6/MkvD0qfYDCQEiaiEC4F9l5v+Tdsxn1ff/EoB/59xvuao78LWvPeclPF3uVvVCXkJKPZ/8VfZ1qcc9dq53NWZJY77ULWuiquiYtQnL7oYl/ZMX6R6niNvbW3zvk8/w2eef43g8gpzD1dU1+n4F33YYhhGHw4DhOGCcptkglmc2dDKH98udyn6zNLfmZ8v3KyqOHBdzgRFr4gFo2ZQpT7KaVIwxZcEq8N4SsyT0fZ+dhUxFWI6H9a8t7IwuiOBVCNQI8dxY2YI/mRcnKEBX7pn2hJyc3bO1xOIqbo4+5eKn75ltDCUxi32WkhCgx+OIh4cdLi/3WG828N4hRREiznt0rhOB6gi+McEuTnKy4EP2QgUoOxw91X4Q6wAB+JcB/CIz//PV599QvgAA/j4Af+2d50KB5LXXWa2T1Z5stUpwogcjzbLvPNXqhXL+e6hkL/C/FgK1TmwqiJXsMohNVdIRJOBwGPD6i7f49re/jSlKPsDN5gIvXrxADAmHccLhcMBuu8PR/AGi7UYALNFIRLnGmV3qbD8TzaB0vfDMDFoTbXVfl4X9uGA11QeAxmiUe5LqPB2GYThBeplc1cU/jmN+JhOeslDmPgKCXIopkZnzPZ7jB961GB7b6R9r+fvEOetPdbb5sflT6yMnECAqekhACAnDOOL+fouLzQOuLi/Rda2onwx0jUfbipux99Y3xQwbQ8Q4BUxjXHAgdt3z7QdBAr8bwD8E4K8S0X+qn/0xAL+fiH6nPumvAfjHv8zJ6olx7lUPMHAeyqWUwIGrbD2nFoZzZNBjJKF9LBkKXF6A5xZBHlik2YRsfVEB9tsDPvv0c3z7298BM9B3PV68eIGrq+tMPj7cv8Xd7QOOxwHTNE8QKRlsa4j5OPm5nPiPeVsWi4dkM2pbmRJWBm5J1NlnSw80UzfM30OIUp6pId579H2ffztNpdyccCYhX8/OKb8DpjABXPsFFNOg3ZP9rfvivAA4He/HhOiXQ4v6dykICkgCqHIsAwmxyxLbIgIOQGQMQwBhh5ubO1xeXWK17rFatXmsRegJqViPWwxBHNCmCdMUYXuV983JWC3bD2Id+H/iXG9+H7UGclvs+vXiXcLXGhHITxfx4ynNJsvppU5TXZ9TPex88B7gOge8O1lEggCKrBc9NpVdNzEeHrb47NPP8en3PsPhcEDbtvjggw+xWq8RgvgJ3Nzc4vbmDsNoUXoNWBeBSPKClkrVozl3kvuhem/FMGqexNBAYdkF6oseWhKg2jms3+z3Rqzaom2aBuv1GkSk3oETmtbBog2Nze66JsdOpBRxPE6wBCoxThC1wgq7JMTo0LYSLWrcy9KlWDwwp5nn5DlBPVelTvvs3O+W82PZv0C9y+tWYEo6NA7dTIZmXs3H6gZntGkS1WIYJmy3O9ze3OHyYoOmuRS1IElVK9d4NI04Y9XEd0mBb/dpY/ebIJ8AASdQc+ksBMwH4zG9DyQM9PJ7Amk+tzncrdu5CZDIg2BOLL6qPFsII5FJxX5dTzCDtzc3N3jz9g0Ohz2apsHz58+xXm8ASAGLuzvxEpymkGeV6MO+EE2wXacQkcApMjldAFDBdGqiKwkuxee+acWjzzcO0+RhjjDyuSzmECdBDATQJORp03r0fafXlzh36TNS5yoxZzVNXxWQReU2XZJiSD+WbVS4Cpf5iHo+1AvgMRRgx1Y9kvvTjl3q+aXvTufesp91jRfvPjKhXQmC4jVgT1UGB4VQ5AREjjgcDri7u8Pl5Rpd7+H9Cs4xpsCSAcr5avzNVChnFeetucXoqfZeCAFDAsB8Ai95gXMPs9wF7bMTPZnm7qn2m3MTZUawuQbghfeinZDKwpfCEkWAASLUjscjHh4ecHNzg+FwRNf12Gw2eP78BYgIh+OAw+GAh4cHYcsbDw5QN1sJ2zXysUh4u+f5xDrftUbknVpYalUhsXj+iYOKCIOmCSD1R+j7TmobeoeYAprWquYCPiVNqyZ4uOtbOA/MicPT8arVDWvmNDbnDebPXv++ViGeGs/5Aq/37jmnsTx29jta/DwfI9/lv9bvhgIASRdOczEgWgFB9Ne5B+OkKcrevr3BatOi6wjetZgcoe0c2iQZoqKmOZc+SLIJUskm9WXa+yEEgBMhsFQLalKrVhVqdpm55GE/R5bZb5ZBJksnpVqPTcHnHWh+Mp6RLaKmxAyxnXOYphHb7RZv377F/f09EjOur67x4sULONdoiuojDocjnHM4Ho9o2h5BIwfFE5FRtJ/5bvbYOC/VG3mmggDKDspq+892rey5BgBtK/ZlIim73bYtmqaZ9Vc9BlMQ5t9KdB8OeyR1ezY1wyot27MvVTu7tjkX2echxtkiyWTYwk9gGUdRL+q5idCd9NOy72qC8cnGAJKud87rPn+lJytz1a5ZIT6rYckowWTjMOL27g6biw4Xmw5d60ATYxwtizWQLAp1smI4QPHYLM/xVHsvhICRHtZqAYDqu5oUWgqBPMCuZP61355b4PnaeaechxDn49hpBdq68XIzUDQgE9yKlk6TRAHe3t5iv99j1a/zYmIm7A8HTFOorBwuR9uZl1uaCQHrFxOE8tnZPj0jCM/p03mDonJ+0dlpthD7vsD4EELOX2DqlcQ3jHDOoe+lAs9+v5txCESEcRQHoGEYshAw9Wx5beN/vHMINPf9CEFIsJq4XO7iyzklv7dP5kTpkoMiWhQMfdemyvO/5TaMAahPYpmvTG2oLB56mAi+gO12i4eHHsfjM1xdbUSojiMIYtExIZCCBSjpYOp8cg5gfloleC+EgITPGgSkTPSIrRqwyDOxHSeUoArz5itebHkR+yrtWBLo1XiHxjt4R2ibRs6g1+JkfvMODozGezTeg30Cx5SrHVtdN6r+n6DZXoHs/z1OE+4fdvjizQ0Se7x4+SHWmzXWmwuMWh78MByxHw4SIrzfYRgmNUcan5ByUc88hKYGVK7Ac/hqNv15NJ2x/laARApimnPJBEKLrumBBLS+Rdu2ItBcg3W/lgo6keHJy7/V/bnvVDjAYTyOSCGBmLDqe1yuL3B3d4c4BUzHCamLIHIYhwFhnNBSA9dI1hxzAnKaOoucA5OqCyEhhUl2UwCeAN94tJ6QYkLQfAIxpUzOGVIgokIBOKf1IlSbzghSRtH6y8hS733mLGoVarmzersmREOU3T6quqiCOI8jz7eQrCVUqiZb5SBxz3zYHvH25g4XFxd4+fIZQpwQj4y2kfTxKUmtSs6eSpTXhVRd+hE6C/1wW2WS055IuYaaSDUZCGPEDeacRh7WiAEAkunuuZO5oAkio1Fhcp+I4J28wCT5+NSYy45LB3MZUHKAVxNeiAHHYcD+cBQbPzn0qzVWqw1AhGE4Yrff43A8SmmwccA4jQh55ymVjIihzzvfvW2hm/AzGG0ooqhXSSG+7LDTVHwxnFZ6jlH06sZL8Ip3XioPe6lAnDhh+7DFNE2yW/sm173zzqNtWjhymMYpW0OICet+jZ3bIXBAnALG4yioYZyQoiCmru2yClL7iuSlykCYJpB6HtpYsf4lSOr2pHOCIKb3PKsyl6Rj7ygLExMAdXovQyE2D2q+4DHikFGSfOTpCp5dG0AWULbbL88DvUuC1S6U+xLfgR0etntcXl2KystafMbMxvnC84xJQggXwvVce4+EwHlSsP7uHOtdkztLAbD0pa/5A/t37Xtg1zslKfXc3oksYHMZZg0jhrD4jnP+v91uh+NxQNf1GSYLmhEofTgccDgcMI6jllwv9ycmJkteakKgLJAa2nnv0bZtNs/Z+axZf6xWq5nqU2fqEUeTMBM01ndG1B38YWaCs+PsXG3bZh09hIApCA9gcRIAMnw3IWT8gakZtnvbuQOHbA51jsAOsLJrJuxMEDNKuS3x3eTcpzJ+qsSpEC0WGJydI8vkLEuysSaqn3LEWbanFqMhAaqtPyx1DPb7nVoLNri8XEutSi6KqfEKp002yqdogfdCCGTSdUEKml26/i7/ZiZOHxcE5/Ti+n2thy5dThPL7i+zSdQSAQTSseYdyAzAF6g4jgMeHh4wDAOsztxqtULbthiGY9aHh+GoHoHGkJfnXzZRVUpEn9jQJZhJqhg32dzXdV1+Plukpqd3XZfjJoyM3O/3+diabDOIXpOds/6pnHS6rsPFxUUWbOM4YnN9LUKAGVMV2GP3akLCyMaa1LO+DCHAEaH1mmiz4jTMMjCfFwQHzdRbOi/PEiY2+v6EGyqHl8CkJXH5VHsXAfelW0a6Ms4hJOz2e9ze3uDycoO+b9A2Pi/sLIwkC/ninoDHeCNr74UQEAgz3+lrJHCeoT1dzPX7+rWEd8vBX0YszvS/FDNxKWqCA1OJJARFcDKmWnat43HAdrvN/tvr9RoXFxcAxDVUQoJrH2/rA0vYkfLuUuZdiR4EbKLWtRXK4gIKmrEFb556XdfNQnJtoYeKZDPHmxoR1ajASrubx9/xeKyyAgmyGIaE4ziAvEPbtcp1FH276zq0TYOm0zwCQRYnaRk3eQiJuCNPQJRx8I3wGSE4jGNFpDLnEvJm8lR8N581eYrRo5B8iQSsP5eE9ZOL3rSZxZw+pZRPkUSt7rLC/mkM2G73eHh4wLNnV1j1fQ4ckt8YbFyuo5qkPN/eEyFwCvdri0B9zDmBcI71zibDBfO7VBHs94+ltE5KNjkllcwECcju6JWnkISgEx4etri9vUWYAppWbOsXFxdwzmG73WK32+XdstQJFHfjQlJB9X6XP6ufo+ivyDu0LXRb1ESEYRiyr75XotP6YZkmfByG2WyxxV478xhissVcR/Mdj8eZeS+ECff3d7mvnDPzY4O+b8UL0Fu8vHIr6hNfVqdQbgTGlFLmAjhJkg3kUq3G/Zl2bs9QOJHlvMLsuNNVcg6ZPMYL1OfmoqDPDQNfslW0B9T5FVbHdpoCdrsDDocj1quVZGPKJnFNTkDCcZTm8Fj5NGvviRAoMHima1WLd2nrPWerXkrp2i5em/2WsK5e9Evdb8kiEwvRR9lqwWCN4BrHCbe3t7i7u4Nv2koN6LDfH/D27Q3u7u6x3+8wDENGCvX15JrFfGSLyI6p0RERtMxUAoHRtQ0uLjbo+x4AYbfbgbPKIpaPxvu8iAgsu3LbYGwbxCkgxoDGeyFKw1T6OwZ436DR2glt4xG8x5QSYpgwHI+5Pp53DgGMw2GPrusyQjEkYjyAjE0hePPzz0hfzRCtAicXMQniHOOIQJXfR9B+ocSgVNnkz0L5uYt4TQQuF/vji19HqhYQi0vVc+qd/IEiGlMzOdv9gRQTjocBu90BF5sNGu/hnVSBzhwCLXMgvDu47D0RAqcLccn018ctw3qXAqA+nwmC83r20se+qAH5nOKDmX8jVWArYtF5JM1fcDwOeFAWfbXe5PsPQeIC3rx5i91um1FAkeAo5/Om182Fn/jgl/LqMca8+FMMiGECp4jGO6x6MW+BI8Ik/EOcRjF9Ng0aT5qeTMym3nt0jUcMEcMwoPGSLiulhGSwOFsPALAkUGm8AycjFidMio4cERpHGNnSgLvsaNS2LdquK+WxktQutF3MWPW63DoYaLsWQYWUjZEnp/kISjnlYRyzuTCrdnljwOzvcvMpc6nMEaL6uJMZVHFTPxw+gPVCWSUod6Kq5lEiTC8vsep7NL4BNdXcXyx4UuJz+Xnd3gshIBDoNOqt1ukN4tYx50vSxhZ7TSbaeczxxDpr6Wf+mGBx5AAraCEHL+5c2jQFHA57MAOXF1doW9G993spD/7mzRvc399XrHY5B6HOh+hgziK18KtdnjNxFQNYC5fs92XX9d5nwq3rOiUhB8QYs5XAntueVXb5JvfL0pOyFqpLxyr7fBzHfI/kHfpVh65rxQW5lTBYKc1WCmWEJDHwicu1YoyYwpSzBHnn0LdrgIULsLmQEYZvwChqSogBIabMuRhHkCs9Z3LVmHPxtPO+QOnlmjIVrG41X/N94/7Hmm7/pJWvzWFMkswCx+OAvaqTKRVlyNQBxrsJzGV7L4QA8DjcsolWewnaYp67gVY6Gc7v8vabJeSrBYudx4SPOa+YrqdnBNQhg3Vymdlv1fe4vLrG/njUYiGCDLbbXUW2zVUAmYsES5XetIXpt2PMyiDehkAIE2IQBCCZhyZxTU7i/HR9fZ09/sxFV64tBTpM16/5gcaXegylLw3uFsLMXpz5EolNsEhFZgk4shyA9XjwQoBL4tWQST27fh0TQCBwjOCYQCzZepumRdd2aHI0Y8LEhNWqR4wtxmmCd1EdiVQYqPNRbTl4l45v6ti548pao8zbPKVGPDXH59+r/4qHCi8gkvwlChiOI4YxIEV1XWenQqCol8tneAqnvB9C4JwuvtDtbQGds/2fusLSbFevkUHNYJ/zAqvPHWOEVyjFOhgMyVdg8GyaAnb7A25v7zT+n3B//4CHnRQLkbwAsqtZXgJSR/N53geZbPasdn1yhK5rsVr12Fxs0PcrZd8HhHFAio1OfJns4qobMU0DVqsVAELTOPR9q5lrjigReoTDQe38TYNV18OBEVLIXnhGioIgPIIj9F0DcARHgIln0XNsTlWRkUg0YN+2aJtG+pLleKkcHQETJDpmUYUCUkLXNPCNem5OxbzoMlpDNWaAI1MlxWOROxHQUwgY1ZIBAJQSEigLO8uaZHOk8BGmGljxVCNpUV3fFn7JeTE/T0nRvkzzZVC9Vj/tc7uHCgzC6KGHhx1u3t7g+fU1nj17JqHh3qFxHq51YPaISYQ0lId4Chu8F0KAMYf/S91+qdcvnXlq3qDezZd27aXevyQY7Xd2LJEsVMcVLIMQTkQOQXXo3W6H7fZBfeElPfbucJQCIzFqhJ4XhyI25t8mxymZJNdNOVehJdnsV73q1wKrY9+CQwCIMI0jDsejCBxl+qdpEjSh5+QUJTU6Ua5gwykiRTEw56DXlDJ5GCt1CsyA91IJmgzSyy6TqEB8TfoOhwQnhdXF1ZcI5IRLICJwlGMJyvjHmAlLce92cI1wDFNlqssoMIk7cWIJZ56mCcM0gZxD07RoG0nb7RsvnELQYJsYME4BIVh8wFyVLItSZqdMj/NqAmUTXFEJlvO3HLOY9/q7OXwX87Odzawn5TeEcZyw3e2x2x0QQhQVhjnzRoCDZ3FMM37pqfaD5hj8NQAPACKAwMz/dSJ6CeDfBPCTkMxC/wC/K9twRcZV514ccmruW9r/z9zf2fPU5zL14tz3AMQjELJgY0oIKYLIg4hzwZCH+wfs9wcc9kekJKXDwzQp4aW8giNYvTzJKDuHyUvPNE5SldhKlqVUMsZuNqLvN9SB1N02qK/74XDIUHpQFaBOBmLPxkp4Ju2HGAKGlOBdKQdWo7Ba/TJfhMf6WghTW+wAQWC8I8jnlu+BWawtnMAxgGMEsZCVzrkskEJKSBwFRRnkQKU2JNH/Jw2/phw3UtQqEEkV6ClinEYQDZm0rdXGpX5feINH5m7pgcVvSr88JQjqlo8DsiCVL4ogEBWUMA4B24cddts9Li43SObTptxA3zUIQTaqpQPRsv0wkMB/m5lfV//+owD+fWb+E0T0R/Xff+TLnqwmxM6Z8+wY+97+/RgxWP+tf7sUAEvEQDoaiVMm7lKUACJHspinccJ2u8Xd/T0OGhGohj00reSOj1HKjolrsOjjdU6DlCw5ZLHJi/OLoR+532kK2O8Pqn8HMbW1LbpFCm5mztaH+nnsmR/TS+V3E9qGZ44/1kfWr3O+YA5n6/HwjmTnJ2Q4SkqCkpJswimIehCVcxGrQ6OZdATNxEn032ITByJYSrfnmg7Sd5vNRojkpkHTttlvQjqRERFAkEAv0DyCdJk2bYkSH2tzbuB8DgVb4I/1vR0na79Ch/nylZBJwHAcRe182OLi4kKQqsqN/HxggMXd+qn2o1AHfh+Av1vf/2kA/wHeJQSohI4uF369qGu/8nox17yA7XzLRBN2rP2tr0NEs+PtGiCxzTqq/BAggoETKlVgJ+wt66K2AanRvl7TbOQ2MctOFPIiAwGNt6SSItmdIy1hXeIi5D4tqMqpB6DVDSjw2bmStUmeGzCyL/cFUMyCqqcbOUoEjUYDUKlnVpAlLzIuzlSOgMYnOOezc1AREgxPhLYxjoQBbuCd2Pgbr65wKSmKSNlnIi8ouWB+xqZp4JgxTSOmSfNGtC1iJy7NroqVaJpGAsF0ji3nylPC8vtty7n8rsZgkQFnBUbhckKI2O+P2O72orIlJ+pRrE2cdcTij85EyAD+PRK7yv+BJY34R6zZhpn5e0T04dnHqeoOXF6uZwLg3K5c/31MWNgCWVoFHnMaAuZ1CmtoaEEuKSWAxDHIricx3UKyiblmyPcRQsAwTYhJdz2yAJ412rZF1/Ww/HrmnltH/nnvRW9uXHaoscjJ4nIr5/GEvKsKM6yYEOYcZFuJkW/QZJjqjpQpcr1208jvU0SCBDJJNGUzs56AE7wufvbQa5pfhVpWiOFJ4g9W67WEbqtnpScnFXVB8CBEcmi9zzEZsqMKd9E4D2oYkcsmYWPqycE1ACqREJMlH7GIUROGBOc0lVqMQDXWVvegTE59JiM7mat+Xi6AxYpdqLS16vllkcX8jJQ/ZCCXKU/Kgex3e4zDBKIWMVpIPTTfgnEC6k34SPtBhcDvZuZPdKH/BSL6G1/2h1zVHfjww5e6ycwX/3LHt+/rCjn2HXCa8bbWg63VKOAxJFBbFZjNJ12j1ZgRpojj8YDdbofhOMjEVs+trFbqoMluW4KIuk7Y/cNhUrfeY9a5s9tv4zIBaOY3VE43Av0FeThVWQDJMgNmOC4e88SQlNimg6OwzKVfGN4Ju1w/Pykisf4hFAK35g3OEbSeEjrn0fUdNn2PppHY/ImDFOR0EhA0gcHezXxFBN5HOC+EYvIekRrtX66Srij7TYVfcYk0G5PxGRNAQIgmUGVcnJcUakSMmNR0bOHbUfsmmjxIGky2cAuetfM77ZLnekoIZIRDpt/XxKONlI2bVITa7yUfhW82kMzCYk62wizMam58ov1AQoCZP9G/nxPRnwPwuwB8Rlp7gIi+AeDz38B5Acyh/nLSnYshyLBwYUo8xx3Uv6kZ53q3Fx1NVkwOrlGb+8PDFtvtVnPkI9u5vffoySGh9kWwaxYLhvjbDxqjD3Rdh81mg9Vqpfn9fBYCRRiyomSZ3HJqN9thyPpBPQtt4nEWAHovFapqvBBorS+OSPYshkYs0s/6oS4gatev0ZlHQt8AXdui14rDls+h7Og0K6ha6+aRCKzVnxMDE1N2CLJjcyPL8ByVhBWVw5p5PcJIRXJwGrfAWsiD2RUmnmx+WX8DlFEl8iLNAl/7/ZzS/xg/sGwZ6dbqwFItYGgfAPAETozD4YD7+3us1i2ahhA1T6RwUEkT1c5T4S3bD1J85AKAYylGegHg9wL4pwH82wD+AIA/oX//rS95vpPd2fTNOjdA7Wiy9GirhcUS9ts5l60+5kTFSEX6WzLHMUzY7/dZCIj/gpdy48oLkCN4sl2VK3TCOB4POB7FldcQQNtKuu71eq3FOR0sYWSNiERdcFUI7dyvIC9Av9zR55xJ3c8S1KNBRzo3a4+8mquxPk8pZUeg2nuz1tk9Ejat5gzQ6kGSranJfgcxRIXvQhImEtQj5GAJ6IrMSKP6dVS8T+ZFwAih4kAIANx8wyZlzEgMoXUp+fq+z20eNr9kHL6MpWA+375fjqHIgDOLtzpNSozDQRLZvnh5ja5rkLzkvRiPEd6/O24A+MGQwEcA/pxepAHwrzHz/5WI/jKAP0NEfwjAtwD8/e86EYEy7KxfzqmtWL8zSIua8Kt2QIG79hJzVDL7tn6PJHmCRH91OfUYQXYlR0463hhsThoqLCa7w+GIw1Gi88YpgFkm7jSaBUAFV0PqhSfXadSd9XgYxL89TFWugR59v4LzDaQc1YgQJ1hRCU4J3ovTUN9LCG6n8N2Tw0TF8QVESASAHWL23fdFV3dlF4QJLA1EQYLo6q70vaGBphHvSYOWTdNkh59ZXL/q0B6MlV5T4hMI8A24bTRdtsD60UKYiUQIuAQpYspFZUlJHHyShG0TNLWWchuFB1BHnTzUrGZW3SxUCDgv/IdHtdBJOBObP5JyDhk9ieXC5WKi50KCl3qCgI+C4Gorwbtkgn2fzjAEDEWDMQKOsd3tMIWozmuk1akszpjy/T/WfpDiI78K4O848/kbAL/n+zmXI0KviSjNiSW/t8XKYqcXZz0q8eK2+5H4Wos9WsgTR6JTZj1K9Xtx9iGQF1lrOfI9ebSexUknSCcHlOSUiSFEoBJ/zA4MxjgeMQxG7glyWfVC4AFSPWcY9gijusKmKPbytsNqvRKSr2nUnBizABChlkRwJfGzb5jR9oS+b7HuV2h8g6lpMIVQJic3iI3HFIQ7MCgOUvfj1tKDyRRzBBEyzSonSTWqzRGhbTstGqLWCa2SK5m6IlJqsvNO9mZnho+sfEUCke2yAr9BhOQbNJQwBRO4oudOUd19NQIyRQ0dTuJv0DgCkZdKvSnBRVmgqWmyyTBoP8cQMAURUIIgxERJKSr/0si9JAk6apwT0yazIkEVRgQwOU1lJuNCNq9kdqAmKAFUtn5DXfopi7qy0GiKCmMkYD6HoRU7LzDFiASg7Rvs9gcM44RVkHkyjhHOtzIfdGN7Qht4PzwGiUR3lPdzNGCfJbY48uJmiup782hjTkiRtRIR5x0+Cw2F9kJMkcXrwJtzimN45zFBko+Sl90mm/6S6a+sO2DCNJUCmrJztlXsfcxRfiFM2d+764Q1X63X8KpvT5OmzYoRzkF2/MaDWEN/OcriIkbrvfgJtOIvENXDzzwMZYcWYSKCgYsQaBo175UwXe9btE6SfTSaj9CUXnErlh3UzIjea6ZhZ+ei3P+sCzeNJeIvq236IpE8cK5DEyJissSgUUpz6XOABdlIHIcSr1QvNt0w1IQ5QVUjW2ELctnuM0wBbdOg9R6u8eJoNEbNU2D1D4CgnIt4Wuqy5CglxKrt9R2bbaV65F/AublqUb8/QfFUixNoWjshLKdJ6leu12us+g6JGY3zsLoY71JF3gshACq25toisLz5WodbWgZsosUQkczPW6G+ED5zAs1+W/MORg7W1/PUIFEUr7XEGso75Qqypg8bbLbCm0RWG3480dnbpsFqvcZ6s0HTdkI4TpPYuMcJzFHKevUdNus1vANSmBDDBFImvyRBYXEp9p1OHLVksJT6NpUmWvZaVK6ohBxp6F0DJPG5X69WihaopFizc2r4MhGBlHjymT+w7M4aA2AhvmxkpuYCqDiOphEBFqaAyeJDgqiHkxePNxc8mNT+peM4KRozn4LEnOs1cMXYCaIskLoIJM5py1uyOeXhXIJLDHhL36WEcYxgyBw4r2e/M1PAbN4V0yGUv6m//zInQRbIITIeHh5wcXGBzWZdbaDKZ73DZfC9EAJZui12DeDUxHIOKdTscogJiJXeVWiDyrpQJyI5vfZy55KkDU52ABJHDcujJ0kwvULmDl3Oy5dy+i2LgRdB4dC0kluva1skiIfffrfD/nBAmAIAxqrvEftOiUOxlSUvD9V3rQTXEANpgnOtOBc1EltPjlSvl+1D0IgSa0lgtvjQT0CK4GhZhHth8/tOKg55ITMTC6oIWiG5abx660VMKYKd9A3zHEE03uL8zfIiY+iVqSfnVL/FbIwL6VfBYyJQ04DUUYY5wiXAa13DKUbEOJQx5XI+ygSfkX/iMxCj+HokZvTrlZKyTQ42EvVOvTaphOEsLUxPYu3H5nz1e+MJrB++jBDI3ECMcM5ju91hHIeMyiTMOObanE+190IIGNGxFABLZt9i6Z88V6qSVIBg4blzwTEfgDq5Zl3QQjpZA26cAxA0dv8giTos0o6KmdEIRHEFntQBKanK02PVr+DbBt6LCnAcRmx3O2y325x1uGl8JjeJoGSeBdOIEOj7Fg2J248nhmcJ1mkc4BuB+FkIsESW2ZMnFVDjJIFG4mnYoe/EOrHue/S9CLSm8WBijMOA4XjAOB6RYgRFgex2f40jNASYLwWRhERbSyw1H9qWsyBmIHMZwj8A0AWafTRiQnKA48IRsSM0TvI8tF0n43RMgAZayfWU/yHKScpsvKH9asFdPleOolyfQcabkNKQLRZ4RFUtvPN501/dlihiaf59dHqzreXCOBgSACRh7DiOBRHHiJgmVZ+flirvhRAwuHiuGWxceovV0V75NKb3546qOqw6D9HSH35eHtucUcAsYa2uuPgeDgdsHyRikNniuWWyBpgTS0RiM2UWZrhpG/SrFcgRYlQEoM4e4zhkAdBr6HDXNrK4TG93QOOED2h14Tm1bBAlEIudvHFilci1C5yY5drGZ/t+SglTkEnivEPjWy0W2goaaDt0fYuu7+Cd5Cs8Hjocjx2maRS+QQWcc+rv3wiNJQy+E1XKuJxqPFPudxX8zTyZJ7NEBQKMlDStGGt0IgsMtvwHTdvkoiOACEhT6zgxkpl9nVOLgY6PqYaslp8YYWm6mqaBV05A4L8GUHFxKDsXePb9mgJP53r5+y40IN9TnoOS8HXANE6IXQfnWIvbplwj4rH2XggB2z2AIimXtv7alrt0FqqlsgOByUKIi/SuBUBt87YJkPXVChWYnZ8ZWiDyQYM2HjAMUj7czFH1OQyJCLQ1m7akIiOIfXxQZ6Hj8YBpGsGsjkZdh/Wql9RRTQNPFlQjL+90V5Jg/hxU48ihaQidCpGua/O1vToetV2LVS8JKomcxkCIy7CkUROvR+8dWi/Hd32rzicjxkOPcdwogjliOOylmlAUrsJBrQaetI6Tl53cKDSF1JSS7sxaBNX8+iOEIEyyqxsX4cgsAjrOvpgvhSxkeCdcSduomdVrkdgEMTE7h8QFyjtnaC0gDcdcsapppEx7TEk5HvObcIg8iTXCuZPF/pQA+DKCoeYHagGQN7mTU5DOYVbrRtS6lnts1sU1ncqhj7b3QggAxVcfmOv+wLwT62Qh9bHlvervlXlmDoeWOQVOhcCMQCRBANvtFq9fv8X9/QMOhwEhRFVPhHiyKEG5gsYN2E5MJWJR/AtEag+WaxCQ3V0zCplVgVNATB6AhNE6SGSeI/ODALpWyDXL4b9arfJL0o0VRyuv8LltW8nxx7UjUgPnWgklVhTRUClTnroOU9eJedN7HI577LYP2D48YL/fY5oGURNYgog0E4KiMzWzpSSOPzEhaL+noHyFxfrrK5sJNRHHqpPybrZCWHfByOKH2bYtLjZAYkKYWcRqghAyXuq8ZepfGEeQk+AuITdJHblKlWTfJHiNMlw6AZ3A+Pr9l9HvF7yXeXeenKv+jZpxzVrFLI5o290OL55fA3BwWqrOvEcfa++FEKh39nqXtjYzFVaqwLK4iDjLILPUQlKpvzgv8+MV6Fnfx/Ll2w7DKF5Zb9++xXBUKJw0P4CGalqNv+ylRcY3AMwBKTlwZIw0YhhHzTbMaqcvgTdICWEYQHFCQ4zUSVCPB9AaGvAODREu1j2uLtdYrVe42FxgvV6LLt8JkugtLbX3WeCZMCDNY82omHG0aNoqrVn+DUBtg77vkKLkNmibC2xWPa6vLrHf73HY7bDbb3E8HDAOoto43yLnAFCWOoYIy/XnADRdByjZOoUR4zhhmrzq6cq5kwYhm48BJNnLNEm1p+QdVq7Dqu+xPwxqZaBsxTDHJ56RlKUq0zCOSMyadcmj6ySGA6FKbKrqRT0nH7NiZdbukbm+bMtNT3xEnl4zlNeDubgzhmHE4XBQL1UJp/Yq8J6SRe+FEADOMa7Ff3+pb51LJpLNhjRPPgL16CtqROELalNSHZsAFIFw1Ljtu7s7yRQ8lMQc4shSVAa5RysUCpgfrpBQhOjEG3EcR0zjmBny7P6axBQZwUBySH2LFCZQ6uA90DYOnXfYXKzQOI/ryw1ePLvCxWaDi6tLrFcbFQJtjliURJxVJiZZWbooGYqRVSXoNJa/5FU0uzm0ck9KEWEawWjQ92LF2Kx6DJs19vsNDseD5DwcJ3XlFXs6K8xvmlTCkiHRmQlSaktYe4+mMU5GdzzntECnoDomaOm3IqQiCzpoG59fU9MgaWrylAhuZgqWnAxd36M5HLA77LM1p9EcDSlh5qbuvIOLZyphYz4PM/rBfNGfNy3O10BRY8/AiTOCwXvCNAV0nXAnx4OggevrKzgvG0x7JglM3d4LIUCYFwYBMFuMtQCwxb4kBOtzLVuNNM4xsMVmXfLXm8DYbre4vb3DbrfHNAnjL5mFnOaET7m8uHOWu5BAxEZJZInNjhXCpSKAcmRaFGirsBUsdvgUJ4CjxOc7h65t8OLqCn3X4epyg2fXl9hs1ri8vMR6vcmRiJLV10yG4ghVchDMUZFMOwfnOzhSEyPZ1KshPQOcELyYBEOMcETwBLRNi9V6JTEUMWAcRhz2B8Rkpc3mlZ0kJZiFxEpWIS8EQc43mG3plkxExzdBf+sdOg0y4pDU/wBKpDbwPuT7kbVF8I3q91q1tG0aYL1G1HE082/bdbB4kZSSpogrc28+p+jk78zM/AOQhTMBU0wESKyh1E7U1a4T4TaOE7YPW9DHhK5tQRBX8afaeyEEQKfwahYZZ7u8tuJocQY9MGc/gSUSyJdb6GC0uL6E+h7UHLiTYJ8pqLlRcgUSaZwrXIUu9B7y5FCSh4rt2qmakJ+HGVH5BCLS1FglsWWc1MzDCc4Bq67B9eUFnl1fY3OxwnotDkUSgbhW+C+OO+L8Mg/AqidwneOQ1YwIqFXBIVs+OKcDF/XHOYtVmBCJNL9gUA9CWXRtIyTknG9JurCSCoeEYRjV1u1yYRPvCDFZXIcufjYyUdx+xS1ck4QSIZJlJ6rCprOgrVAQFxVhmqQUfNt1ePbsGXY7SQx7OB41XqVEl0oaszJ2dbj6bCov5uP3YzWozZeFTKDZ/408FBCnxVeorBnpV1HZPJoTAXWuvRdCwPSbmvlfCoHaHMjVQJ7CePNum3MC1sxCUF8rCwwgk0VWNfhhuxcb7DTqolFvbmZI6i+ZiJaIFCgvuQd1dKPi+mzPBJhVQfTfxgHkpcIxgdWRh3Wn9egbj3Xf4fJig5cvX2Cz6dF2otuuVxdKbPlZv4g1pBYCNRqovdYIKQkpVgQiA6SxFBoDwJDF5VzUawQk78DRI8agnINUOaJOipaEKPn/QpSddpom9ecPwm94B7RiAk0JxYRYmfHISnALeBLo7x1iVB+KlKTiECet32WxBlnM5TG3RWy+Hs55rDZrCF8gptvheIRv2iI0Y0Soap4XK4Ob5W6sx7YmhJco4V0roqzZavHOCCz7p5DQMp7ioh5CwDCMaFuvHMrT7b0QAkXClUQgtRDIiSo0YaZJ3zqcFDAdVhchmfSXhQqgEhzlPLJISkiu6YX2/pDt+FVJLptsLDuo7BbGtGcCuwgXU+nURl23mbOIU5KQNJuvcODYbNZ4dnWF68s1ri83WK96rPseq3WPrmtUt7X6fk2eoHJeV11nXvNAqBVLOuFlMZOYWbMqAAY7jcdXpxowK8lWhHSC/NaTExfbGEFqomJm8SpMDkwJEaLqgDU60oLHnM8hviEGUJSYeSERYxYClruBFT3ESIgk2Yyg7tJlvJFTu+f54Rht0yC2LcZxxG6/RwKj63pcXV1JlZ/9Xp2NijAlKj4qM3WKC8F8jsNaqriPCYI5Eqh37zM6rKkGqi6Za3hdZ2JzsUJD7p0GivdECHAWAOecgWr9Cig6u0G1uskkKmnHl0JgWfbL5Q6MuT5AdhYC1PYq6cPFyUQ3GoaqB0JulQG0HdfsvTpJku6mbDC8qCJeo+pa7zTm3v5KtZ7Neo3rqwtcX25wfXWpjkRePPpacZhpWvEHkDRoAIxNp4IAbDLPnp/sM4cIUQeUfgOgfuesi1Fdp1l3XXKSg8A4UAdBFGCbdnVqL/FpYI1CNKTS2BUJiL4IWZExAQlarYm8CgQTAgnsCTE6BFWfrD9L9KlxCXNVk1l8BxqNvhzGATFFXF0RVqs1gJVYb6qF7b1HWszRMiepjPMZ+P+YirD8rD5+jhygOwqyMFAQAAYrhyKkrakDw3AUZNa1v1mchU6TPS4lZb27LVWHfB5dbDE9LgTsuPqczBrbPo4ZBZjPgDn1hMki4vR81QRxGtoKmLohT1XfY8wlzmvHKCtqSjlnX+OdWAHaRkp4Ocaqb7HqO2zWK1xdXmDVd2hbOdard5vzjeYJqIuzFr3W/rqMBgqcFWuLAyWfvSukGcR2YIrgJKa5jCrIrDEMUtKTMhBLcEwC412SFGIsQTnee7TaB4kZ3mvZrSBzIKYiOFNGdA3YKXpLrH4ChKg+DTlrkQo770suChe58DJANvdl0yFDqzcrJ+IlwGscJVBMxvV8kRtBolE3hlPuaW76e8TLNauy5nOwJMgLL8B5XhdiWeaw5mFIrElIhcjmFb9TJfhBMgv9LKS+gLWfBvC/BPAcwD8K4Av9/I8x859/6ly2CO29nv/kVbPb9lrqY6ZOyDkM2NYprBjOpZk1oo5wMzVgGAZRA4ZBvysLl8iBvOwwlkJ8idYs4k5UGyHDLG7fmk1WK7TRKNvdNh5d12K96uE4ivtwI4Rh37UaAuu0sIaoAN55zTHnKgFQhFPZEW3nX2bocWBXiqPIMwBwCSk5JCKAIlyq8uyRlr0mkQdi0oOigwSPIhg9swgDR5lUIwiZZ9mFSMfPVLmaIKYUZIIzaYy8hHlT1MUOEbAiSFMuOtIkBiPB1HmxTDCmINWInJe+Pg5J1b4R6/UGm4sLMAPjOOkYzjcn46HK2J9Wy15asp7a/Q2hmJ4PpLO/ySiDKGsEeay0pZRwUH+NdLEB/I+IGGTmvwngd+qNeQDfBfDnAPwjAP4FZv5nv49z5TJjdrPLNE81w32OQJxbEArjX+/agECmeecjR4zZecdxxMPDA968eYNBcwjasSZYCtSeP0e5N7NP2/1VcNWgcNMINFakJ44dLbpOSoy/evEMDgnPnz/HZr0WdNC36FrN9uMbQRKuqci/ovfP4L/dOxXyL/cVPOA8Gvi5KZo5J1whEh1c4iJYob8KVgIoAR4EIhXAWn3oFPpKoJBT5IMY1RGojLVU1CEATV4cKSTEwHBJrp0geYR8flrtQ82X0MSI1nvEBkhIkkHZksOkiDjGfM22kfiD/f6gc4FzjUObUzFGhDhf4PYy57Q6JX49J+z4+u95RFDeL0OWT94zwHUeRa00RCSK3DAMKsAS0M7vadl+WOrA7wHwK8z8609JnMdavaMvzVnnrAB18EbtaSgknNhFRR90ypzOoxTnQmSOJohI3YR3ePv2CGqt/HiApPVjYae5mKAkjBZYCibJqFMnMxWiLwuARkkbtUMTEZrGY71e4dmza3z40YfovMNHH32E680KrSdcrjdY9eIm7JoG6hECOCqaj8k91q+cy7q/pZ2Sw1xWI0AeCWWHzvRzkgXn4PMzOi/JK51XSJr5d9VRWdj8phqvc2PIOvaUTkvQmcphU3eMA0zxy8eWm4W55zhPcMmp+7OQlM5Z9ijNQqzow2ImbD44VQ1ijHh4eEDfr/J8FG/HeeHUev7NYb+hhXLcozs6lgJAE5s6iAs2sc5jzHxgyBHIKhPVt6Rzs4Sxz6t3n2s/LCHwDwL416t//2Ei+ocB/BUA/yR/iTJkIUR4X/SfQnBZx1SlpSG7mnMF4pv0BMTE5sipY4jqSVFMVJwso22Qyc+QNGIJYPK43x3xxZs77A8BzrfVrq7kDwDLZS+ZXUhhLGDLwCYakTquaH4+IuSMR23boWs9UphyurF1Q7hoHV5drfHxq+f4xqsXuLxY42rVofUOnZbi7rq1RAaSz6qAIyfRBTm3XFn0zrXqNOSr75X+c17yMFJBTGz6JgjQPIaSBNTBw4Pg4ahBpAmRHCgFOCWkslORk7gKcIBQhg5geUlFxwAEcY8lz0IQVgLABL2Na3Ae3jHYayIvLv4LBHH6Ee1FhG+KKccepCj+ADkhCmuacQYQRQDLJu50AUWM4x5hSppwxaNrO8QpIUziEk16v5YfEVhyAGWBL7mA85yXIU1Ugi0vEOFiMtlMJbNWSsqPACFIlquUAOd7hESS9aoNPxp1oNw8dQD+hwB+QT/6FwH8cX2GPw7gnwPwB8/8rhQfuVhX0spMaEL8yM2X4KK53mU9ZgLAKufIzudJdoNEqp+rh1+MLMU1IEtBIv8IIQLb7QE3tw+IAVitexwrq0ABnnOhQwTEaHxBGTznCL5xaDW8FXCIxECK6LoV+oYkKQcB687jsm/wbN3io2eX+Mar5/jas0s8u76Wwp6cNGlIg7brQeTh4NCqAPAw054hImX6yQk/4FuQa4QjqGYak5jXZGcVAcWz3UVQlYPlJEwgakAx5Aem6MAUVN/W6L/kEUnMjikB7B2kUokHkaYGp4Ck942cuKWkxDJ1KsWEpmlVaDmZIYqebHFZAhPSDEoKYmD69cACaQJLolmwR6j0/BiBFCVJp3k3hmmP0EfxwvQtQhOqGpNy6hSF56jmNbwvfiu1ACjZoFA9Y1nqzlXdXgmCrE4WhisTnepdDQYwhYRxjIiJ4JseMToRAiH+aIUAgP8ugP+EmT/TB/ssPwfRvwTg3zn3I66Kj3zwteesn81g/pJUWSYeMUhV/8ZrUsWsAtiC1YEjZ1VyZQJbYkrnCCFMmrveI0KTgmJ+rSWEy9IbqIgdEQBtKzb8thVEEZOoO92qx2q9Aocxq0Bd12O1XuP6+hrPX7zAs2fPJPtQ16JxJS35arVCq3b1L+MI8mijeTpr29lmH6hYkc2HsroFRgWx9T4WyVsSJGWYqVz1QhAVCZoJuix4qoST9qKY/kDoug5B9d2ghVDyrRpvRITImjS1k5ByuT8PYABjQBzVVEsVvOYStVc/g5HF0zTltOzr9VqySqkVSeYk5dteWq+AEgNTf3cyHDU/gNPvHx3GSg0RxFCS5MRYajX+qIXA70elCpAWHtF//n0A/tq7TlDfni100x1nE6tyFFrqj0X3tM8kMSjUdBNTIejgJJhESEDx2INrsd/vMQwDBFkkrWdPJx0ogmfOyM6eh5CrBVkSD0tE2nYtLtcb9F2Pwzjk+24aycl/fX2NV69e4fnz51ivJA6g7xrJ/bdeo+/7LByd6fcL4q2akfp6qvMpv+qFIM+mFgXmfB2283Gxv5twcI7gmJCS7lcLgQ0y4g/qGAWk6GHJVwCWJLHVuAMkLsJdJ/ZwAI1ac8xr0XsCHIOUv2DzO3GM1DQIIaFt1H3ZeURiOMUJsk0kJTI1eKsa59pcLOnHhJSVgCa5flTHMVTjsFzsyw1kPgTVAPFTIsAQms3xc4csQuPVWlY71S3bD1qafAPg7wHwj1cf/2+I6HfKHePXFt89dqazkmrOGJ86Bp01E3qTvjb5CxKw+nakUCFGJXtSBLPD/b3Exocqr4BrGnjMPRXl3ng2CFStO69mJ6uGZM/ReI++kzj/xAlTGMXxpvEaZRex3mxwfX2Ni4sLiYbrxEdgtZIEpvJsqYRRP9qj2q/VbldgpR1xHgnkSarsYqb/DOoYuXp2uhZ00DSNCj+xIDAMnZGt1uKnwAyNyJqNL8CafRjwzGhSQhMll0Pbir8AuQRn7sYkGYAmLWxiJlrOJK04LUFLjVkG6zy+mtq8FkS2IQiSaSoSVwPOwlS8GPm8s1BNSM96a9nnNUJZtBnqRBkjZpTND0uvW8vs8Dg5+IOWIdsDeLX47B/6vk9E8519ySbXmYDr2oI1G599A/SUpVOs85MSK+KSm6PbFK5OccB2u8XhcMgdSCSWAeMkloO4lFuGAKRgaJdRgHxH8G2LtpNJNB6PUgo8Tyx5jvVqhfVmg67vJGpQw11NBajVpAzPsYCTC4JpfpOs+jdZx1dootqpKrYaQCZUz73OyQJHBMp1DOVEScO81eMoO75kIYDCB8jfVK6vbBzpfGjaFh0rpIgJLoq6lUgyCLlJU8grZCPRD8FJhUKCeCCmJElNYioQeuGybnNMHIpCLoZj1a/FZFerieedhoDSVRnAG1KCIayn1QFWwbwcn3r85y7NCWK9+hEhgR9mq3d8W/j1qxYChTVOs/fMjKCkIKuvQOEMZBIxpbxCSq65hMP+gHEcZ+pG27ZqDiuTodzvXBikZDxAm9OOW5Yge3nfgshjGkZBHFNAv9KkH0pqbjYbrPoe3kmmoV6rENcp2fMu5Wg2weQAFG0ABVK/q+9RTeRz7TEB8NgOB5hLdu0jkRWO2bXtWRy7xfmUjGTWxKFFaDRNC2ZJagYXEVwCRfEwjInRtWIdyPMiSBbnFJXciyT8ZRYCRX/OCIE5zyOrM0EuouGSKHaaAgTbUN5wrL/m1oLH82jKsJWF/RhvMD/exmXxG0XGpg6Y09rStFm390oI1AKgUXPY0kmoxOynk52xmLZsAci5xSatEXAEgUZsUWkiJB622yzVvXNIjtF1HSIj27HtPsv9lvv33lyAvSb0KELA7gHkME0Bh+GIYTjm8zpVUZq2lYKkVRHQrpNEH/W1Mkoy+/EZVcp2+VplkMPmCzAfQ8gRd0QWKVnQwWPjRaZfnNxCZdLlxe8cgdiJmW92vtrsawtSOZ0Fv1BQotiERLhrPsPGg/sWIIgXpfdapGPE8QhJlGICJ9lmEjWEsSwYtutXvvcpJkRKSElI5GGY0HQl7dm72pc76ksIggUSsPfm/ZpfSfqEnjjVeyMErC1VgaWFoBYWdnxdgNOr55wloRQpriZCtii4Eh4aQ8A4Tri9vUNKCW3bIpCYEYlI7dFLq8Bc2tcejfV9Lc2aIQWMwyRZhfR5YkyYQkDvSxnw+tz1Ardr1cLlKdZXfyUICI+sVTvPWV5GC2lV98JkCKsWAoWgdCRmvFRxBvX/rS0/I5QYjHNsds7/j8JPGC9BJEFYBKcBRSmn1ZLWK+y33REI+0lJwFSZRjVBrF5DEo5qiG4qfTBLNOKNRC0Cyjar/Kx8GjBUf7ccLlTWhmWPVVcyTW52LkuKIh6Do6Ce1D05T947IVA3WzznrAMAZguwNkFZhl0rxCDWgQgkaJpr3V1ixBQmHI4H7HZbpARNQBlOFn19L/a5Nedc1v+X39cMs1XIScya3TdpFp4E3zUnKk59XXsWIxvLIpjPl+W/s/54Zg6oml0Eit5TaXOLQV7kRiUULqtCFHNBnS0yKP1isHU2/0kWce0TUvrBIjXnny1bfg4YySjOQd4Ruq7Ber3SrZNwGLdIcRQxwAynIqAWlM5b4VRxJfZW7ShKkRvnCG3XSxIPLmrkUgDYs3yp9shYPf21zTVRhUwQHI9HmW8pze5p2d4rIVAvtFzlFvO0WLX+X++MQPEtcFpl19yHU0rqP246IGvZqojjccD93b0OUvHpL1V2z6eSgprAmsZr2THKPEJNCFoTe7PULsxqjN7zMERwDNjv95l/MHfWlCQCz65dcwLmnWeLgrneGfQDcD7O8iBIvJDLvEbeqeodi8uOa5+XXX9u07edyVBH6Z9qwdZQPiMKysVnM/TmYle3yD3pB1TXrNARSJ6DbcGVjEONI4H9PiF6ScVOSgQfhggC43AsZkGn443SBWBwJpkz2nEknpNUP//5ZmNWj1393fkfzX9bPsy5lfTOjK9YhNMn8W3Ybrc4Hq+Q+BLtwlGpbu+NELCHrdn/utVIoBYQNRoALJY8f1nOn1gZ4JBr2aeUNKnEDt5LsQmB2vWEnSchLYlNipmobdtsi62FQA3ZRf2Q3zVOsuuSVx+8GJAmxn5/BMHuQe9bH+McyrD3FUjGEmjXgoEN8jKBEmM5L87tMvlK1SKeb+CqbvDs6EpYW7Xi2WjOkIMhGvlNjSCs4AyDjNBV7iATyZxAkQCuNgaSGAw5f4AkTElI3gFto1mSRTjGaOXfWT2YTHCR8gQqBJzL6gg5KfzCRNlr8TFCcEkMLue2EYrzTi+DVoRyGR2TqQSRyYUrgZoIpe8Ph4MGRQX0fY/H2nsnBOz90ssKmNs/rdUmuHoXszfMpTDIpGXBUpSEGJZI0s5DZLBeSEHvPeCQY9+Lh1hJOln7AiyDn+pnSck4Cck8LCZBIBHnnIiJ55V6bFOq7caz3ePRLYgXf+lkUtUQ/Uvrq5irR1l5zuN0/jgzVc3NVnzmGnMzcUEE7oTdXs4NwwfGSXg5uSxeDyQfJXNS49Cxx2a9KgKAk6Y6j5hSAkdJWmokqThMVcJPEYldP4SCzh67P3umenev+3DWD/S4/8diNGbXK6pXUXePw4DD4aiFdM+390MIVJ1QM8DLjlyG/NYC4DEmNev+mjUoRkmOQUBmUAnqROIbVS1SKdcVK8ccknTh1uoFLxl+mxOCsL6HkhlHS2QxI1ICMaOhkkoLsIVvyvf5nQXVgM/7Ekpozg6bwdmlsNITVaRbUQWKyjFfwNb39X2W+ynjmbi4DhuhZuestIfKiakgPFYWH/FUFVg2UpLQgmvYOcCrQuSdvJIDc4NV5zH1E6aV1FKws6ak7L8tUJhzWd3XGrHJxkMVlFC3GiEsidxaRThBeIxHfAVE7SHU3GE5r2h0coxtfIfDAQ8PD7+5OAHgVMexz+uduP58/oDVAuFiMjEUEGOyYc3hljLhfN7VnUPxBgPPoPgyh4Hdh6kFtUWjfp5atzMSqY6CIwckjhl2y7qaT6x6ItkOZSAx6950ZpkQVADYWq0Wk/6meKAVjsG+Btdw18aHZ6eX6855Aq7OVXM5ImxU42bbbSvcclbonQoAm/D53/X9wHR8EyimNjp4BrxnreQstRdrVTMml2sbpEoAlOuU+643oyWZXH8GzFW6JVdQj/Uj21kRREAx54KzcJcvRb2wZzkeBtw/7H4TIAGcQs9zTPzSm6vuvNKJRoQhe+GllCQphfkXwMNRykLFe48GlINEpPx2M+MebJevycn6/s7t/nlSVTyHSWvKMBA6UZWzONGf520+WZb238IL1MIBKLuGLdRq41ZBVHbn3Is1GsjnKt+X8wKcr3Byx9W9/SDtvHMSV5+DWX0PeHYfWVXQNGSa/BnekaZza9TFOGEKEd4nMAlZ+bi3rQlK4yhO3YWXguDcd8CpsH/KY7AcY+NbU19UqReaYWgYsN3u0bTdo+d6b4RA3c4hgnrw68/tvZFJAMQPnyJIYZGECpuPQJk4KQlp1bYdyENdcx28kzjycRwllTXVNmcZNKs9WJOGy/s1NcSEjezcYlfO5jguEDbGgBRjTksOnk+G5QI4N1dsnYpwWKhY+X+nfV0k0nyHLddaeAU+BlmVmMv+AmluSbD+c3DZ1GjE4JmnOUETZ1/niDUVCGImTOX5DHmpuiHFWj1ibDD5oJme1OJg92tCc9bJejogByPN+7MgmiVCoLNwrcxherRvkdWBfBtsKfNKXIj9MqWEaZyw2x9+8wkBa0u1YLYLLiZChuiAjncq7K/miJOvZCLEqEiAGW3bwDGpK6os7K7rZCHrVn2O8DOLgFkMLEVa/b35CACQkFvNaUB5V7ZnA0KQiMbMe+iz2GJYPrf9fvZZnrycJ6keVBF51e5ErKXEkXfIuo+XbQk8DHXZwhDVRgWjUyFJJDH3XLlek/oFLFSN+jnrZDFL3wnhGoyjSBn1zYRajW6qF7PkknB12fdGTMp1ureMm6gkMk2Z1zEkoAlkeN53S75lpg491b8ZMS6ltZ3Y/pQ+A0wQnMqWEMQM3raHk2tZe2+FQK0vnbMU2DG1/T6rB8a8s5gETzMZJ3BkTb8U0HdrgKyMmJiiTB1oDKJz8UtYkjtLv/5z9+e9B8hhSiJ4mhzyjLxJhVBUlEKWngqAfH7McwI80pGy/vncpOPqOshewrOd+QyUlb6ufOXtVTXL028+D5QqByJTYxxXqCf/b8a7mG9D8TkwAZDySwKB5otnxtRnuZCqlxKJxhU4cRd3TrJANWoiTA6asUlQHJEgjKDzS1RyAmHZt+cXuTzlKdLKx5JZWd4xrtpdPDvP6V2kxJjGiOE4Ln+d2/shBKhAoUc77sx32cZc7yZW7FIVptn3Vo0oAdMUJBVTlAkLLucU3T+i7ztJf61VazlZSmjpfUc6cUiz9KcyqSyhA1DcgVNisWtbLn+W7SMRMCWZbMcpYAyikxaHIc1i5Gy9yb7t1V2XubxMKIgXnDhAiTdCFHWEk+SmsypMdX8aw62CqVYnCpmpEkVTpwuDIRmHGCz5F0niA7z2izgHSRrisk/rfRpkJtZIYq5uoCAbZs1JqM/CLHH8KUlyk6T9aXpyQUFUXdNAS4lFIO1n5xxcI+qBj1JjwDHDRY1H0L5xKgwciSdiSsixH8ajwEy91e6tAEw3KRQBan1aJvocUpTJPkNb9bjVctisBIahJQU5YxjC6Tm1vR9CAI9I8AUxWO/6S2hek0NSn86Dspsng6ARVUny3Y3TINlrXQOiBqwqg5CDDXa7HTYXGzAEwk5TUDiYkIKkx2o1Z4DprDYSDoQpiusmc8JqtQIzMB33aNIIuIjEQEgM8h7BEcYpYtM3uD2MeDiOeAYnOjUYYwiIkGy+0Bp5HVqsXSsDnpJE2Gkuv1yAhVir05R7Mxjt2MpVm2Li9PfQ6SxpuJh0R7RdV1gNMEUkEk/MZKk5HIoQYIJPpB94ccslm/z2C5nw7Eg3Z9vxFbmhqA5MnXyvEaIJVnBDX1xDbkMIxvBL1WL5ywgQIRD19rQAIsg3cE2ADx5T1FoISTzwxJVchC+RFGEl0XBgJkRmG4vFRsYLiwwxiKWGowi6RfyBUZkL1JfRw2zh6GekEZXKN0hSnQTSEOLDYXp07b1TCBDRnwLw3wfwOTP/7frZS0jNgZ+EJA75B1iTiRLRLwD4Q5Bkgf9TZv53v8Q1TpDAUgDU39WQvP5OJLuez5cMv8knUJAFovJcYaYErDjnARJHHklwKtdp2kby1zuPSDHDVEMLpgakqL7ZlWBKMeVCm13bCWcAgDjCkYTFpgzTCVNitCni9v4Bb25ucXWxhqcNUuORNIGhbzs0/QbtyoFaQseEJsnzaKfJ0kqywKIuMiGMbEFKDj7TEWp14hzPYOZAhqkmCZEDal3c+HipESilxhIDHBkELzUTvJffhii++FEyP8n9IiOZoIleSq4Hye8Ykke0HZ8kOzLgwHCIGuCTeMkjyJhZ8dNQ5Y9ItiaFlQWc1jT0Xou4RDjH8F6wB814msxoAlWf5Z0eDoZ56rmZ+aJMDNb9b1gB+VyzxX4CkGn2R5Wb6oM6z+Hjqgnw5ZDA/xHA/x7Av1J99kcB/PvM/CeI6I/qv/8IEf1tkMzDPw/gYwD/NyL6HWx1wR5p5mxjJB5wetNZ38cp6VIf75TJlwVKIDKvMw/nTP90WcrWOQxqE5/A96LPWwIJMynaNa1oSk0c1s9hbsQSpuyQNC+43H+xmwPAMEW8vbnFt779bYyHLV49u8Cq9SAAq9UaV8+e4+p5A2p6DMMAnyJcayHXmkSjfi6vDiSQnQGmDiTZyW0ui4iQ1KuGGoq+ncCUynEMdXRKeaKbymG7V7Ids5EMSimnudLqQuRAbQ/PLUKYEOKIkIBhjDgcjprm7ZizQ4McfHMJwAq/igCyOSAbiJCt4mkIgCRDQ0wSIzLFgClYYVTWe6Zc/9GTlUqTdGW+0XTnpOqGAwCJYQCMGDT38RLSTWQ7sSz8zEct5nRRUarPqg3wqUX7ZZupHu9q7xQCzPx/J6KfXHz8+wD83fr+TwP4DwD8Ef3832DmAcB/TkS/DOB3AfgPn7xZUIbVdaTgUmeq/9aCoEYNvvGzSDtwyU7UJCmGycoN5MVPkkGIHM2uE9VcZ/kNJOcgshCo/QxyfvpU4rklzViHruswTZOEymrIK8OBHGed2Hn57f39A7744jV4OmJ316Ih4RQur67wtSkgkscqRPT9Cqnv4dYrbC42kDIzZRJ5c1gCwCkiJgY53XUdhOBy4jMB1WkdaRBPSqiZd650+YwqAAEU2l8Mg+YWsUmA80hJshlHEmgoFYc4u2wPxwHD8Yjj8YDtbouH+3tst1sMwyGX3va+gaOHDJOLYFNkYhmCTMlQIWVJRYJG/YXECEmuD7JnkZcJAu8F5bVelA5A1QorQusK0SpzRfT/WY1HVHSAcTuLOQ0g169YtnrD+7LtMaExG69H2m+UE/iINZkoM3+PiD7Uz38MwF+sjvuOfvZ0o7pAxxyO2vvaS69A/yIIzJGn0bJg+biUsqdY4gaALAjArulzVhvZKYs1InsYKlIxt2UzCdr9mbegIQPzIWjbFuv1Ov+WSBxUcixYpRP6pkHSkuiJWX/ngCRejff394IU7rfYXD3H1bNneHl5iXR1KZPQAU2jSTx9CcVNbHZxTentGOwkIIecByHCAnLYnr9m4o1OU+icv48QGJ6JPIvtSLrIGQGyK8fEiEk4kHGKOOz32B/2OB72OOqufzwesdttsd/tNOvSiGSmVCIghEyazbzusuqscHoR8WcCI2n9QpBX4VcWclYhnRQtaXwCN1AVkbS+pW4qSfRvqErhqj7JqmC15rJ5sdrdMwp4YpeuvyK7yXegg6Uq/UNDAt9nOydyzt4GVXUHrq8vZmSfQfFzCGBpqgLmHIHtyLlDnLH9NhkEDgNakNI3MtGUQKvNU6KXRi3yIRmDak9B4wbM07CuZwgAfd/n7MDZqclrMEwyYSf31TgPuIBxCri+vsZv/+2/HZfrFggTHu7v8eb2Bm/evsH2O9/D+uIKrz76CA/Pn2P/7BrH8YiQXuHiQlKUt0SIMH065ucU05xaOVyESx5EXkx4un8VE5wJgbJjCjTmorPHlMk9RyQFSyuyLgA4aiq14+GA8ThI7obtFrvtA3bbLQ77PaZpFFNuCPo3IgZGmAKmEJBCQEsl60/ikmsyzVJnOXh13fZNg0aLuvqmUXLRgzxA0CxCJo5JhGjJalV8LJiBxB4+b0LIglUKhyL3lY1xQhFWhjShm8qXhvlcSagy4edrCHMEsFSZzdLyrkv+RoXAZ6SpxYnoGwA+18+/A+DHq+O+CeCTcyfgqu7Ax1//gO3GbcHU2YKWrbYSlLBTyuRcsRQg64m+EShKFGEFLmTAS8HOpZuvwP0yoEtuwBDCUpVh5iwAJElJyhPAkdMkFLbgVPA40StDDOj7FT766Ot49fwSfeOw227x6Wef49e+/R1sv/0J3rx9i4f9EbeXX+DtsyvcPtxhf9zjgw8+wLNn17hYr9F2TcVxCPw2nZ0owSUPuATnhEGW7pJ+iZUwgJrYjLcWws8qOVXjqQx5jKwp1ALujyNu397g9evXuL29wWG/Q9DSWDFEqQIFqQZ12B9wPBwQQ0DTeGw2G1y/uMJ6tULXeGA6IIUJ4zhhGCRzznEcME5SSj4msWY4RS+s9xSZAKEVlE8wzKApTKioAuxIqyc7ECx7k+Yi9No3MYnPA7TeACiPI6ACwXqESmzBcmc34fquBbrcVR8D9rUAmKkT/DQpCPzGhcC/DeAPAPgT+vffqj7/14jon4cQgz8D4D/6sifNkEwFAFB25nMeY3Of/NIBdbkyg90lXRnBcsgY/Cu/Kznb7T2zy6mrl6ZK4wrsXgwBNE0jVWtURRi0srHdp+0yzjkts41MMsUQcTiIfvz8co1+s0L7/Dnavsfm6hrPX36ITz79DHcPWxyHI754O2IMAcMw4GG7w9devRBBcHGB9XqFri0JV1QJEYGnOfud81KIBdACl5wXvPQLKoFV3J5Z7XIG9yVByxEP2y222y3e3N3jky/e4Hg44nDY43g8IkxTyfYbGazJL8CMvuvx6uUrXF5e4vryEhcXG3R9j8Z7seClI1KU8vHDccThcMRhOOI4DBjGURCDTnjWPlacP1NzohpGvEYXeSWJLXGLkQXkYhYCQAnzJhaUxVrbsObjBfqfhhvUG1lxassfPLlI83dfgiOo1eR8LTzue2Pty5gI/3UICfg1IvoOgP8VZPH/GSL6QwC+BeDv15v4z4jozwD46xA0+D95l2Wguk5erOf0/qxvVUJh+Vvx3EKpaQ+pSyhkXB1pxTMeghk6oXkWpBSjONjkTLN6X8b8WzJU4wFqi4AlGpVklINmqnVCxLkAYoJrCD7a/RZBdDgcsNvuEF69yDvIarXCRx+t8OzF1/Dygw/x+Rev8fqzT7G9v8H9wwPGacL9dos3b97g+YtnePH8OV6+fIFn11forGIRALNZe2cWEycohADyrJMtz1DZSXIdSFWpmBCDEG/TFDGMAYf9Hjc3suu/vbnB27s73Ox2Rb1IEqfPRvaRQ9t3ePHsGpv1Bpv1GpcXl7jcbLBaSbp277zeMyOOgqC6rkffT+hXa2zGAcM0iRCYJKmm1BuQewvq7ZmUGyLnBCkww7uUzabMBK8WjdmcUrNbShJxKCXHNOuxqlZQ4ZpRFxXvweWmhWpxApiFI9vxj66PM+vlXb+R4f4hCAFm/v2PfPV7Hjn+nwHwz7zrvLPfoBB1eo4Tx6Baz7dW2+2znwHmulKuG0eZW9b/F3OOIweORdjOAoEw3+Vr9aNWX4wPMI7AEIIFEJXnU1MiS8iybrVii1bYeDgesdvvERNjfxxkUpGDbzpcXm3QrNa4vH6Gq4sLfPH59/Dw8IDtdof955/j7v4eb25ucX39Fl979RKvXogg2GzW6LtWrAYAopNiKBkhEWlJglKTQCwLKqAs/0HkXPjycByw3e5w/7DF3d09bt6+xZs3b3Fzc4Pd8YDoJdsvqVAmAH3X4/LiAhcXF7jcXODVy5e4vroSz0ud6nn8lLsBc66pJ3Z/5CpSPjEE7DggRUQVVJRSJjOhC5CgeQOZQBhzTIPXMXGJNYNz0kzQ4v2U4nxzYE5gJ84+jhjsnNZqRJ5rNkfqOJB6IdfWgxrNLvmw+phzpvEZ4VgLmy/LPeA98hi0VpNoNRFoHWDEn6UHX3ZWXZ4vpeKwYwsMtOxsoOtbHMYBTdNKSTIUstECjwDkmnR11GC90wFFFSCinMgkmxGJEFOAlNwWeJ2iVMtxjrDue4Ai9ocjHrZ7jCHg7du3cj7nwRjgu1GEwNUV+r7Hy6+9xJvXr/Hd734XN2/fYn8cMUy3uL27x+eff4FnV1d49eolPvrwa/jg5Uus12t4T2icB3UMghPXWweAAd94FVakCACA+g+kmDCNAfv9AYfjiJubG3z22ef4/IvXuLm5w+F4xDCMGMcRU5zAPoG6XgNzPFarHq9evcDHX/8YL1++FO5CSVUOCVHRFJH0yzCN2n8RIY7Z4UfyQxQSdtJXiBI1F5I5BZVkJuCK2SC1DBDDO8CTR3Iic6JPYj0ICU4dyApCNLYQADMSOzA8kKT0WkXRzYhqi1a1OZ03uGYec2JWrWU7ZzJc/rtsegyXJP4lE+b52POC4b0QAhl6n4E4tfRbCoXlOYzVpsWxNgnstAxkH3/b9Wo9asb+Owd4yve3lMbGHQDzNOOFU6gkuL13YmI6GRJ10DHLhXMO24c93r59i3EK8E2Hq+cv0a8nTClpQs0Gz158DReXV0ic8N3vfBeffvIJEjNu73a4vxO/g88+/RQff/x1/PjHH+PVq5doV16Kak6Mrm3ROI+YIigCSJwFYFIuQBYC43g84M2bN3j79gZffP4ab97e4u7hAQ8PD9jtDtk8Sg5wDQOOcPX8OT788EN89NFHePXqFXr1oByGHcYjS1hxAgynGY8yjkPu20kjLC1BzBSj5os0ByCxKozTmFUACSE3mlDMhYJ4JO4jox1SbJgAhoMUUGYwnNQ99MIhOMo0g6AKFiIyQYOk8lxMJ85BrlqMhgzq9v36BeQps9zxDXXYHMZvEiEARg7DrfkA4FQILNWEWudKWT8ze7+aw4wrUCYebBlpnSKnlAm7mcUBYjuSWO3iQ1B3vC12opIOfMlb5GQk6v1GxlPoxDc9PCn+vr/f4s3btxiGMWdLvr+/x+3dFkP4FQR2CDHBtw02FxfYbDZYr1Z49uwZ2n6Dlx98HX3b4vUXn+Pu7Rvsd3d4uN/i4X6Lw26P33b4MXzwoZgU+1ZKoE1TgrhLSzBPUqQkZJtwAFOIGIcjDvst3rz+Al988QV2O6mkJIoOo/EOXd/j8uoCz19c4PLiApcXl9is12gJeLi5wc6VClMES6sGgdFJBECdSi7GiP0wIYSoqdtLkdAY5N9iIQg5PDxVc0j4n4ocJgfPohYkFOScCCAyAlUWEEdG6zwm7+FcEA9jskVW201ElZIJRerCbPkoi9XK5nH5ZbUMzkD+L0UaPvLdlzVHvhdCwPTuJew5B4HqBJRLwlBOJjtXec9ZHzWW2PQmc6awaD+7BuUFIHrhCcFTNRMCS6/B2rkpqw85D1010EQgktToIAJH4GG7xedffI43b9+gISmSMk4j3t68wSefvcZuP0rJI+Ue1us1Gufx4sULrFcrbPoVeE2CHJ69xHDYI0wDDscRX7x+A0eEECd8/I2P0D2/Fj+7ENE0pH2LvJMZFyA8gBROiTFiGgdM0whwQtd4NM0ajXcg57G52ODq6gIXFytsNhu0TYsUEx4ODyIwnTousaHAlN2BLV++JXJhhlgEQkKqxyAjv1R8DJKV3TJSucwZ5zwa5+AbzRmgcRSK7LPHo0sJjhLIKholRogejZcMw9GRxIBUy1h2fuUciADngWqzMHW0VnMth+G5hbpcA+eO+TLCwTbFd7X3QgiYLbNe3OdUgiWJci63m3S0IoeaGVXyjVUtIBQTikHOGmWcM0kuLRZ1rEHt8VirCLXzklS60rBb1geHhrJqtSTW+9gfDvj0s88w7LZwjnA4HnG/3eL29g53Dzv4rgc3LdA06HcHpBDx+uYOl5sLXGw26NsWfdPg+fUlLq6uwXFC24iguXt4gPeE1apDv5KKxwLDi2XAYgRgLr4hIIwTwhSy/tq2HikquegbXGw26LoOm80GfS9xA4fDgPtplxOw9H0PRx7HYcR2u5MqOSFiGEYchyMOxwHDOKipuFW1YQS5Jof8eieJQJzXOaFmR4spcNBsTQ7qDt6gcR6tI7Rei9Jk56hiBk0MuJjEmUiRYfJOOQ19kUYQypYCgu74iuhyEpWFWmlWpDIfE2KVJt1avQZsTv9gqkKxmj3W3gshsDRjPJZR6BwyWPIIpgYsBUmGYPmj8rsYIlgdauw3Bjed7ljLa5o/A1BiCcr1iypwknfQMu8Cmah0IC2fJoktu9UaXbfC3e09fuWXflGq4foGTOJtGMEIY8BmfYHnH3yI589foGuE+X+4vcPrt7fgGOBBeHZ1iVfPr/Hy+TUurzboWw8gYggBN7d3WG9WeOVfYNP38JSDWCuQKwlAzAIyTSPiNEqSzrbBOAxIKaLtGmzWG6w2a/RaUPU4THj95jNst1v0/Rofff3r+NoHHwGO8ObtDcL9Hocx4HgchFfY7XBUcnFSs+s0TQgxoW17NK5B2zTouxZ9r9WaG682fwZYkIAjyGJtHFwreRoaAlrv0HonUY0kTKhwfcXfQULOoaZBIQp95WNCUlKyMjmraZkZLiO/+bJaWrgy2gzz3XypDjwmAJab4dOC4kfnMfhDb/Ygj+26y+8AJe4qz8LH9KAsIPTf9TnNBwCOMoqor5W4ciyq6gqYoxBQhMCSC3iM6U0qofPozPgJ4ObtLbYfvsLv+NmfxU988xv4zne+g299+zv49PPXOBxHvHj2Ah9+42P89M/97fiZn/95/NjHP4bNeo0YI37pF/8GHu7u8c0f+zH8jb/2V/FX/qO/hNevX+ODl8/x4Qcv8MGr53j18hrXzy7RrVa6A49YrXo06j5s+4bBaWYCR6vlZwy+5E0wJyfnCKt1LxYOAHf3D/ji7T2apsNP/vTP4pvf/CZefu0Vdrs9fulv/S388q/8Cr736We4vbuXe5hGHI6DcB3eq2OPxH20bYvbuy04MdrGo+tarLoeq80K675D13h4b5ahIN5/jYN3DRy7HBjUeOjLoWmqpLEsi5iienCyqgYk5KvmbtG0YyooSYSjUyRhKg2RJC+djbfOiZrziinBVZGYT6kF5/ixpSB47Bwyx54WBO+HEKgW8bmdvf5bv1+qCALBdYGhwH39FZAHsJA7BE2yQe7knPJSezHKYl8KKfvM4F7RQx2M2JpbIhRdkOw4dl2Q2MD7xuHq8gIff/wN9P5jfOMb38DP/tx/Gdv9EccpYJgS2tUaP/7bfw7/pb/t5/H8+XOkKeDu9hYRQL9e4yd/6qdwf3MDcn8Fb29vkWIAp4AYRrStw8sXz7BarUAAwjQiTgHoPVBhgVqEmblVwoJllx6GI6ZphPdiFt1sVui6FjEkNG2Lq+uXePW1V3j56hV82+LXvvVd/Nqvfwvf/eQTDOOI51/7EFcvP8Bmc4F2tcL3Pv0Mv/yrv4qbm1uQ93Bth0SEQB79+iI7G4XEOI5jrt4UuhZd6zVFGIM0rNo5p5WdW/StpBZv2xZN6/PmERNLLElK4BR1XnB+LfuhbqS+gaJKGZQPcE2TCd2cd5Iox5EwM0jDq+u2dILL16kW/jl1eXncbDM0te6J9l4IAdODz+n5S6b0HEIAUOndj5gQ7Q3J/8R7DFIBaAGn7B4IhKC73rKDjfU1IrAmY5bl1GtykohySXEgFhJOhZHMRzlX07S42Ai59uprAYdhwsNuj/vdEa7t8fzFc6xWK7lW2yGBMAwjPv/0M/x//+pfw7e/+wmmKcI7DXJqWhyHEXd3D9judri6XMORzya184NTkXApIYYJ0zhgt9thu33AMAwa8dig77u8uDYXF3j26iVevHyJxIzPP/8c//mv/Rpev32Li6tn+OmvfwPPX77EF2/e4vr5c3z9G9/AL/7SL+E7n32B3eev8fLyBZ6/eAHfthJAFCLiNGEcjqqCqF9ACML8EwNe/BEsNLw1AdCJB2ffdZIoxjstKyY6fYBEC+acEHmelFYoXSUcocQS5nOjpLUv1iQjvWtXchAhpHiCBGaLu0KKhUc+dRg6neXVmnnELFi390IIAKcL3BZZ/dDnLAjAPD8hq4ub7bSCBvJFYG6zznk4BoAIMODJFXOV+sV75zDEAUR+hgJsZ1/CfxMGNUcAVGZH8nDUAhyAKIE73oYpJYBF4DCAMUSM44S0XsPBofMNAgL4OMBPEzZdjyZFjIcDOu+x6tdonUPvPe7evsFf/uS7OO536DuPr3/9A7y4vsLzqwsQSWajaRwRQ4TvWwCSfCNCMwARiR9DiuKHr2bUqL774+GI/W6L/W6LEKMIIgDeSRYl71tcNGtcXn2AxIxPv/gcn3/vU8Qp4Md/7Mfxkz/90/iJn/opXFxd46/+4l/Hbn/IzoEgOc/z58/w8cdfR9N2mMYRvToTTeOAaRwwTgPCKN6UjQfaxqHrvO74HquuxXrVY7VaoV+t0K1WaLtOOQTlA5JkIABDfQUKmz6v51BsAaykH2tIuKRTA2Cu6c6JChl1vurvljULLYFJchq7UCNgO1Y+0X4pggDVhjJfP7oeRI/LFgjQqTmybu+FEBA4dV43WjKny8+X5hTn1EOrjKwyyMX3XVJ+tblIaYoRDTm4JP4K4zgijhP6vsdD2IO1XLYJIDMJAtA8gpWfQoVm7N+GDEC92KenI9J4hCPCykmOgxAnYfApifNO0yDBAa5BGic0cNg4j2MCegZW04hudwAdJ2yuCGtHODaEj7/2HHcfvsB3vvXr8B3j5Tdfic7sJMPxenWB58+u8OrFNa4vL3B1dQHvxO9gIoHgEtiUgDBJn9EkXnvTiHE4YBgOCOMBHAMca3ZCdvDUYLO6RNev0PgWmAI++eRTfO9b34JzHn/nz/9X8DM/93O4fPYMq4tLwHt89OoD/L/+1v8bf+kv/oe4uXkLDgO++dErfPTqGS57D6KEy02La6ckoPcgAkKccNg/4HDYIaYJjSdxi/ZSFkxQQIdVvxJB0PdoG0EqHkCTJoXrUYp6xoTIBNKXxRTAgqYswtA7eU8OSSNKpySmRteKn4h5K9p8sRiTqEFWuawdS95IK8vmGKJ6OhG6xZrA4mau6uvM9KjzP4SYEa0RmTFGJLiS3yCcqhrAeyIEHmu2654jPs7pTkXSlr/yeUFWXqFgHSlYYhDKua1wiOnxteNHvcjr0mj193kQaqTA+gxZ39R7pmI50CdZEKCimjgnIazHwx673RaX60s0hx12Dx3WfY8Pv/Y1pHHEcfeA4+4BD/e3IIiu27QtXr14gVcvn+PZsytcX13i8lJ8+PuukzwHJGYxrybLpGaCGFxW2aYpaN37UnzFHJVevHyJ1WqNmBgP91vcvr7HcRhwfX2Fl1/7ED/zO34GP/7jP45Iakv3Hh99+CF+8rf9hKRL84TNZo0YA3zjsd9ukWLE5eUlXnzjIzgQhuGI4XjAOB5FTWgbrH2LVd9hteoAzQLceFUF+h59v5KALudFFeMEHxkhJiUCGT4ynEtwZJaDZCOR0WV5LeYdeO6Doq22ItXzYR6qPt84ah5rGSb/mN+MjcPy2KVl6rH2ngiB815SdQKPutnnSyGxbOXBSZlu0/nKIrW6BJY7oN7Vx3GUzkWx/eddHaWgad3Btb9ArRMKGrGAJlVpmHUCVUksSSalZO2JapbSvPho0XYd4Bi77RbHLz4Hb67QNQ7Pry7Qrdd4+fIFfu5nfxbPry5xe/MGh8MOYRzQOGgAETAcBtxME8ZhBDPQPG/huyZPIt80ms7cgZgxjYXgrDMnOSJ0XYf1eo3rZ8/w8sULOO+x3e4zZH/+/Jk4EF1dwXtCSgHtag14j5ASXr54jp/+yZ/Ew/09xuMhJ3xhTuBOfBhevXiBdb/CcX8Qj8XDHmE6gggiBFYtNusVVqtOrATeo1UhYOndGs3SLNIs6o4aNIJS6X/5tKRWsx1dPU9n5DTKpuNI0ME5Ym5pzbLPzkXCFqeyYpFiFtXhMVJwyaMtBUZ9XknwdtreDyFAp/b0WhDUD2ft0VDiEzWh/rdJ27ljj+xwE5jnrsExRrimA6MkD6071RZFLXlrpGDnyv/WbD+S5QKQ3P2pvOQgcc6JktGXnNi7G+dADlhv1lhfrLDbbfFwd4vjt78F4og0HnGxXqNpGqyaFj/5zR/H/uULPNzfYrd7kMXnxabuHaHrJPXZZrOW1GaRM9dNEMcYhi4caIqtKuGKcw5N26JtO7Rdh77vsV6vwQC8P6raRbi83EjwEwEPD3fob9d49vIVutUKxIx13+GbP/YNDMc9usbhzc0bDaoakWJA27RYr1Y4POyw3z5gv9tiHI4gYrRdg1XbYNW36LsGfScLv1Hzbdd2kmmpaSWVGiPXemQKGeVlHdwKmmjWJKsdUSeAycKayyYjburnU+PZ/LINpCaL6/lbz32qmEmi0/m9VIHlHuY+CLX5fHm9ZXsvhACBZh0EzANzaqhdt2UcgenpdceUBJBU1ALGbEe3Xd/7plIN5F68cznmYGkSNHhXQ776nCc+AwQkRDCS6KGacts83cpzaf6+pEKA1PzFwOZijRSfI0wTDrc7PNy+wXfiiLeff4bVqsPFZoPr62tcXW4AcK6sw0n4kvW6x/XllSQeudyAiCTR5zhgzi4LcrHQ4VjBWOccum6FlFCiKvVZveq/FxcbdE2Li8sNppAwxSTBR6+/wP5wQL/eAE5Sm3nv8eHXXiFOIy4vVhiOBxz2e+z2OwzHAx5u3mB7c4/heEQME4gY3apT4q/Fqm3QekFLvaaJb5sGbdug02zMlhfC4gVsT7TFnVgsSxI2XAReTGIWTVkIoAgDNiLbPBBxdsEtHcdq9XFJfEsfFvPyOY5siQiW51ke8xiitvZeCAEQTsIol66850yGtVQtHVA6wXzGZce3pCFFt6+vKZmEi96Wr8VKWaPs7PVfO+5dAkC+i2BFAs6hwE6u1AGGFiZJCGECwOIeCxGW/apF65+Jrt+u8Hp7wH73gN3NW0EB6xX6VY++62QBtI1GzAHjcUAYRzh4rFZrbC40ZVrbwaeElKb8rEmzBRn8D6HYtS2LsuQqFCL2eDzicDhgtV6j7Vp88MErrPsNhmnC/nBEx2Jxf3i4w3e/94kk5vBS6WfVr9C0DbbbBxy3Dzju99huH/DwcC+f7Q/gUdSupnHo+w4X6xXW6xX6zqPtPNpOvAm7VlBA68UU2HiP1okaEC1z0hnmH2omtXmSx9ASk+SFW5PYxuXMd/5zzTiAeh4BOIscG8zXwlK9qDfMp7xrLYz5sXuy9hstPvK/BfA/ADAC+BUA/wgz35KkJv9FAH9Tf/4XmfmfeNc1gFNdujYTLmsRnMKn+t+A6dyn0lGEhFkI6mNklyvQzc5dcsaVQap5gKcEQC2B5XkiEk9KDEpIK0erqVf6IrHUJTyOA2II4NYDjrJ6sF6vkOIlomvgVxscDgfs93upksQJPE2YOGEaJKxlteqx2WxwsbnA9fU1rq+vsd5cAOQxhojjMGGcIuCsCpEKzVjSp5tHnC0gr4445ARyHg4HbLcP8E2Dtu0AzxjDUeF3UFOZOES1DSFGxjAecXN7h8PhAAIwTaNkHz4eMY4DpnEEMeNqvcLkxArTtl5Vjw7rXnb7tmvQ6WvddlJKzHuJFyBh1SVzMSQbkJkFda6YADZ9PKODyCUvQaUSlN8CEmadVEDOF+ASwi+huTkztZb5qdq8LP390hO2Xhc1gqjXRU1Ifpn2Gy0+8hcA/AIzByL6XwP4BUjdAQD4FWb+nV/q6lWrOwA4hTgGv2t7vbU5bNIBzAvQBsBQgoQGx6i/4ZqPKARgTRCiyim/zDJUBwjVqGQpnEQoxGwBcFk9sGz5dgciBKYp4XA8YBiPWDWbnBHYEdB0Hv2qwzokJG6w7lqsuhbH4xEpJngvk8smcOsb9F0n2XwuL7HebNA0rao5hFyFCZw5AMB2ROSdUTlNEKSykwkBR4QpTNjv91itN2g78cwDM3oQnCcJDBomIAmjTy5hGAes+05KlMWIi77D2LU47FrEsMI4HBFCQOMdpq5FgqhnXd9grS7KXSUE2tZj1XfZz9+TuBNTHgNNMqsCLVa7vAi56r1FNkYhBWcLH2Y1MLu+cguYbwpL/b3mkmpBUKumAHLm4+VuXqMBoHiw1uR4rRrP6m880X5DxUeY+d+r/vkXAfyP3nWeL9OWUH+px5wQbYuHW+pI8t4WuQyUUxtvCPVAzBnXmSCIUSv5FMRQuwHXQmCpwthzZBIxxlxQQxw4yuQyIcVsQiBgt9vjOA643qzgnDD23KgffOtxsVmjaYDjMKBxhItVXywc4hUF3zRYrda4uLzA1dUVLi4v0XW9Fjutrq3lw4wYVFZF7OSJyoSH5SbUvvONFk6RXIrmQdh1PbwX9OK9Q9c2WfemKJaF0LUSC9B4xBDQNQ3GYwvPDMcrjH2H4SgltXnV6Q5IaLsG6/UK61WfF3/baLhv22bXcCIHx5JYlpO+YlKSU3mOYJmPU37Zzh6rXXimCgizOBtjcztfksP1vFwu6HOknU35em7V5GLNTRnxtyQF7fqW4u5d7YfBCfxBSF1Caz9FRP8fAPcA/hfM/P849yOq6g4IiSWtXshL+F/vzsud15pViqnhUjkPUEwv5dx54GPIPMJ2uxW2e0HALLkAszBYIlILKlpKbUuGwbmopvEAxYlJfiDvj8cRb16/wTCMmsdeJ1srO7ZvPNbUolXLwaptARIPNtNjfdOg63t0q7V6zvXo+h7OSx7+mBeDJs8ks6DIXzOpAsi5Bq3/bJI7VyDpOEoq8HEa0acOjW80CIh095f0POMYEKaEVd+JMI4JSVGOJ4hzEwiNA1rNw+hagdfOO7SdqASrvkPXyeJ3akbNOyIqAk8dwjgmc9aoVL9UJSlJOamqkbVW18AS1JjQLAOW3fjm/TVb2PPNbEkyWxNU0AhxTGJ+tRT3tQCoBY05Idn8M2RRZ8J+ihQEfkAhQET/c0hW4X9VP/oegN/GzG+I6O8E8H8mop9n5vvlb7mqO/D1D1/xku08R7zV3y3u4wRF2GJ2rlgFbLctrsOie9muP45TzhJ8OBxwfX2NqYLDdSRY7RBkerPVGciFMSrUEoIkvrAZpHt14RtQJpcDcByO+PSzzzAOWrwzCpnYOY8UJ4mCcx6OPLrWa3FPqIepJPdwzoG8g2taNI14IWb9dKmnQkJuzVPOiEiYE4z++7GdLsSAcZowjgOG4Yi+79A2YmqUTKARDMKqc2hcg9EFEAgjRrgeiN4jhQnUd2i95BhEasHrlezqXhyYmsajab1aABzaxmc7urNIUDaSTvwtBAFU0F47nG1cTUikkpOQ9Xt7ZV4gzYU2EeWYj1oFX1qunDqo1TUrbKFa7Uwj/WKSmBKbizWhuExgY+c9F7xWq6k/EusAEf0BCGH4e1hXK0sNwkHf/8dE9CsAfgeAv/Ku81nH1MKg7sAlbHrivsoCp9qcUx8zd7lsW5GYISTtcM4SOEQunn6YwzR9zhkUs3YO0RTXoKQw9fHnCWHC3b345lt+PedJ1QGp9EtowU2TPQ5zFny7F1Eucz5Fl3cqU0MKQiKt0JxfYFipcs7w1/TNaoepxiyEgGEccDwe1T5P8L5VhKTQ1gNt26HrElariGmS3IApRqQQEMMkCzIEgG0jYCQyCCzCwHtCqwVXZ5Zjrtj6nBTFBLnxAMWTM4ZSKDWYWVBRQap2flYpPR+xigeg8156NUFc7+a28C0ztSFKsZQV1cKOWzqg2XWBef2L+ppLE/tj7TckBIjo74UQgf8tZt5Xn38A4C0zRyL6aUjxkV991/nMVn6O0a+hu0nEs+c4sxjnCzHfJcw5qThxNGgaxjAcZjp/sAq2FTFYu2kur7vkKpbIBtC1xChZjlChgOoWpzHg9vYeozL3UxPQkEcij9Z78YBzLdC0sqvr4ocGtUi/mlCAEIuucBFy3+KzAIjVgZw4BxUqTf3nUYSbCQFAFlRWwPUZp3ESIdA2WHUNqHNomlay+TjAs3AyUuNPCVrTk0NAilJ2jNVaI1l6RXDa48jOD4kc1FLrNg7R+oBLVCZzJQC0OGlWhXS8BanFiiNQd3BFAlbNfbmpZGKQqn9jLgAsKjQqSvF+zifVJj/C/Fw18jTvx3EcZ3PfhMXSfF1n5P6BkACdLz7yCwB6AH9BT26mwL8LwD9NRAHij/FPMPPbd11DvOTSTIpW158RH8sFVj+k/C0DldRsUwZNim7YhLHMNVaEo9a9vPda1RbZXFPf0/LvU7xFfm+zOAsBuaeljCYCxhBwe/eA7W6PYRgRugbJO1Vp1KWXPOCakjTVaULL6j5Yz095LddmMIkrgOZMcNmTUvzrUQmDcpOmCjQgUi/HBTcyjqMUBBknCSRq2rxTMlO5d5Q8g+AEjlqdKASF58rmMysvUmIrABViKWp6MTVdZBd0uZbo88hMf9CiKTHESgBYwtI5QSgh1jX/BOUToNcvnyeO1VgDlmwlqxBQklq7dOnaO9sAaT6/7VgzJ9bEtK0bK3pru3+9Rt6FoH+jxUf+5UeO/bMA/uy7zvnIb59k/h+TZPWDWqeYQCjnMj7AZU1cXIUlZfV61alOVXwAvPcAn5I9NTdg91Wbg2r21+4lE0EQvdp2pmQwU01y9o4gnnrb7Yi3N3f4+KMPcL3qxV+gdm0GA144BOcK9JdrWBqzcl2QKSSW5ixBfBcicoASmZelLSiHLEVozgdIf6ICCuZHEaWY6Dih6yRdl1xfSEdyXlFHZe5lBvtGhFLTaAKTkAWBXEdxkzpYpSTBv5GtC3XnA2k/QeM1tKZAErt/MGtAFgSasjxWRGnmAgxRmJpRkECOMmRRJyzhaFYbKu7g3FyuN4jSr0V1rG39dkzf97O1YXPMjq3/bWjgN4fH4KJ9WQm2/I3pj9Zh59BDSkmqxuhOSCQFQwQRFAFUIBqpOe1UxwdOScl6YOvjy28o18SzXINQl2ZUsI0ZGMaITz75FL/t46/jxeUFIrfgpGGjjuGaKAtbM+l40kVLMjkJSZyHlkRkjlWIYGiMgpKCRR0oLxMMrhKI+vBQg4s9fR6DLGSnCO4r3whN/OlckxN7ZBjMDGbJ1JtiQIpOXXYjLKLfUENKMT+L5Ap3KiQoL/raz8H0/hjtFdQjMuZU5sl09ozidAOpAstmc1OPyfYBxqPJWayfvC/wv0aLZWeX56g/sznVaHg0MI9gNZRQb0a1EDDk8Fh7P4QA4QQBnIPV72qmDtQMKWAqaxlEMwk6V2K9Acx0M0BNNhDoXXMWs1s/I2TOuWlSXqCl/oH8zpUdDnlLA0MSoP76r38LP/NTP4EPXz3HZegRW4bT5BcpMdgxGpZ8d6TbctYrndOswVHgtCEOAihfE/l60B20CACIYHCWZHNhhQEq/gEKa4qOH610WEjgVpCDcw28OhlBE6eK+7EKFGYRAprem5yDS26h+5MiHKkQDL2nsvihsN/s/lwEQUgIU8QUBa0Ysx4q9SBxFTk4IwcrQZBRHFQdyr0wH/fqvaN5/YP8uXlfmmBAQaQ1qlwWwD23GdUqxtKT8LH2fggBzCHSY6+6PaYu1AKguE3KZAB08ZFlKBK7rJlnlpDea+AJKshvhKJdq76PWgg8JjBED1azVU3AlYNkOTIjccR3vvMp3r69wfHwDUyXG6ReUlU7q2KUZPVTYpBmvTRBCKhoIVOHqivO7q98nkVR1b2kaMAtVCNUv8m/zZF4GoprCysmOG/3ZTq7nt94CCfPDSMwlcQUTUa5lLzzk+68cxNujDzX9c3Co8RgUKetMMW5AAiFD8rnY65IVCjM59kzw56GSAQrzz+fHUtz8109T4uZMJ1sfEYiWo7CJeG3JP7sfb0hvffqAEEkvvRWyrtKRgP2Xo+XDU9ntk0oe6cLW/LSuyyxi64O3UWj6l1yKjHPCJEU1GbMiUG+7DSGFJYxDksu4yl1AbqDMbMuzpoYlPeJRc9nAG9vt3h7cyeVfl48Q0oREyJ8I4UzU4zCIRBJ5hlSfz/dnSpqr0xW/cv6G84KgO1wZQecjZKhGW3JYv/tIpV3W7atZ5fjimCrSbW8g+ldWl+p92IN8zOxapMgIWeHElNfLIt8Ye4LGhFo0ZDZ9JqPL1YD41vyfaJgpRkCyM+zQEflLufYgOd2fJtTTeMz6ScbSUk+4kiEBIO1UrPDqKiBWaol1wlL6/mW0eZis1q290IIiELrZWdjiV3XsvFIBB08zqYWria5bnvyoJm88mh8C9+4rM8FAMxSVJLIIWqRyxAnCe914s7CjkC+BVNCqhhza3WUYd2xufCkfmbH1AVMgQTiAAfJ85fZYy3iBQISSIVTA+8dxuMBv/rr38ZP/cQ38c0f+xhjYgz7HZz3WHlGkyZ4JHhJJAWHRioPZ+jO8DZxGXmxpQikKYGmKFV3HMNxAqUJPEmxkWmQIB4JJWYkdkhJ8gwYw55VMEdwDmCXwFNEaiak2ImJz3Y06GLS+yBf+IdCwDJAEvtP4hwp84JYSLhU+t1B7tnpguE4gcMADgEcJqQQ1PdDS5XFEVMaMfKEYwgYI2NMwGQvBiZmBAYSS97FkAR5mZUgghERETlK1AdxdqsG5qjo3N5LTnwmiIBxGuH8BZwnOA90vSzHcWC0ledpo0LgeDii7zp45yU/JDlxaBsnDOMAYqD1IiDiFMoGyJWgPtPeDyFg+5Dq7jDYTJwXuGTXmZMbSzgunzk40ggu1Y31XaVaKLGmsFOgJxVpn8uCnZpxHif8kD+vUYJJ/ZRS0Xm57BgZ32SYLEIuJsaEAN8QvvjiNT5//Rrb4wF9L2a1qHDbIUkmLAKQHChJ2nVHfrY/2bUM5jNDVIjEoKTdzCxEYuIM40tgDTTSUU1vSXPtR/Fuc96BGip6dJznSLD+s/x31Kh1IKOkqp8ZSF4Z8sRILgExijUlpULi1a8wiTVBrQopBSTNIyg1CwNCmhCTlCubUpIFrlaaBKk9kC0BkM/Mn6Ma4Grs1RwI3bzObLb12isqWplDAvWdcgVUkBhzVqfkPIQYAlLlPBSjZJJOPoEwn6d5vtltn95abu+JECit1jlrptP0oqWNfukgIZ5xc37BjpfFKGnEXONzyKlE0FncgenUrtrB5/d3Tuc6JxBO7L9ypP7f9Ed7Hv1Wdc/EESFFrBvCbrfDZ59/gc8//xxX62+ib3w2dVGlmsikPE3Znq/N5wVnvn41a2aQvSbD7Hslp2IIAAENS3HR1OiiswVSqXGkKM17n4nBWhXK/6c5W0LQhVlB/DBNSFahKEwZ/hduoDgDSUBQyRZUH5eMw6hSiIkKmjJ6qtWCMoRPLavzrZ439dw2PxXrVxNcIYSz4cB10JoVbq1jDIxnqKNdn2rvlRBYkhuz8EoqHla1o8Uywsri30xfzefyyjKDkFKEY61O693JAq2FQJ1NB8DJYM1MRsCJ0KkjwXR9z/5Wl80tm590hxnGCZ9+71N861vfxk98/BG6ps+6rUfhKZ4iUpf9uxSQc8LPBIoV+KwSn9giyf4Ak/S3nxOiYWrlHqvFZBAoey4ap0OlE1gxrPAIJceDRQFKAlipQRCmKddHtIKkc0FQk4FVtiAlKnPcQOYrqmesozHzoq/7F9VzvbvNyFrd4Nq2nZGC5bvTTaVYtuIsLsDyYNbOQ7ZWltzUY+29EAKM05utIbUhAAucWKYGq918SfVsYs6/B5B1e4t6mxGFNeSv9NPlosnHVLtp7ZxUmwaXwkpPAjaIieWGYjjdNGeZOFNI6LyoBL/2a9/Cf+O/9ndI6G3jRVet2N+sdtgzVw5N556lfiadedUir3bL6j1XQleyDgWkFHXn8XlnbbzorZk8hC0c5XZYVAyrxmz9kxeaug9H9R5Evo+IGKcckFUni42ze67zA9SkoDkEzQUGp5QXNmcOIBVhDEM1hTBE/nyOeOpWD7/xNNbvFjdQl7Qzq1WtStbowPxZiiAO2cxdb1AzgVON9bn2XggB04NqB59zr6UQMMkH1ItRGF6CSNTaFFOfa5omjAqlipQWW7jjOQtet3rXnT3CYgde3vsMOdiEYVTiwKgkKyyhi1hNVXcPB3zv0xt88foG3ccfoOMVYgIiV1mQFqal+u9yJ6jvV4SfqCCUxC6RZovdFkxh16Pa2QWOFugaozgIeXIYr65ldyUo9OcSlUfqqwGvliHOiw452WfI+j1SyELACIoa0p+gAHvVyUJmn1cCgNUtean61Lt9zfhx3YfzefxU885loeCcw2q1mgUI2bPYeWorgo1lney1Hnebc8vPjFt4qr0XQqDWP2tJWS8i849eRlQBBQ0IQxyFWYZBsFrHm0NZyfyLbGko18PsunUnn9v1i05+ygnM7k8XdHEXxnziqCmMlMxkTlh1rVgwGLh/2OKXfuVX8OLVc6xXEZ2LSK4QQefUgXMw0O7ffkOkZkpFAMysu+yElEIOpjGdOucdVFfbMg5Ttr07R3jYbXF5fa2l4SrBxJVRjU3n12Ah3fEN/pv7cAoWbVjQSYbwydyIy2KfLXw2L8F54pCUjLyc8x2LmTlXEWafVYLiHS1vSIpI27bV8m1t9iKsTauZ33JuNk42n5ZIwca6joGx4+t4gnPtacbg/1+Naw+60ubkiTvLCSz1VBtY063smJowyotabd8u7/qnZOQS0i+JvhphPKaL15xFMp/2RyZPpseotkTIp/vDDr/8y7+Ch4cthknz/nHZ3XI8gAFYQlY/Eld/F68Mc2vInUp03QxSKyQteQdV54fprAnDMGG72+Pm5ga73RbjOJQFrCiIQCXF2kLNEBQwaVixqANZKOm/LfjJFs2M7NP0YNYvwfwEFmRhnU+wdlGo5+UJFDCycAEBHoPbVJ3XOSnzZh6Cfd+rqlvNIeVjiCijhCUBbXkJaoc4W/QmFOpU+CYUHmvvhxDAfDG/i9R698kKvAfKIpTOE1hJRJV1gFQQcf5tLXyeYleXBOZjLQuoigGZgYB8txlrSgquGNUJCNjtj/juJ5/j5u4BxykgMrJXW86AUy30PH1rJFT9l5nx/O9YXinkyTMn22KehHOEJOHBSYN09vsD3rx5g/v7exwOB4zTmPV41qQZjky31Wvo+JgAiGFC0r8hTJkfsJ3fCMwTHiCTfnEmELJTkAmuShDPZ1cZiaLzzzmB5Xxbjudjc8V2fYsDmBPT9XE+JxUBitBJSdLj26K3720saiHwGHJdtvdKCHyZY2a6tbZ6wVK1Mzs374SiTwG+8Wh8UwZhxtyeEnuPCaHHdO0aLRTddS7gKqoOZUaKTgwGGt8gxISm8yDvcRwZ99sBb97c4DiEHCtf64ePscrL70/UBhTiikQmahjuwjpQ7brMpr5ogJG6/3ICxnHA7e0dbm/vsNvtMIwjQhWjL30HUUEU8s8W/6QCINor5DRhqUIA9b3VSM+iNHPcQMUHzJh/IyxPZxvOLuvl+ucKMJzMjfl7Q522gxsKWI4TqFiWaoRpz2ghw/V5rC0T8T6FUK29N0Kghvc19LeHqCfekviqhYBVn6kXMTNnCNs0DZ49e4bLy8tsS40xSenqvoOkqOLZvdjvjY2tI7RqCGaIwEgy8/XO963FPEwoF9lTzHGkQoC0CKp3hMMxYAwRvgWYAn7xb/4t3D5si5nwTOETAFk/rHdw28VrYWW7zmazxuXlRvP5S8ky8UQSwdh4B69OPt61aHwLp4lIxP5vbHcDR5Jm7buffILPP/8cx8MhoyyvHnMpSop0T1JanFTISDZgQSOS74DhtZ+kMOqEaRoQ1TJhY5XVvlrdIai3nHplVk5pZS7xTKiYNQRANiNny5IKLpt77wKBTj0Eu67TuSLZhGyc+r4HEWEcx9nYAJB8jeOYXYptM1mqxhcXFzP11KwFli+j5s/O3uPTjwAQ0Z8ios+J6K9Vn/1TRPRdIvpP9fXfq777BSL6ZSL6m0T033nX+eVHCzPdQi9/l3mr5gzEEWUegmlESttIbbq2bYV4y2arBIvuKrH0pwEYS2hVf2b3VAuzpa22hpGsz33SGNn7K8YoOrOdH8DhGPHm9g73D/+/9t411rItu+v7jTnXWvtxXvW6j7q3u93dfiSyiWIe4gsJQkmUBBTJIVII+ZCAhJQggRIkImFCPqBIlkgUHOUTkhFIKAIMEiSx8iUBlIhEChAMtvED2227H7f79n3Vrapzzt57vebMhzHHXHOts0/d6rah6qZrlnbtffZzrTXnGHOM/xjjP67Ydx1jDPkW0AU/otx6s9eSMIj3uKrSlN30uF6taNbrvIuYMIibYtkhKAX6frfT4qCk9GyBTvTWk/vVdz2Xl5dcXl6xPxwY4xTJKRWRZhgaNmBRgZEwqIsQEuVYBgnDkPCFqfSXLNTFRpGNqzIGIyyX/aQQpvLuG6C/TDt+no/iTU6m2yLvao5fMY9+6fW4ucmVt6UsHLNQlxiZrZ/nsbC/3b4DAP99jPG/K58Qke8Hfj/wA8BbwN8Wke+LMT4bnpx/x+wClRfi2N9LpaFVdQDzzCtQF8By+UPf550pBENizfyaE0XCBACWqPryuRK1XZrg+lgXoYF2N63NyTQHrZnIoU7RyNh+DDx+esUHjz7mavcGF6sKbWCagC40XXgIARnH2YJ2yUpS1NxP3IOJt2+KMoQsRDDVrvd9T9d1s47Ec0xEcC4AWgbb9T39OLLbH+g7vd7OwoFhugbT7juiqb4DYewJY8IBUrQgYzrjnHVo2TA0G/rJmQ8YybBkQbbLbZuA3UxNZICWUsGUUYHSHS1nzVIu5i6ZXccYFu3qgRtJSsmdsc/cFpKewuJx9tzMbeb4XlOOT7QEYox/F/hkijAdPwT8eIyxjTH+GvAl4Lc/52dvCPYyKvBJn50rj4kGOyuBIh47hsmUDyEknkH7nXkdt02a7eylgigTN0osoLRC5tbA7QBShqNS2qzRUjkHOO2hN4yRJ0+v+OZ7H/DkydMblpE9Xh5PufCWEY2p1VifgLmBYehp2wOXl095+vQpu92OruvyLihiPAOTma31i1a7oUVahnaHqMpMuQYTw1PU89XXR00QsuQfSwYaes0OtGNLSiHEMTUFWdxY7OxRlecM5CuuuYX7yo7UMdpanAR+si6m37lt7dn6mx/L1Jei9PeX8xXiPBS7BGVL987WYWlVleutVDrPAtZ/PXkCf1RE/mOUSfiPxxg/Bt5Gm5HYeCc9d2NI0XfgNDXGTM/fuC0+B8zdghlwott4nthMr5T58SfkeFICYy7ndH7yq0tzfincpTVi7y8Vlfnjdow6YbZ47HPlzh+L3Wd+vpYBSbIirvcd3/jGN3n08WeJn3s4wwLK3cF266X5WCrWvldi0L7v0zXSRKrLq0s+evQh7733Ph8/eszhcEiU6eSSV/1Rshlt+Eo+S3FsN1s2m62i2BPupTu0WR1ppw9mEeSoRNr5B+PvKyID0TLhJgBObIeOqkBDLKImBswyl2GjEFtaPxTrbKkITHGUw1zL/Bn7naC5KjYvMLEDLzcLOx5tgjoJeKmsbUMr5aMMH5bKIYQAFuF6hlvw7QKDfw74buAH0V4Df9Yu3ZH3Hv31GOOPxRh/W4zxt203a/3wcyqA9PmZdiy1ZMSSM4owXxGO6rpOm3Ok3T2EqW7b3leau0eO/YZbAjfJI00JTD7wzetgjEDHIgVWWRYi9KO+4msYQuSb73/IBx9+lJlnl67KsXMorRhLWBFRod/tduz2O653V1xdXXF1ecnV1RXX11fp/pr9fk/bthkkNY4Ayx3ouk7dhW6AKJyenvLGG2/y2muvcXJyAlK4SaMyJ8UUNpUY0i2i0ZGJRHRWw1AoTqFQAIYrLQR/HC35KE6KgWNRkkl408Uq5rpQJMlULw2CUnnc5oPb80vQm+JYTFFZ/sqxtT4RoQw3LIESa3mWHC3Ht2UJxBjfs8ci8ueB/zX9+Q7w2eKtnwG+8Tzfeexgy91+6fsAR5WAM1Mu6k5kPjBiu8OkdW0yuq7HV0wLIaogmqm/PI7Jd54LWtkP3sC0mbtwKxaQz1ivRX5dTWxbKIjGhbu+5+nVFR9+9IiPHz/m4uKCqtaCHUmRCXFuyhcIIeePi4gmrHhPVddUw5C4AQaGvqdtW3a7HVdX1+x2u7S79+z3Ow77A0M/EIPyMq6aFXXVZEBQCV0E5yKbbcPp+Wu8+fBt3nz4kLOzc+p6ito4FyaateQHT/ehMPULru8C2kuX5+gVtHkcMyEpk/Cn65BpzsvJEMUAZv55cQ3LBKNnTOLRQ1MQ1c/ShHUNz9eXArIokxLH5UI/N+FSS3cAprWNc5mN+rbx7fYdeBhjfDf9+XsBixz8BPBXRORHUWDwe4F/8JzfOft76eMud96Z9pz53pN2VR9Ufa8xZcHFtMOGGDMY1vU99UoXjNXQizATYHMP5qju5BYs87yXx5l3mfhMLTAbIYI3LgT7VPr9fhj5+OPHfPDBB7zxxhtsiuMS5/BM5mHedaICiLZ9zsJJQ0+377i+vuby8pLrq2uur685HA4c2gPX1zuur64YOm3h3jQNcYzEFSnppaKqtBFpXVdstmvuPTjntdde597du6w3m8kPNhfLzjO7AqUlM+bwILeZ4Md28rQrlzkUJsjKQTi3APKxiGSOxpkiWN7C9Ht57ULq/TA7unSNy96ANVWKttimdmPdh5tQ3jHrwtZl6Q6UbkVej5Y7E25PFvp2+w78LhH5wXSmXwb+03RgPycifx34ebQ92R+J30JkIP3e7ETtvtRy5TgKvEXLgQ+z95nG9L6CxDQM0Hc9cwrqMcd/S8FeWgJlmXF5K02/8vhiiER5th5QoFkX1GgL1DlcwWuvTDHC5dU1H374IbvdLsehSwBwHJMQFd1pYwgMC8umqirqxQ5liP1keqpvqkKu7dbqFG7V7Lc1m82G09NTTk62nJ6fcufuKScnJ5ovkajLQwj0Q69cCOk65My+bLYXYb9M5Z2uz0KpTsJfKJCkMu21YI+Liz5rNc5NhH/6npt4wPTdedHOSD0m4dYNx65r2XJsWueL9PYYMs5i67bEeEqr2B6X7ykjVN575Zv59QKD8VvoO5De/yPAj3zS9z7j8zfu7WY+9m3gYPpD+8qPmoBjIUAFA0OeZxH1S83XtJTUkICqsbDc7beXUYryd8tQosXbS7dhpt1L67YY9nu6s9jCBe9Eqb2i0UyDdxVt2/Hxx4+53u04OT3T/oTRavFTPj7gkgCLIfEWUmMKhTbNiotzl5KmVmzWG+q6IURNlPJeW43XvqapV+qDG3uDOCpfc3Z+yp07d7g4P+f07ITNyQrxGiVg6AhU0OtZjkPIvR00p2HM5bszPkIT9jS3xxSANQwt6b5N6PNunzAHa0RgyqGYkkno87xP/v70PTG5msW8LaICIkIuBkvdknK3IWeZrKDcReVnrYAoEsZ5uHmZKGe/a68tw4jZEtAD+mcWHfgNHWXYzU5mCXLc9rny5EMwk8q68USiBJyvqIod3DlH33V0bcd2s6VvD8RxpBbBETnsryBcaH+9tgXIiG6Zuw3Q9/2sEWnZBMLOQ90LrbHXBV2chO0Edg72yEEfFbRUyjEVoLqutbOwb7geIu9+8DFnd+5z0awAIfQ9Ec3wqxB8DPjQIylO7qJo3FEcwQl9OiBxwma9YbPZcP/+fXY7LQL64L336bouAakVfdcnUz2VtqaowOlpw+lpTbMGXwXqJp1VtPdrApC2RIuMY5tqBlIqcOwJsc/hPy3lSGSxjIxJ+DXuHxkj9CHQjSP9GBjGSD8qcJoVSgwQBlwcqOKIyIi4kQ7lcxwRxkSXFnN6sd1b7kJqi5byNnxCckWS2yHCWKzhaUS0CxIoZd0IEhAJ+CoSYq91IW5kDC1td9Bzj1NUyiwI24gsg9DqCrquIyZFbWt02fi0Em1Rf9t4aZQA3PRzlgk4yzAiTCb3/P1K2ql1QYmDb9pPZr6YiGYSDv3I0HeEccQ7R1PXmWiiRFkNCS+xgOXkl+FE+73jYJLcRJBuaIj599o5S+Ls3x9anlxecji0bFYrmpSSG1NwfBwG4gjRml44jenriU1+bgghWUba9rxZNayaFatmxcl6y9D3qgScp+t7Doe9MvukTkPDMLBer9hsNkqG6d3ky1u6XcqAlNwebNr1My5gYF4MeXePMc6qJHPcPJLKhg23Kasj5/UOeks4AyHRmU+W3oTZcCPxKMy+w6om8wym85wsU8k7/VTDMoVqrQHJsnFpKm1PWJTMXptAwKZwwcr8geWaXuIIz4oPvJRKwO7LkysTXZbJLvZeEYGgdfhGvx2xCQH1vyZQL4drxNN1LnWkGbLvOwwD4qbGEECRWDPQNM2tYODyfmmxfKtDFxOAXQt1cw5ty8dPnvD06orNek3l10TnEnFqspREiUDNdzUTGhOycYQxEEe9bhWexlVqvay3+NTFp661LmAYBp48eUJ7OChmsNKdx8pjLStzHMz9EvA2xyQdZBwCamKHMGqBUJgLf57fQdl9yzDdaMojFPUCM3Bx4c8X30dmLT7u9y8BxFnZdYkHYG7HbXNWkuG44rEV+iyB7WTGF36/yYFhChbaLUu9S1ygXI8lfnDbeGmUwPJATbBhHlstY6zHdlv1x1zWvqYE9HWZactcb+AqmmaFMPnwVlzkm1U2x4AcCy8tgeV52JiHbmar5vjjxSgtntIayZmPRHZty4cfPeKDR4+4uDjnRDba3gvdJSVGfKXIvXcu+ZtGcJLcpSSIlRYqEIeRkJRN4yv8Zpt3InGOwXuuRHI1YF1VmUXYroGCfWkhFqFWcQkcTQogqq2PJQKFIoxr92MMDMFchsn/zz0CEnZSxv+nZMKlApjwkPjM9xTCafiKWQEzMPHYPGZkB9uDZ7Ut4m6sXfstJ4LzguCzhWnXvkzTNgVgrmmZrLaMPHxqlMAS+DgGui2tAfucLGZDk0hMo+pzunNO75uUiF7o7XbLer3i6VNJ4UA1t6pCCIF88eem+bIBaqnBExtWypX/VoyBJQ5iCsCSmsYY2O33vP/BB7zzjXd5+PBN7sgFUdTcPnSdugep0k+PZWrEOpm8pHRfR98PdMOBzjlWqzWbzZqmWRNGRfW1SWhk6Hq6Q0tIVpex82qVpVoqDrVAXFUpd0PwWQmYK5LnfSFskzswlf7m0uDycwuQ1Z47plyzsjCgjzLkF2dCP+tqVOzUycuaRFxIyT0FsJj8ihhBZFrXGkZN9f8jxXpQq24YRryDqmBit3VV9i+06kBL0FpGqsqOWuV53TZeCiVgKO5SoE1Qy4tQ3pYKQ0/UfDD7nvIzmiJsw/x7ouP0dMvp6SlPnlzStl3y3TSDyFDnY5VedpzlMdt7y7RhzDeeIkN2eMuLceuY1VGICsS+7bje7/na17/O93zPF7l//x6V9yhA2OLcmjbVn3vnMmWXChwpV30k9CNxVB48L9oq7Xp/yfXjS5zXGLerVKBFhDgm1HzUpJw+kX1q3wdFxSszf+tKb1Xib0j1A1C4cjApgSTIs5Jg5j78NO8l2sONv3NIV7Q5TSBOkZeFuW8uSsYlsvCHYq6LaYrFbzjt35CV/I37qCXJM0RfF4CFRW3TqMegtSKFy2pgc9/37Pf7LPTmDkzf6WYWwZKT8Nh4KZQAcY6sLk3gpQsA3BDE/H6ngSt1C5KmFgNmrGnGlIsexkAfNctuu92y2WzY7fbJ92oYUk788nfKY7hNCUwKI51bnMzDTxqlsrPfKp+PUbvsdhLpDi0ffPQR733woSYOrdcaVkQR8G4Y6EOgcqnjj6XQph390LZ0+5bQDqzqhqquiDFdJ6CqK5pVg6sqoihV2OF6T992edfuh9TPIXVIJsKAgoy+rqkaVQLa70EtA+eTexJiDl9iwpggRX08rY2lr1+yKKWfnVlbEVQBRAMLbfcnz43dW/ZiaQXYc+niI7l7yzSVTsBhbdKKdVLcW3lVnld7JVqNy/Q72e1YzLfVeRwOB/1dN+9LaO+3HgTHQOtj4+VQAkdGqQjKUQpjeeKl9tM228YjmGisCrN9BjhWPicJVYlvQDPgUsLN0OVc+bKqsVwk+XeLCZsAG92X5FtwBcrEk/I8S4VjbDkDgX4MPLm+5uvvfpO33nqLOxcXGot2niCo0I/KDhzHoD7/GAjDyGF/0ArBfYsf4CpaqbNjtWo4PT9ns10RA+wvr7m6vuZ6t+P6sMs98EKM9OMw1SM4vf4Zj0h9An096N91IMYaH/0kmNmGn5D2BNEtzGzzodH27sXnb8sSXPr7ZmksFcp0KyIWFimQxEXpQnIpYk7qEexWbhR6804jKt77HK/3hWU7FASqguCdz8dQbgJmAZgVUKaoL7EjUw631b4sx0upBI7t9qW5c+zEc7NQ75CQ4r6mCJzgmDMNxRipqprVKrIfD7M4qzG/mMIwJWCodzkBS5dgqWgMzBLTAsWupT9yyzV4hsVgv9v3keBGunGE/YGvvPN1PvPZz/LWw4eIg8YJQ4hU6E/3bUe729HuDwxtx9D17Hd7rq6uGA89q1jjJTErOWG1WtMdOrr9gb4feHL5lMdPn3C1u2aMqWuziAJ3w0i9alivVolMU1hVtZKFjBE/BtzgqerSNDVMIilKeyR2iwSJWQmQgTxS+7CYLYn5LeTXzZqYK4cJ0T/2OgvFkGfEgYupDDpAcvZyIVMpb0Zt5/2UKWhzZ0xOtrtPqepTopmtGVvvXdex2+1o27boYHzcHS03xOdRBC+HEpApTx9uugOl4BpusLQUssmPR7R1XVYEVkY8tRubIgB1XXMQpXEiSi7ztMQMK7Mt+8cvTfXy2OcKYFnV90yXf35Jbpm46RpA2w1oIo4moHzzgw/5yle/xmfffovPvv2QytWMMapgE+n6juvdjt2l1gDEMXB9ecXlk6f0+45m8GxWa3zlicD+asfjjx9TNTXDOLDfH7je79T0FyVpISmBMYzUKdvQpzTkk2aDrxUTqBp1BVztqYeBTYyZ1CRKmECyOBVbZdUg2v8wokVHY2na60fyjj/PFSh3c3tPGRG4WSNgQGAJBiquFLNijnnNqkUiItrHUfJyzkU7zimrsrIOSc61cDiGMOTqRqIpfsmVhKYQlmQuJUhYgtK3Zat+KpSAnXDpvyxTdJdhj9nnC6F0GHYA4yiEONVfm3YGwyBs1w4Z1TbFMI4jbduy3x/UL06a3PABy+SyY7QJMu1eKgH9PV2E6YSfiQuWiSI2SismT34I2meBCOLZ7Q98+avv8Nabb3L//n2aqiYiyrsfIjhH1TSs1mvW9ZraexzawbYLXiHrKEhU0E5Q0tCxS6xCh56x165CYwwM/ZgNdhEhjC1jH7QgyVf0rsN5R52UAJUnOqiaipOzLafnZ6zWK1zlEJ8KpaLVw8eM6YBaBoFph7ZrZW/KgoxlFM5zDrILkD45jwSEuRLI5CVGBgkSJVsqtp4QrZiMAs6UC4lizBlWkNyEGPDOU7kKhyOMgaEfshvgJCW9jarorD+hbUYGMttmVPIGlOuxrF4ty42fNV4KJWCjFPTlbgtzjn+YC0n5+TLjq5y60qcszfZhGFit1llTG9lGFvikXbuuyxNQmmSmhUvWl9lijUXY6vlwwdlxHrOI0hmDWMw4UHnhyZOn/NpXvsqbb75B/fnPIZuVLlIRmvWa2lecrDb4mIqUx0DoRzrXgh+pfJ2TUUIM9Il/oWsHJciIkm92zX3CH5x3usuJgzEmJqJUqOUgOBjiiHjH+mTN2cUpF3fvcHJ+St3UVFUqmy4gtmgAb7IaRnGQ2zDbddL7EKFsPKJzYZwCk7twzOTPYGCRvz9bX5L+i3P4L89NjClUah2dJhdBEjjNYm1mWjQjh0S/OAYFQ20dmTtamvfzdTBf/2W04JjLsBwvlRIAZkK+VAS3CcTMQlA+CjBwJ/HGTRx2YfYZFSA192OYLJC2bRmGPk2mmlbL/IAyiciOacnoW47ndQVgDgIei4pAMh+jMI5Rd2BXcXW14ytffYfXHtzn7t0LKn8XqT21iKacrtb4raLZoeu5vrqibmpkgMZXNFWTyUXHhPrv9nu1kqqKuq05tG2G6ySFAZ1Vx2HAaE/X9fRdp+HDODJEjcREgdV1w6HdI95RrWpVIN7hfLmrJ/U9m/dEBismyDbPOtfWIEUbkYbcrXi6KV5wQ/hLy+CIgJXrM8a0u+f5UQwgxlSSIRxds0vBNCVlvREmdsOYw4G2UdmOXm44x9ZM+d3lOr/tfOAlUwLZpF9kRy2Bj6UCmKVMjmgMOwE+Yxiw8I8Jp4VVrFRW6cUqhn4S8sPhwDCMecOxz5UNJJcpw7r450ogC+/yXG+7Brdck/IaxDjVnikJjwpEGCP7vuP9Dz7i177yVd5+6yGbpqE6P8XXlda8e09de2rxdBHEuVws46sNdUr7FREkKFglztM0K7Z9Tz8okKXxdN23JVkCCIzDSN93dAcBNzKgFkRM9RsOAbMYEnJuPARNU4ODMQyMQ8xJRWrxuyRcxocgaY5TroP58uNYdCEu+ibmRqSBcSgshVCwUuWOSsdTvM0CEFFnaYriJBwgKYLb8CqbO1uv46gJQtrrMZntlZ6rrdky32S5Do6loi+VQLmObhsvjRKY+fVHagRuwwRK00fNMjOrdPe3PnpGGGLfMaS4dgghEWbOteUwDAmvnvwvOwYr4jBAcEkjVmrudCjFiX5710S/Z27aKVV3TD6l9VAYEALvvfcev/ylL3F6smG1qqm9o0d917rx+NpTB8UH6lVDv+8ZYoAk9DFlAPqqYr1VDsh+GOj6Livf3NgzqsDGoC7AAUECeJVi9VMlghfwgq8rmu2Kk4sTLi4uONlstTln47WKbgiMk3WcbxNH4DybbygUgKZFT0lIGkotAD9z2UJMFZBzPkNrTlIgDkn6ZVIA2QgoLAFIFWsljpXAQEk9CCUpjoTRjP3A0A2MvYYJRRT4cF6I4Yjl9wyrYlniXo5lmfFyPA+pyF8E/h3g/Rjjb0rP/TXgX0hvuQM8jjH+oIh8HvgF4BfTa38vxviHP+k37ASPCf5tkYDyZMt7IWndhZuwBNlsgQA0WvOaX8tWCJLLSi0GDrBarTJQaC6F3ZvJaQsl/+xkPdpWkh4vLkTCDW5zAfJkiyR+QqFKfnjbpfdV8OTpJb/yq1/mwf17XJydsq5rfF3RR5R2S4SqqdmennByespwGAh7BflwIDitpmwazs7PqbynGyZSUpiKqfpBhSdIYBTFGjyCqxvGMYV1HUit0QG/qlhvV2zPTthuttTNxLZjiWOlSxCImXy0LJMdxpEhTC3SLR16LuwlNjBOu/8QsvBbrD73Vjxmt0kW+WLKSksg4sVl3z5HFZhyA5x4xQ0Sv2Lf9QwpRFiGv508m9tydmS3bBS5FqEIF942vq2+AzHG/6A4iD8LPCne/ysxxh98ju+djaUSOPZ6qQSOheogLV7xGLocKfu+zy+Wpas2TZNTXqtKcsJQ348Qhqx1l8d5I7R0JEXTQEGzYKMd6yf4A8cm1753SqEmWwHmDuhGE9nvO95//yPeeecbPLh3l5PNhur8FOeFIYz040iFUK9WnJyd0l63tIcDXd+DWKjU4+o6ZfhVNE6yCQ9T7Pp6tyMcDoTEU9i3HX3X4xIXgBcPLnEbVoofVFVN5VMKckgMuz0MIVGLh+RymMD2MPbzLkpDcvH01s8UQNmBeKYA8u5vxJ3mLiTrLVoIoYT/7K/0/6TN01zFoqmtPj+Voc/XDBgVfp/p8PV3J/WyFPblZmfro6wRWAKGdq4wtxKOjedhFvq7aYe/MUSvxu8D/rVP+p5nDuGGC7A84WP4QPl63ikL7asoreEDgXG8aRWIU6UxDAOVb6hrz3q9Zr1eMwy72e+M48hqtZqZZ6aMph2qEPrZMd72xy2XZKEAynBQft08U3HqU0aNVYcQQFRI3//gfb76tTPOTrds1g31es0wBtquZRSHryq2Jye0Zy3jDtpDz6HrqbxntV4RcYkFSK/sar3i7PQciHinBCMEpWhr245239LuW/p+wLtKeR2FFB7TYiaXIgnip3TYUQIiiuGM48AQzU9PxU69MA6hUAKBIZZKYKAfjoCAN4DBdAtzOjntZWB1IqI4R47ZU3AIzmID0/+F8gYIOXoyty7NNbVqVKsXyEpCJGEX42yjWa6NZVLRUgHYeoTfACXwCeNfBd6LMf5y8dwXROQfA0+B/yrG+H89zxeVgr4cS213bGRwblSgyFsVIT4XycAUErSdNEYtFOr7gVWzpmlWWQlcX2v/PDumYRjYbrfZXM3mW4ENlCXDMU7ugB3280QIjgGBy91AxExUNaFNUEHdl6quWG1qLq8v+erXvsbdi3Nev3uXbd0QnFYS4hyb1Zqz83Nc8Kw54dEHj7i6vKRNdQN9PzD0j1QBJCzkm+9+U/38cVRL4Po6U5+PwziLewuOyjmquqZeNbimwq9qzVVYrbMCFQK4SIxKNTZmfsOevuuRoSIMJbibWqwdEfASL7hNAVin4ikyUGQmpusaWaZ7i8EDxh6Wn7PPFZOYI0szhZ6qBcvkHygUBYJ1hL5t7u39yzGFRSe3yRTPs8avVwn8h8BfLf5+F/hcjPEjEfmtwP8sIj8QY3y6/KAUzUfOz07w3jLyIIMyGRWeOt5C4W+Jx3vdCXM4xCXuerRWU5xQVY4QHMPgcU6JMUBwriKESHs4cDi0rJqG9WatdNlnWz744AMCWlvvRKjq1JzECd2QkoNGRauHFIs28y/GZXqwJLS7eOoWsOY2a6jU8k4ECRESWh5Gpa5yTpF3LxWOGi8NXRv51V/5GqebM7Y/cIp3dXJdhToKrmpYn8LKeao60Hwcubq6pm2vFbgaB7WWYoV0Qtu22Yzvk5CGMKbwYCD6QKygr0QnsQLXCKwd1bqmWTc06xrfuFTRF4iDZQAmsCsAoyCDw41FT8eY8IGhwAMSbXoIo5KYjiPd0Ost6N99GNW6iGr6H9oe60lgluiUpcgsE1El3wQp4rK7kIZX3gpT+qpkjIpeG7gKWtk5hkCXMgBBH0NMnZnQeo9xArstX0VEckp7Xdc53ThbHmEeTYgxZiITKVOvj4xvWwmISAX8e8BvtedijC3Qpsc/KSK/Anwf2qVoNmKMPwb8GMDDN1+Lkn2qcuEvWl5P6wBVApEY00mmsI727kuak0mDLlFWM8EsJtt1Hbv9Nc1Kw2Pr9QrnAcXAMjDoCibf7P+nysSQncBIXFAKF1jzc4/blITtcj7aNwYVnhBwrsI55SD0vqau1zhX8ejjJ/zKr36Z+3fv85mHb3J2cgK+Thx7EVd71qdrfA3NtuLkaquU4/s9bd8x9oMyFYeR4MYssMGNSAMuqEWiidsOVzvcpsoWXF3Xyk68qlWZVkK0WvsSCDSTNmrkIw4RRskl0DnkN/STAkiCMgxD6n+oz/fjkBVAH4bMcmz31owEmJn/xQxoQlVk6mSMOQT54s9yBmbmH4WZn7IvQ1Q3ZBh6kMg4qhLQDUmAMVsO5RqblZEzRaIMFyirVktLVY+x7H51c/x6LIF/A/inMcZ37AkReQ14FGMcReSLaN+BX32eL5t2vgn8MwVQxljL++WYBN2abZB9Tr1NfvtUZWWdeOD6+hoRLZzRhVsh3YBDqw1LmjGbKGOGnR+TKYDbXZfbrsFzjww6FeeeFqb3yfxuVtTNCnGOw77ja197h4uzMzarFafbE62gjBFC6rOwqlnXFfV6zfb8jIu2Zb/fs9tpN+L20DKklNqpcaYm1+hOKROtW+MJa11eNodLuu2wEP4y03IyaydBmHx/FfZhGLRMOkUohlHblg9hnCUJGW1c3/d0fc84jEpDN134tF5KwS5kudDnx2bWXDPya1Yr4PLNznfarQPiNIwNExltCBWV88Q4pahbXkpZvVpeq2OY0bTGnmUD6Pi2+g7EGP8C2n34ry7e/juB/1pEBtQe/8Mxxkef9Bvpd2aPj0ULjvk2yx1kIpNMptmoVYCaATjl/dui836KBlxeXhJCYLNRxNYncg4zxUowJiYUOYwhp3Uuz+eYsE9mrb1xfs7La3GbwrDVmF2P5KyKk9x9uWlqzQYU6IaeRx99yKqpeeP113njjTdYh4CLGt7GOYL3OB9xlaNuKmTd4DYrmtMTTrouJbbojkoBuoUiIuLM9/WOUM+V2tJ0LRduuYvZrYwE2N8lq44Jf1YA9v44txqsFsA+H8eIv2Xpz2C/cnfXJ47+7RTRSE85rScwsE8mzCArgNQIx2FrWguRhiFQh4aqWTHGicOiLAwqFWIZVlzepgV0fPmU49vtO0CM8Q8eee5vAH/jk392MRaL/1jo7zmOUxfRGJUeOk7FPGUdgNE1d11H27bECKtmzcnJCfv9PiG2IVsRGhJaoruWLDPSDx1d1xfHAIWEYn9BQSV+9BLMz92+rzy/G+ds/9IPaOqtlq2qyV2hrMSBYQgc2pYPPnrEO1//Op//3OeovGOzWrFerZDK0zsy83AYE8V3VVE1DWuvuQIC9IO6Btq6zBJtSvcoMsaRbuxmi3K5y8PUcPMYGFr6uGMh5Edv6fWw/NyglOaWA0DpFnITvxGUcn4S6qyl82dn67JUBDkcqOCPFQHpCY2MQbJSCmFAZNpUQgopjjHQeIcPfrY2brOUjq2Vm+vplkWXxkuSMXgzHAg3hWAJlD3zG1ONuykBC+X5RJVlRKJd1+NEw4InJydcX1+nBZfe7xySuQ011XNp1pWTUY45KnD7OKYAjp/TQpiSX64+gFJ2+ST8JKDLQlwhvXPfHvjm++/ztXffZXtywmazQbwnWEKPd7i6RjIAlshXxEhZHSvnlKl4KZyJgnwYB/ww4IabRVXl7mXnXCLhy7yIpTJYCn+OBiRMxirxxvJ4BrVWJKS0ZZcyS/OGPllTZs5LjJmRiGTt34axiyh9mUOxhaQNMqY1CbASqhrHhH5WV0oUVUS2yZR5ALbz2+OyD0F5DZdrx0KOthZvGy+JErh98R81cexTiwVjphUprJOLQgrnzphejXqpbbuUv64WwjAMHA4HYtQ6gWbVgPgiQWf6LYv52jEsXZoY4+ziW9jpWxm3ugMxFUcl5YaIUnelrDznlI14iAGvW16iGht5cnnJO1//Og/ffJOLO3e0LZmkJCTvcGWbrITTeGf5/hbKIu+MJpx929L1Pa7vGfuOupcbAluGrJZKb55qPVe2fQH+lY/L7MD8/iGZ/72GGK1WAMiYhYsuKwGbu5wTkBOClIDE3ueYIgezeRbFQvJriTtQeSzURR3T74zFuokJRxFXKPeU3WjuhF0vu5Vrt1QCJgM35CimJLJnjJdECdy+sy93v/L9tqPMfEy142cXpdxpbdGWDEH7/QEjHKlSNtswKJPQOghjtPxryHh8sJZgYXGsU/7BzXOKiYvudrjmWaDnzef0PzP5xKkAe+/B+0SsmZqQEBmCVqr1Y+Dd997jG998l/OLc8RdsJIVUqduxubLSkpNdhM7jktkrZaQRYy4ENT18B76Hul76l7DiSUgN/QarsN2fr2Q0/ym58r4fin4JcOuhcj6hRIYexX+Me3+IRVX2X7oXUrtFVMCGsmJMeJlEvIYkywz8RjYrj0bMgG0OUxoa05AUldkglpioTheXZPpP1HrdQyBvu/Q0KLM1qyt5/J8j4WT51ZU5HYbRsdLoQQEZqCbjaUCKDVfqf1KvzGOA4SBktZrKvnVCbTQymazoe8Hrq+UtslqAvQ2KriGox/mDS1K0EoLkCYO/mxQHgUGJZuWzxp23MvPz64NkyLJdk4y56XyiahDW40Nw0DbDQxhpF431OsVHz95ws//wj/l9PRMIwjeI6MnuoBHiG5C+r2QW5k7EYbejGZsFSOAi5Eqkhe1oC3HTEqcc0g/4SekyMJo90tEP90sGtB3XY6xZ1quGAscICnmcczt2L3ojmy0YHPcJinrGLEyXns9yhRdMtnXvhXT/BkWky3S9FzM709chCRXKKYmKcOYrYEETCCpWjIE7SXg3BSJWm6Ado1uc51N0VgI/NOBCci81LK8Lx8vzcij+ftDTxj6bKbDxD8YIxkgquuak5MThmHk8ukVl5eXrNdrTk9Pc4Wg/YbMOPKnzi99r6xFpsCK08mWwA3AhqQmjsxM6Sc/a8Rk7UyfKUMN07XxvgLnGUJH27VEhO3JGevNCY8/+oif/flf4PU3H/Lmw7dYbzZU/YgXj19pdMEXuRvap6CjkkSYySQkMfnhcYgT8Yio8FtYq5xXU6L299KaO+b/mzVRAr2lErDNICt/FMeQSnf+GY4SjIBUr5mxFh27zrZRR8N54xxEtKuer4lmqhCZhFOPLxSkJ0teC7M+rAdiT1XJbF2VO74pwHl34+mYy2tpbFrPWlMvhRLImnXh/x9zAUqz6NjJKXhFbiAxxwv01+ziqTsA7UG5BHe7HX3fs9lsqetGLYXrA0Ypqb7pmCdBjymJnit5BqfzmZ2nragjrx0b5vvZudt9BnyK7ErnPatVg4gep4GfAgqMpXfu9nt2+z3dMPDoyVN++md+lgcPXuNf/pd+E03lodKdqg+R0WkloWA7oAOngGCJC2AMxmkXTubS7DzKm42lYihNZeuus9vtOBwOs0479p2AsienLMAwRrzzSBRGWx9hcjMCaq1EJ3ixwjKShZCEMHMXFqQu0SjR4zzCEy1DlKwAnDic05bgY+p6baFcB5k41TnRbs0JECRqPoDzmjRkyq+c8xIXMAVR4gR2LcsNUqMU1TOtgZdCCbA4yVIZHLuVhRFLC8L8Py1UmfvueoF81qjee05OTgj3I23b8v7773N9fU3bdpydnbPZbIAyyWPhBogJ5xxzmB3P/ETzjmHjeTGAY4pv+v2YsQG7Lk0qz1UfsweEumlwiZN+GAL73cBXvvIO//QXf5kH9+/xhc+8Re0cEgKV9QuwsuyQlr+PyDhq9CFtkaoAekIKHRKNCrzI4Q9W+z+mBiDJ/08txvpxoBt62q6lazsO7YFD12rWX5jXBNi1ds7hk/Bq+XjA4QkSkDAyIjgxFy6qf57mQZyfXdu8KycTXq2AALgEwEaSPM8NL8z+Mss+cVlGCwmXoDQZs9K1YgSryWR3k69RRgmWtHpLd9jmvFw3N6MsR5cZ8JIogSUmsER7l+bk0kKw50EXlSRSx5KVtUyrLJOQvK84OzvLrbjblCWnGtTN2IjK8M4nxfWfda6f6KSlscwCmw/ltEPcxEnXjzm3fNWsiEDX7XOhynZ7wsnJKRFJ6bORRx9f8ku/9CVeu3uHB6db1j6l0SSgMddlJCUoVdAwW+GijeNI6HvCMOadT1uJh5RLMZnwGSQcVfDblK9hCV1t2+YGG33faz9F5zStN950AUtkTtOZU+HSKDiZmIajmyjI1B9zeQeOTHUZBBXWYK4CghV9BCI+LlOLUfDULELRVOPR0MWMOcTi99QuiRlUiIQUllRuvPmufix/ZikHtlbmbsbUtuxZ46VQAmYJlGZ7CfiVwnvMXZiF5gDinANw8qOsyEdYr9f5IgGcnJxw7949uq7j8eMnKUwYaQejgJpKWEtta5VfxxTTtAtMR2cLIe/gzBXIMeVWLoacQRk1QcXqGPR6DUiEVVXTNCsNj40Ju4jC6ekZJyenHK6vabuOECO7fc+vffnrPLg443s+85A7JyecrNdIiHT7lo42YSS6q44xEp1L7LpFDH8YiEkJKECmXYRnPn0SeAP2uq5jv9/Ttm1erPa8vWcYhmQqV3gXj2wIuvAry0cI6nd7syxj2sGj4QFGizYlCRlgOAu/zdyBaX2FEPBHlHjGBVJuwNSpKKMDs05J9los3gfGKHQTD7NR1g6YMrDHYKChOn9TROvZm9PLoQSYazmYL7ClgJjVUJJ7TD66w3nrAT/5TVVVpQWpiyUnYoSYNfLJyQl3794lxsiTJ5eaOIRnjBRuwFwBODcBT3qMJSawPMuY/cNpcmPx/uP5EOU1yudswmCmcNq+DBB1MYUjB1343ns2qw21r3jadvRtR+UClYPDfs9Xv/I1/vE//kk2TcVqrXjIkGoE6srhpII4MnY9AxMoRm7fFQiJLjyGyMhAH7oM8JXK3Px9swBK4TdroCyccSKsm1Wey7nLOA+79X0qFS7dyzAJS3CWmcdMAcSgyUHGFuxiZLBJDIEoWvF4rMbfOB3y/CTjIYOKE+oFJPbl5TS7NP+JkDEWa6oU8nItHHOVJzfC3qdH+KzxciiB4uCXt6X5UwpCOSEZNIkxNxspQZSmaZJpKHkHcs4ldpsJwa7rms1mQ9dpIsrQG111eXFlobRmXr6d0pHTjMl9vx0HKCe8PHf7O7s4aXESldXG+0prAEJg7HraQ6vXYNRqOaVT195/bdvT96OmGHuluvrgw0f8zE//PNv1Cl853n77LbbbE5qmRrxoqmtplekBFcs7iZS5AKGnG7qsoMt5suQf2/GXLgCQeRyr1PYcUtJMqlWYRxMmJSCSwowzJZBk2bIK0+4cY+FeOmMrnnZYwy3K2QohzHojwuTKW1ap5lfE7CIo/6DLrdqdUzfDmowYoGBKveyHeGxzPBYRsHtTjM/pcQIviRKIHGdOLVFPYJYnsESZbbE51GdVM39Q4XDKlScyaWyLBGhK6eR6iAhNs6JpWtxuN/v+STjnSug2c+uoJx/iBCgVM1VOoO6AkwIoXYEb6Hq6VT4tkjHQt4qqV177LMZkFYzDSHvQ3XdI51o5l2i6ep5cPebLX/0yp+dbxjikvoZ3CK0y8YIeeghhihjYAk1g4BhHhjAwjH2m7JqEXoV9t9/lvnqG/A/DACKsNxvWa2uJ3mSeiT5FB6wQaOwLcpHCbXQu5KKmCVvS6x5SuHIMQXfrHBmwTURSC7uJuTo5Don4NLlvceIOgNRtqKx4FYcLqiht13fOZTdl0pzKAGVdSmKMKbtvWgd5nstFJbe8xtwK/STL0sZLoQTMElgOE7KlFlwKz9xP1AkfLSVTwqwC0EKEQFqELfvdQYk6lpEGyCq1PJayMER//zhYuTQH4hH1fDypaHqtPO8S5wjjqNT7qWLSmY8bRrquZXd1RbNeMw6KB4iIKgCU/y8GrSCsak9VOU42FWd3VhyGPV979+v41AtASVnUdaqrWhVLyoPP7k0xFznhJ1XK2Y5vO/3hcJgJf78Q/pOTE05OTrLSzu7DoZ1yB1JNwJjcu1IJDEOqHUgKQ5+fji0EZSd2ht0vLEk3joRgbgEgiVJ91FJfhohEyd2kVME6vOE1Ti21UYq1mhSEZivqGoyCUrFjSiBxG9y+p+T1VLqPn7Se4gQ73TpeCiVwAz4rgb7CHLJ6/tI9KBWAc04RVpl8Ipt8SxbSnWXaUff7QzZLQa2Ntu1SKrHDFX6g8Q+YEih/Py4m6JPGEvWdTP75NVjmB1jyjZitGXX3VwRaFWBXtEQbhyG5SI797sA4DPTdkPoXOtZrFb6L8zUP7jZs1jVDGHj0+BGb9zf4pubOnQu99tETg0yFQ6N2OSbtqGYNZFwgDLRty263ywBg2eF5s9lwdnZG0ygGsd1u2W63mcfRIgZ931M5ny2KvqoY6yHVCISbSmCYko30tXkRkg8jfbypBCzLzvL7RYxmfF6t56LL2X+gTUrVEjALVSMTee2a+5gAVVvzIXUexqe5DFG7SIvDuSOm/y3CPJcXJlDy2R/L46VQAsDMjymTHSIaqrJ2zvretMALn8klDSxRJ8AaQsIUAYhJLZYCtVqt2G51kp8+fcrjx485HFq8r9hut7RDC2PqTQDkPIN8HOYP5zNJfxkmTP7LGGhUgG2BTJM9czmYWwF6029MH8/fEURpq3Bph0qtrjVpJWYXqOuUUTiOysG4Wq04PzvlzvkF201F5Jpm3XByssV7Tz909H1L09Ss12v1qwflHhxJlN9DnxJ2TBGkUFwSQgX6ih1/vaFZNTS1toE/PT1ls9ko1pHYfUyAQwi5TfxY6c5uad19r3kJpgTM9fD9yOj1vRbi9T5tBE5TiCVITlm2bL5y7YlTl0DNcp+ve/SJs3IMie49Jf6EtKZEU6xLm1YiyrTsHD4p3vTNjEGbsea5FclYi3od1hOSjGHYy5GF3y/FD9oGWECSzxrPQyryWZRu/E30/H4sxvg/iMg94K8Bnwe+DPy+GOPH6TN/EvhDqFf1n8UY/7dP+p0YmcI5OctJudsMjJv8b72MydLSuLWoT+ctXbSwBEofa6rnJrcaq2rPhg1Pnz5N56ztpPb7HW0/MkRliRHv1fQfwzzmK8KI9rEzETYOP1MJBqY5626bzT6B6LSRaq5T05UUxpFIoPKe2jldYAlsS79IcMJq1dCNA021IorQ9hrFGBGauqbyKbtvHIh9R+2Fuw/Oee3eHSonNLWwXglOGtbOs/Y1J82Kk2bDumrY1A2n21MqX+VEmhC0q89QmORDovMyMDKdeULJHc4rEFvXlSYjJeRf5yal047TjuxcAjPHkVhTpGuntOFUKdgNPW7QoiHSmsAp2BddRELQlThqWEBCajEeE9X5qHkBPvVkHUMkjOr7C+BDKqWWyCjaGMU+EyNEZxvLtPND0A5O6PqsfYUXp2nOQY8BCcpKHPQTEjwSNDLgYsSljY7UKt6UR0jrWdAoWEzrMN8n2CikMnNXNEQ5Np7HEhiAPx5j/Ecicgb8pIj8LeAPAn8nxvhnROSHgR8G/oSIfD/KOvQDwFvA3xaR74sx3myeVoxQqDnB4at515RpN6dwQkuUfhJ8cRN5hJrr6e1RY+maghpniUBt283onJxz7Pd7QlCgZ9Ku5IIVG3M7YPotFs+LgHdm+UUsCcVCTGZBTJOqn3VogxEvAok1RwggIRX2+NQAZKTynhCFtu+0yaf3rFcNRKXtrhysG8/rD+7y9puv0R12WvZbQeUbmqqiqSo2qzUnmw3bzYZVo8QjdWrSEm0HFcl4SDa3s3tmOQ1zym2zfnI9RhJkbQgyKGehTEKleRADMUAVqkSe6bMyEKfErxksFjQ6pLR+2gg1BM1yHGIC4Kao0jgKoxsTGOgY3YgbSUCgNgwJQVIEICYGRTScaNEHEhjoCkKRfK4amamcJ4rDxUiQkRg9xMBoAioTGBDT39FE3hSFWQjmOmLrPUWLpHgyJoukyLC8bTwPs9C7KIswMcZLEfkF4G3gh4Dfld72l4D/E/gT6fkfj0o6+msi8iXgtwP/zzN+gxDGAgGXJADTIivPYbIIShRlCslYGG7KNV/iCHFWhLLf7Xn69Ir9fk+McZZk5Lwyxma/P/vuE8uQXXnDHZYjT4CZlSRTzgLKBcGFuUBFjplmzHndSYmk3StkYVKT1+VmHelyUHnPyXZDXXm6dq9sQ5s1m6bi7PRU6dP7jrHvU7ch6wnYZJBuu93mBqU57yJaaOuYEp6AMDFofIbApzBfKqIRr2rQjSpoo5syNScl4AkFJ8Btw3CeZa2CKSifQE2bN8MmSnxn+VnbJGZKrFQAMRKStinxIj+MOD/oju6LSFJay1W6LhJCThgy9zLEZNW7wrq3xZ0sjbweJz9mwpkKfKq0Em4b3xImINqE5DcDfx94IykIYozvisjr6W1vA3+v+Ng76bnbR9qhbefI/nTy10rgTWSpBMx8T7tquooWCooh5sVUgnu5i80wsN/vuby8TMLkC1/Sg6uUkTcvHlkeOqqAJhV8bK3qAg0MQQVXjYtS3KcQobk7GWsQwXlBvGaAxVGz4hyRcdT68+12jfjE5DMG1quKk82G8/Mz9rtrDoc92/WK9XZF7YSmVr96f9gTx4Gq2rDebJIC2HB6esrZ2RknJydUVZ3PzwqHIpIXoCHgpsRLOjZT4HkuQ8ihuhBS0VERX3fiGKXcEHyK+0/9+paxc3tsAHDJF1EKfgkglgVLy/p8+6xFjOzxUglY/oN3nhQQxLkKRKgS3be2YRN8pU1cJF3DCq8KwUkGCXMEKzBLSjPLQNVl8vPjhERle9KuiaVKx8njfNZ4biUgIqcof+AfizE+fYZ5ceyFG2pIyr4D56dZE9/44EwBlDvuzbxqAILTzsQxImPIwqWTHOn7Iaeudq3eDocu7xTeT+3Gq7oGfC4/1vi4Xyy+cirmGMTsuFDBiUSNCDnDACRPehCy0JQXLbp54kpItxjAZ/fFoifaZOXi/Izv+q7PIgKXjx/Rty3rs1Pu3jnHS2S1XjEOvWIi3lOn3d+QegvVrdfrXG2Zdx6BsudeTN04nFPLTWTRJLaYp9JNECHly0diVOYiius2uQUJMZG5W1GWe5viLpWA/W2KoLQMlkI/JRtNCsWOYzyyLkuWJPF1nl9BQerOp05LMtHUjZSErKpcJE6MRqnOUU15KVLLDXRNiKApVLNw8/Wy30cYmWTmWa4APKcSEJEaVQB/Ocb4N9PT74nIw2QFPATeT8+/A3y2+PhngG8svzOWfQcevh7tQJdFM9kUmj53VCnkxRFFyzVjzHn+5hr0/ZBLhrVQ6MBupyFCo3WOkVQ7L3hXMYR5ItNykdhz2fRaYgHF52JyWSze78QTY4pepHzvqQ49LTyhAH1IoNDkO3rvWa9X6kPGQO0d2/UJn3n7IW+/9ZB3vvZVuu6Ad9A0FWdnJ6wqdRP6ds9q1bCqa1ZNQ1VVrFarHK5bbza58lBj4GbiS1b1+rdLnZ1v9pKcK+4k49GIXPUEp++cXAvLiTJBUxmQG3NezskywmKvleZ8qQRKYg5TBEtLQ0QywLccWQH5KmNFVkpcVZ7KC72QFZgkd1CSBhQ0UqF4g0NbeYyK+0RhHK2IPZATlGz+C2xMbJ0V3W3MESuvk33bcjxPdECAvwD8QozxR4uXfgL4A8CfSff/S/H8XxGRH0WBwe8F/sEn/Y4rfPajxwHZ17EVFNMFNTzWO5fj1rYDGDBo6aXWvfZwmOLXWltQpwVXUj/Jwh2ZgB9bcDbsPbe5XiEYiwwpVKW14yTzXmJMnZMmP85kQ8NJAcYxHw8iOKeItsaUQQh4X/H666/xvd/z3YzDwOXTx3SHPY6IF2GzatisG5xA33fUtYZCm9Uqh0zNClit1jkkp7XuPrUtL0NPkyuw3HGWCmG6TssM0bnQuQSSii+ej4pQ519dWgpFNmXf9zcE2Xb5Y9WkphiWeED+/hAy0Ff+dv6eFDUJKQTonKe26IdL85RcUpd+N5IUvUiqM1AOSDPtQ4x4L4xBIxHZ+C83QZgpp7h4zdl7fgMsgd8B/EfAPxGRn0rP/Zeo8P91EflDwFeBfz8dyM+JyF8Hfh6dtz/ySZEBNZ8nzrVpkZWxccnAitWpZ63vVGA0PdRCgBP5hAmOKYIQIl1KCLIFo/0ItUGJRQjKBWMT7y1MGKfIg5rBgbIPYT6zWJj4SXvHclcTNG5taHBGiSe0N8bIMA5aBpuWg/OeyqlrEVLzyvV6zenJCZ/5zFu8/toDfuanf4rry6eEUUG2MPYQA94Jh8OOq8unnGxTBGDVUNUV682G07Mztilrz4Aul8zbNEGYNRATD1ck0b2TgD5xVFXqDl0M2/XFvifz9CdBFEd0+i0xouXALmJcmcd26vK7TdjNFTC3oBRyWxslnVnpLnwSMGiuY14bzqVmJ5ZN6jMm4LykUKdQVz6DhCGlDI9JwEXAJ5cKGRPdmsePY3bug4F8+Spb2DAvtPxeQTBiqGeBgvB80YH/m9uhhX/9ls/8CPAjn/TdxfsZhn5m1qdXJtM3Tqm7Oaaciz1U0L33CjRlH04yDtB1LdfXe66vd1xfXWcFAFDXDSIuRwysJv/6ejcz522CY7TS3TL8OBkqxxRvCOZOm7nrcyWjc0XCCKYrYp5EVV5zjv6qclRA5YxDMXD3zh2++7u/yMOHb/LRhx+wu3pKU3tkuyGOPd1hx353yXZdMSYKtu1mw3qzxleepllxenrG+flFKh5q0q4lWkWXTtQsEV1oRS0DqNkKKUV6aR0kRMM5XIDoTIHoGvDOaL9KC8vhEquyjWcpAbtGy/ceUwIl+HfbrbQ0SrdhVs2YJl0YEfH4yqcqVpetAe+FRip199KmkTKOiDGkuTaMSTkrx9EzjhOfQ84iMR0sYs5jzt84JvCl63psvBwZg7oKsJNnAbblR7FwC2JUFFTASJ+GENNikbzrj6lD0G6nEYCnqUS467okgD4j0KZUsiJa7D51XeWCFXMPFAiz+oFpoyyVg34HSDILrVLRGku6lPCz2+3puj7Vg1vEQ09cJ1JyibRzHomKPG/Wmmb7xhuv8cUvfp4w9PziV36Ne3cvOD9Z07Ut11eXhL4jjNoBuKkrLs7PuXPnQjMEq4rT0zPOzs5y6q6vKqVNgwLoc8n8T0Qf6THJ5y0BUV8qiKjXKap7S0jhL02VTSZyAgbnNO4QQlmsdfu4TQmY4C6VwBK4LIW/jAgssQOzAnJ1pMo1g5t6Znrvsktg+RsT7ukYRTcy3dAmq882gxgCwyAMKRkIIVugSuSoxr7lkxggrYr5OIZ223gplICIhlQm5BMoTHBLJ41J8Iu8Cr12EWJqNW0SuCSrPBxadtf7DArqxCY+uKBYgXdacejEpbRbl3yrmHbfiq6dLAhbKLdd5FIBOKdhvqZp2G62XFxcUFWeq8srhMh6s2YY+kxeap8zQA3AO58TgEh+si2c1157wHd97nOsm5r3Hn1AU3u+8F2fgzCy213z3rvfoD3suXN+xqqu8asV/vyMi4sLzs7OWK/XnF1ccH5xQbNa4aoK5z24KVXbMjgnQM4SqcoYup08U8WkpJOJJOVhrwua16gkHC49r9NYJIcxCfMU5r2JNdiclOuq9PfLe5gT15avz+oz4FYFML0PRueoUofqw6FXJVDXVL6l69scEQkpk9J7qIInxGEyHdO994pF9b3DOyGmztvBgYTIKBOZqZUdK7uypC7ELiuGEMOEJ9wyXg4lACkCutg+sxONQaAFGjKBgyG9x8pMYxFh0DyAlt31LimArgj/uPQeja177zPTcAiBug70w0hgal5aXlBJ4NwSJNTXpnvbXeq6YrvZcn56yvnpGSGO7HfXueNuDIG6EojCMJgy1PNtEnLfrFaEEBhSgk9dqbn5hc9/ns9//nPsri/pu5bv+54v8t1f+DxD3/L0yRP2l48Z1zVvvP4aYRxU6M9OOT054ezsjNPTM7ZnZ2y2W5r1ShVAcV7WhMMljCImHr1JviPGpGEea57DQmHrM8mbFZgy4+cmfoyaV5HfccQFWApvjHFWZLZ0BcwaMDO+JK2150ssyNbQMTfAvlcfWz2HukLOQeUddarQdECMI97XgFqniAJ/btTaj1Lha58H/ax3ybh1GvqDhEUxgcg5KzCjBIbXTK7sJE83x0uhBBC9cLbQYnpgZqhmTxmYBAacRYz50SILiQMwTJV/7aHl6ZOnfPzxY66urtIubqBVQqmDXqjVaqVmcNrhQ9D23wUl5eLAp0QYsJ07vRKnndx2kPVqxcl2y3q1IowjbXugbzuGviMMPfvrFhGtCKwcaSFUMxPVJUxBfEXlwbnIatXwPd/z3dw5P+Oj979J5YR/8fu+lwf37zK0B87WKx5/+D4QePjm6zx98pjT01Pu33/Aer3i5OSU0/NzVolluaoaJlfHlGXhEjhHTKGtjNskAbgJhyyfsb9VyEWS7kh9AVRwHZprIFg6tcicbt4sgvI+xpizG0tff+IamAuyjWO5A6UfXYKDE8BcUN+lzA3zZLVWQ6irSo+naumHnqZJ1ZFhQMyt84p5aGaqWgDaCCcmbKpPFq5eM09gGMjEr2Y5S7qkE0rwfK4AvCxKIPkxkuvyl+W5kzlOxkOniEJeWEIqedVJbA97Pv74CR999BFPnz5N/rcWsOTPRLIFoL6wcg/udjuWC7gviCzs+Mo8kiUgWPqZ3nttUR0C7aFld33Nfrfj0O41mhEjfTvgPGw2Ndvths1a8/XHceRwaGnbnqFTMtFV3UBUs/Ptt97irYdv8vHHH3HYX/Pg/j3u3b1gVVds6i2bxvPFL3wXQ99xcXFGUzma1UotgVTFt1pv8M0K68SU8ZHUmdlCuOV1O/Lwxrze/HtSmAoiTlaBFFp0Evb0VI4izHfuUgGYQigp6ey7yryApY9fphGXkQAbZh0cyyxUZTEApkjSGnSRZhxp6oq6qhi6DjlRtzDGoBwElacaK4So0RefujuJw7mauCmS3IYhpRKlMPWYQNRy/WUgtgBnnmO8JEogTv3ux5B4/yaFYMCJiIaeyGGQaceFVOlXeWJQnoAnTy559OgRl5eX9H2P9xVNs6Ku6wwMel+xXq/Zbrc5RVa7DO8ZCnNRcQPtbW9hRrg9IlD+bQuq73uuLwNd3dL3GqIMYcCc++2m5vR0y717F0qFHjTBab8/pDJV9Z0lRs2HIHB+fofv/b7vY7Nd80u/+B5V5fn8d32O9brBSWRdN1B7PvP2Q66vrxCgqS8Qp7UCBgI6U1JMO0gS15n5bREAM8omwbUTLoT4hhJIjkKiAUdSdCGCWgWKFjo3p1kDBd9MaIEsiLPjLTaNJTawxBDK3b9MKFu+b6k0Sncg4xQhQvAEr1ZBXVe4oG5m02iL+K7vECFFDhQwrL0nNhXjqC6A944hRQTNSlDlNBIPMKTqVe0JGXL9SJqlfL1FmOFmnzReCiUQI7l9VGmmmZCb0IdxJEcOovmaEasm9CmRp+17rq+vefz4cVIAg8ZuU9JLjgAguevQ6ekZ6/WGGCNdlxpe9oMiv86KUIZi0ehhODeLXhVjWni2yPquZwgtTV3l7/eVsGpqmrrm7v07PHhwn4s75ziEjz/+mOunV7T7PYyRpq4hWTld27JeVwkQ/CztoWW/3/HGaw946+GbbBolBq29xt23mzVx1AadRtmlC1TLerFkmySYRjduXASQ3DWZ7++T/y9ZIRhkc+yaKIpbWAS4rATz56VIK07Zn05cTqUtd/CZMMabqcql4rBRWg+lG3D0iI/gCzeyRXFE76hisppcxTCaEmhYrxoOhxbQOQyJ6dp5R02Vo0DiyJTmmgZeQ7Rq10AIHUE08lAFrwCqWJKZpQZNsmNYW37ilvFSKAGiMuCUiTk2cSVjcBhD7hJpEwik5BIdYRw5HDQcaBiAmuPKTzD5h571SivlNCx2iohkssu+7/NOZ+myE5pMvlckNxaLtjDJWBxr0JLWPihq60Q43W65c/eM87NTPvOZtzg7PwOBy6eX9F3PfrejO7SIq6ibWgtshoHQj2zunPLgtQecnJ3w/nvfpKlr7t25o/x8lcAoipOMA0PfJqWg5B3NqmG7WU/x75QRqBC9kbRMwhPSedzc3YuFZxchA7nF82megcLCSO+TibotZoDQ2HWsT8BNYLC0BOy+TOwqlcMcdJzeX3JLlvO1bC33rBHxVKiLKXHE1w3Sjwx1zWo1Moxbnjy9IqRQ4Waz1t+NI77yuCipBDvhDkWo2mo5hsTXEMIIlSocTY61+phiJjIQm5KJ5NkmwcuhBADz821M62aKFKgQTua5Ko1AjLZjwaHtefr0ksvLSw6HA0AC13wWRiee7cmW09PTXC7rfZWpsHa7nSYNVRUICb2dmo7aWneJf29MGXneu7yA7bBLf7NCy3s1j9yz3qx4/fX7vP76A87Pz3jrrYd03YEPPvyQD9//gN3VdQKK9GKMfa+1DIMSjZyfn3Pv7l2IkXff/SYP7l1w586F8gp6TSCKw8DY97SHA8PQK+bhHKvVmvV6k3xbDdFGJyi6NFkwOb13FpfWqIwzvNpcpmge/mSW3hzFa4UvZaCvCqMCg/peR2LgmAn0tE5u7vqGDdjrdi7l+ZiZP1uB6T1LV+LGGSy+U5wjeutf6ajqBpEhWwIh6tro2464DayahkMX6NuRqq6Vl3DsGQblh2AcMbZh7x2b9Upp4fqeYWyTu+DwfgLPrT1aNrTStc6H/7JbApISaMrQjGls84kmnsASK5gDNQC73Y7Hjx/z9OlT9vs9CihODUa992w3Ww2JbbdsUvls34+5uMgaj9RNk/rBWZhs7gqYuWxkElNmmSqBKaNx8rG9rzjZbLm4OOf84ox79y44Pdsmem3PkycHHn30ER999CGHQ0sMIQGiLrHeKIORd9pC7c7de1R1zeGw586dz3Hn4hyRxEo0jiq2ifHIrtuqWWXarqkoyGLYNznunXPaelz8BBCKYG2v54k82v7cLayh2XwXu7G+JSnZxU49Ab5S4D83swRLzGJp/i93+KXLsHxtiagvow/HnhfUDSAqLVld1wxjTICzilhVuVS92tMkDsVhGKibRolc0XWslasQQo9xbFRVRbNaUR9aXNsRRl173kU0NWHMkq8estlazwcLvBRKgCg4GpCQWIGKPnbJXCu1t5qq1mtQgbq20/tHHz/ho8ePOHQtrtJkIKlcIsvQ3W+bQmG+boji6Uc4dD27Q8dur7eu6xBxHPpO++iNIwTB4xW4AkhVgE4qrR4TZZ216bC22yKR1WrFxbbhYu052ay5uHvCyekpq81KyT7qisvrHe++9yHvP7rk8hAIsQKUlirGyBB62q5DEDabDffuXbDZVrz33tcZxpbtds3p6RYfA317oHIgMRL6UYuHmjWrZs1mfUKzWuu1SWnBWi6rSiMF5snefoQYRsRFiF79eKsZiI4YhRiM4UfvI8IoUzNTYMJzkrKZwrz2dwAZdR0UNRRCpB9SK284qgSOYQDzTWMSB6Xk0lRd58FFUb4gCdN5i9k0iWwW5RUcze4WNHYfhRATqUgFVqlnpjzJaj052XJ1ec3Q7ZG4ofFC7wUXRyrxhKgEsfVqjXgIfaA3pqvKs6pqNs2KtmoJQ6fn2dQ4H+iAru8TNiXaJzLGnC/zSdrg5VACkBeXIxBir+bQqLTVZdKDEoUERCrlZ5OKSODQHri63vHxk0v27YHooKq0JqCuGzZbC4dt8a4iBhgC9PuWrh/ZXV/z5Mkll5c79rsDXaeYQD+0SusdBeUSmCrcIpJCjMnKcA6JgTiq9eCBuvJsVjV371zw5r0t907RsNF6RdV4pILgoR9H3v/oCd94/xGX+55RVop9ivEZDvRDoO06NusVFxdnvPnmA1Yrz6986dfYbhu2m5pV44l9oB8HBM/Y94xdj4+O1XrNerulWW00cSVlAIYIMgYlvbU0xFIQUkPODOh5rxZ6EpwYNeHFRQcJcEWE0fCGbBHETM5JtqpU+NUg0A1AJBIlzN6/7AFZjqUSgHlH52W4EBLTr9N+DD67MNPNjVovoZyQEYJMlbqZ8UcIOIagfAtWYdr3PU4UeBWBvms5Pdmyu75kHFoIPavK01de18rolfi075FxpJIK70YGQs6QrXxF0zQ0dcPQJcp3l2oTiMCoSW92rlGbpMQoszZqx8ZLpAQmH3TirZ/ChVD6noGhOzAMgaZZ47yn7weePHlC13U0zSpbD1VVsd2ecnFxwXZ7iveOrhsYhkDX9Vxd7bi8vOTJkydcX+/o2gS+kJI3mJcH54cmIDl0pSZwGLVgyQmstw0XZ2fcv3eX1x/c595ZxdkqsGpqbf0tFb7Z0o7CB+8/4itf/TqHtlefVKJ28A0j4gXjFiQq0v/Gaw948803lRH5cOB7v/gFTk/P8jWqfMUw9LS7PX3X0dRaElzXDb7SZW/1CCntUQ35mEAyl3Z72xmDEMSlXdrYbhK6b9fGTO0ouWOxiPb2VQFFhTzNNaYEFuFW0vuJk89b/kb5W8tdfmklLJ/LrzmXFE6yHHzM5KPm8kUsFDm5esWB6O86VX6BOFNCpTJSnoZmxo242arZbzTqJPei73tNEnM+1SJIvhbeOZqqJqwDXa9rWETdD0Touj7lskzdsoyK/lmgwMujBITU+TYlZcS5BQCpTtwJlquuGVUNV7sdj5885sMPPyTGnqapqCvtXrPZrFOb8S0hBK6urnn65JLd7sB+f9CqwusdXdelNlE20SEvAHGWqw1TUGzqEmRWgJNISLtZVVXcOb/gc59Rco/79+9z0gTqeACBbgja5xDP1eMrPvzwIz569CF1s8X5OiHXQyr4EUKfeiFGYbtZ8dqDu9y7c06MgcvLSx6+9Rbr9UoJUpJwHPZ7dtdXOGDV1EkJaJh0iBrCiiL4FKaaGG7Q3V+SOWwovExc+zonEXERoxOb4TmQY/smaK5clKWZHi15eC7s5a0E45bx/byEjmAZxxKKwPgrnC29LIQlfhOZwolTTwFufpeFsSmskmRJWSLaetWwXq9oW3U1T05PqbzPlaumLPquw7k6/x2zNlLQtW7qVLl5YBxbhIivq9SGzhPjXvGzdMyQ+GqeMV4KJaCLxMptY3JCtZTWFTz/VoKpiKzgxNMPPY8fP+bx40doiKVijGjK5mqF8xX7Q8vV1Z79/sDV1RVX1zv21y1drz351L3wVKluALR1N9GqFAsLwGLlIuoDO5fMSX2XE6hrx8X5lrcevsYXPv85Hj58ne16g6cn9Drxm1XNiOODj57w3vvv8+TJE2XzjZFx0AYdkszkOAyEvkNiz7Z23Dk75d6dC7w4Li+f0rcdm2aFQxg6TTMdU8uvGCNV01CvkgUgpFr1hLUwCdcsvScY8BkIoWDwcZE+CYw201BFsDTVzT0V0SQnXFIqhvWl34xBlSalkjUAL07Zo3aMZVz/mBKIS2EUmb1XX48YV6SFNcvIglkCs7yEomHN0XwEmP12BkYTY9UqpYz3fc/hoE1gmrpmTEloMcYEjmuHZiQiCfNXene1UNXNqAih0W7ZKblORKjqilVcYQVxxnSETxvbLdrgpVACQAJlUo01ERyL/m6abdV1qZFlP0Ds6cfA9fUVXdexWikVVtd1jMPIIWiLsbbt2e8PmUNAew8WnIa2c8UpxC3i8L5Reuhco2Avkhd4NhFDIIaRGEea2nP3zjlvPXydz7z9BvfvaRhv6IXRBXA1UtUcrnd8+PHHvPfBR+wPLXWtUYpx1ErCyjmCCwxtD6FnXTsuzs54+Np9Hty7AALdfs/JZm2ld+pOdS3t9bUmFDU1m+0mt/UaR62tGIIWskSxopewCO8JImFeLozu5EIkl/c6h0R7fWFyOslJPkRLRJpwqhgjMbk4kwKJ+Twm4V22I5+3sLfjzUvpiEWw/JxEo3ublMAk8EIs4vXCXKks8Qd1i6boRY6WCBBkUgInJ1xeXed2bJvNhlWsGfqecRxz8VMY1UJTJRkZxqC5IaPyRzpx1FXNqhkJrW5mMclKU9cplH1QRUBczsqN8ZIoAQ01aaBYi0rMnJou9GRijSFyaFvdxaOCPPkCRui6kWHQcuGu01ZW7aFowulrlEegSma3pgEPQ0DGVPabGHVCMlcnO1lyJxvb2VRjj8RxIIaBZrXm7p0zXn/tPg/u3+X0ZEPfthyCR2RDtXK0Xc9HH1/y/gcf8fTykmEc8c7ajetCrSrlL+jDgJfI2cmWh2884OGbr3Hv4gwJ2oDj/v37dH2nvuU40u73XF9dQRg4PZmow0JUqnW1MdJuHUUbYjjBiy8EKAlEdAksTDt5TJ2fxSWW4CLTcKEIXNBsxaBZP0TnyFxDtuMm5UX+1TgT2GOhu2OIf/58oQCOAYnZWpBJ4Vloch4KLMKIRxTFTOlkxRlT0ECmvpMu5rJiDUfXXF9fc319zXq9pq7UFRu6IbufIZiFljbEWNDKRcsf0ISvflTi3BBHpK5TGvyKGK0xa3Jkj+AiNl4aJRDjVJKp6OtkpkbQbrt9z5jqsccx0LYtVVXT1NrG+vLykkPbs08hvq7rZwvG5RRYIebGJcZyO2pPn2BAYMT5MlyWhqgLkKQfUohL0H4AcVQw8vR0y8XZCSfbFaumwsWBMVQIjqpa8fjyfb7xzQ/46OMnOSd8t9sp1yHo9RhHpQQLal1cnJ3wxmv3uH/vDttNwzD0uBi5f/cOXXugPdQwDlxfX7LbXbKqdHotR2IYhwLYdCTSfzU6x/SUAYEy5ehPz6XrFkmhXD8rprEvyVaDQIiCi6TogSmehA3kW7LIisdLoV8+N60c8mePCbzdz/NPbEpLq2dhCYjLtJwG/MYj2Yf6fnVdHIqDOKOGT895EagU3V81yu94dXXF+fm55ms4RwwjYej1ugSjrXOpa5WRkep107Ri5TFs6oq2UmA8hoBUQl17xrGm69qjTMnLIce06T/vISIfANfAhy/6WH4d4wGf7uOHT/85fNqPH/7ZnsN3xRhfWz75UigBABH5hzHG3/aij+PbHZ/244dP/zl82o8fXsw5uE9+y6vxarwa/38er5TAq/FqfIePl0kJ/NiLPoBf5/i0Hz98+s/h03788ALO4aXBBF6NV+PVeDHjZbIEXo1X49V4AeOFKwER+bdF5BdF5Esi8sMv+nied4jIl0Xkn4jIT4nIP0zP3RORvyUiv5zu777o47QhIn9RRN4XkZ8tnrv1eEXkT6Y5+UUR+bdezFHPxy3n8KdF5OtpHn5KRH5P8dpLdQ4i8lkR+T9E5BdE5OdE5D9Pz7/YeVhmZP3zvKFs3r8CfBFogJ8Gvv9FHtO3cOxfBh4snvtvgR9Oj38Y+G9e9HEWx/Y7gd8C/OwnHS/w/WkuVsAX0hz5l/Qc/jTwXxx570t3DsBD4Lekx2fAL6XjfKHz8KItgd8OfCnG+Ksxxg74ceCHXvAx/XrGDwF/KT3+S8C/++IOZT5ijH8XeLR4+rbj/SHgx2OMbYzx14AvoXP1Qsct53DbeOnOIcb4bozxH6XHl8AvAG/zgufhRSuBt4GvFX+/k577NIwI/O8i8pMi8p+k596IMb4LOuHA6y/s6J5v3Ha8n7Z5+aMi8jPJXTBT+qU+BxH5PPCbgb/PC56HF60EjlU1fFrCFb8jxvhbgN8N/BER+Z0v+oB+A8enaV7+HPDdwA8C7wJ/Nj3/0p6DiJwCfwP4YzHGp89665HnfsPP4UUrgXeAzxZ/fwb4xgs6lm9pxBi/ke7fB/4n1Ex7T0QeAqT791/cET7XuO14PzXzEmN8L8Y4Rq08+vNM5vJLeQ4iUqMK4C/HGP9mevqFzsOLVgL/L/C9IvIFEWmA3w/8xAs+pk8cInIiImf2GPg3gZ9Fj/0PpLf9AeB/eTFH+NzjtuP9CeD3i8hKRL4AfC/wD17A8X3iMOFJ4/ei8wAv4TmIljX+BeAXYow/Wrz0YufhJUB8fw+Kkv4K8Kde9PE85zF/EUVtfxr4OTtu4D7wd4BfTvf3XvSxFsf8V1FzuUd3mD/0rOMF/lSak18EfveLPv5nnMP/CPwT4GeS0Dx8Wc8B+FdQc/5ngJ9Kt9/zoufhVcbgq/FqfIePF+0OvBqvxqvxgscrJfBqvBrf4eOVEng1Xo3v8PFKCbwar8Z3+HilBF6NV+M7fLxSAq/Gq/EdPl4pgVfj1fgOH6+UwKvxanyHj/8PICJotsLm6dEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 1)\n"
     ]
    }
   ],
   "source": [
    "img = train_dataset.__getitem__(917)[0]\n",
    "tf = transforms.ToPILImage()\n",
    "img = tf(img)\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "\n",
    "print(train_dataset.__getitem__(917)[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 0)\n"
     ]
    }
   ],
   "source": [
    "img = aug_train_dataset.__getitem__(17800)[0]\n",
    "tf = transforms.ToPILImage()\n",
    "img = tf(img)\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "\n",
    "print(aug_train_dataset.__getitem__(17800)[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set: 16402\n",
      "Validation set: 1937\n",
      "Test set: 1921\n",
      "Over_Training set: 23778\n"
     ]
    }
   ],
   "source": [
    "# dataset 개수 확인\n",
    "\n",
    "print('Training set: {}'.format(len(train_dataset)))\n",
    "print('Validation set: {}'.format(len(valid_dataset)))\n",
    "print('Test set: {}'.format(len(test_dataset)))\n",
    "print('Over_Training set: {}'.format(len(AUG_train_dataset)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.01\n",
    "WEIGHT_DECAY = 5e-4\n",
    "CRITERION = nn.BCELoss()\n",
    "N_EPOCHS = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Epoch:1/30.. Train Loss: 0.7157.. Train Acc: 0.5725.. Val Loss: 0.7883.. Val Acc: 0.5075..\n",
      " \n",
      "Epoch:2/30.. Train Loss: 0.4120.. Train Acc: 0.7983.. Val Loss: 0.3774.. Val Acc: 0.8601..\n",
      " \n",
      "Epoch:3/30.. Train Loss: 0.2308.. Train Acc: 0.9054.. Val Loss: 0.2326.. Val Acc: 0.9189..\n",
      "1\n",
      " \n",
      "Epoch:4/30.. Train Loss: 0.2118.. Train Acc: 0.9148.. Val Loss: 0.6931.. Val Acc: 0.6649..\n",
      "2\n",
      " \n",
      "Epoch:5/30.. Train Loss: 0.1887.. Train Acc: 0.9254.. Val Loss: 0.4402.. Val Acc: 0.7594..\n",
      "3\n",
      " \n",
      "Epoch:6/30.. Train Loss: 0.1817.. Train Acc: 0.9254.. Val Loss: 0.2605.. Val Acc: 0.9308..\n",
      "4\n",
      " \n",
      "Epoch:7/30.. Train Loss: 0.1719.. Train Acc: 0.9311.. Val Loss: 0.2834.. Val Acc: 0.8776..\n",
      " \n",
      "Epoch:8/30.. Train Loss: 0.1634.. Train Acc: 0.9335.. Val Loss: 0.1948.. Val Acc: 0.9344..\n",
      "1\n",
      " \n",
      "Epoch:9/30.. Train Loss: 0.1504.. Train Acc: 0.9405.. Val Loss: 0.4252.. Val Acc: 0.8286..\n",
      "2\n",
      " \n",
      "Epoch:10/30.. Train Loss: 0.1505.. Train Acc: 0.9396.. Val Loss: 0.2228.. Val Acc: 0.9122..\n",
      "3\n",
      " \n",
      "Epoch:11/30.. Train Loss: 0.1517.. Train Acc: 0.9384.. Val Loss: 0.2464.. Val Acc: 0.8957..\n",
      " \n",
      "Epoch:12/30.. Train Loss: 0.1412.. Train Acc: 0.9434.. Val Loss: 0.1570.. Val Acc: 0.9489..\n",
      " \n",
      "Epoch:13/30.. Train Loss: 0.1418.. Train Acc: 0.9440.. Val Loss: 0.1436.. Val Acc: 0.9494..\n",
      " \n",
      "Epoch:14/30.. Train Loss: 0.1365.. Train Acc: 0.9440.. Val Loss: 0.1272.. Val Acc: 0.9561..\n",
      "1\n",
      " \n",
      "Epoch:15/30.. Train Loss: 0.1296.. Train Acc: 0.9474.. Val Loss: 0.1424.. Val Acc: 0.9437..\n",
      "2\n",
      " \n",
      "Epoch:16/30.. Train Loss: 0.1293.. Train Acc: 0.9488.. Val Loss: 0.1638.. Val Acc: 0.9417..\n",
      "3\n",
      " \n",
      "Epoch:17/30.. Train Loss: 0.1318.. Train Acc: 0.9478.. Val Loss: 0.2223.. Val Acc: 0.9220..\n",
      " \n",
      "Epoch:18/30.. Train Loss: 0.1296.. Train Acc: 0.9459.. Val Loss: 0.1266.. Val Acc: 0.9556..\n",
      "1\n",
      " \n",
      "Epoch:19/30.. Train Loss: 0.1260.. Train Acc: 0.9516.. Val Loss: 0.4294.. Val Acc: 0.8198..\n",
      "2\n",
      " \n",
      "Epoch:20/30.. Train Loss: 0.1290.. Train Acc: 0.9484.. Val Loss: 2.4992.. Val Acc: 0.5761..\n",
      "3\n",
      " \n",
      "Epoch:21/30.. Train Loss: 0.1204.. Train Acc: 0.9563.. Val Loss: 0.1813.. Val Acc: 0.9406..\n",
      "4\n",
      " \n",
      "Epoch:22/30.. Train Loss: 0.1206.. Train Acc: 0.9554.. Val Loss: 0.1332.. Val Acc: 0.9577..\n",
      "5\n",
      " \n",
      "Epoch:23/30.. Train Loss: 0.1251.. Train Acc: 0.9517.. Val Loss: 0.4179.. Val Acc: 0.8529..\n",
      "6\n",
      " \n",
      "Epoch:24/30.. Train Loss: 0.1164.. Train Acc: 0.9535.. Val Loss: 0.1429.. Val Acc: 0.9479..\n",
      "7\n",
      " \n",
      "Epoch:25/30.. Train Loss: 0.1153.. Train Acc: 0.9549.. Val Loss: 0.1751.. Val Acc: 0.9339..\n",
      "8\n",
      " \n",
      "Epoch:26/30.. Train Loss: 0.1179.. Train Acc: 0.9540.. Val Loss: 0.2950.. Val Acc: 0.8782..\n",
      "9\n",
      " \n",
      "Epoch:27/30.. Train Loss: 0.1163.. Train Acc: 0.9556.. Val Loss: 0.1591.. Val Acc: 0.9391..\n",
      "10\n",
      "Early stopped\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'train_loss': [0.7157348944235218,\n",
       "  0.4119520204704861,\n",
       "  0.23078389991392462,\n",
       "  0.21175527988478196,\n",
       "  0.1886732272746027,\n",
       "  0.18173510904691015,\n",
       "  0.1719165700235108,\n",
       "  0.1634293241325275,\n",
       "  0.1504074920170991,\n",
       "  0.15051735221415527,\n",
       "  0.1516775252340838,\n",
       "  0.14120501152766768,\n",
       "  0.14177480091651282,\n",
       "  0.13652040145193883,\n",
       "  0.12960771095845125,\n",
       "  0.1292723954994549,\n",
       "  0.13177187619514244,\n",
       "  0.12964496547861618,\n",
       "  0.12602007048305616,\n",
       "  0.12903737747507502,\n",
       "  0.12043370491312455,\n",
       "  0.1206043111088202,\n",
       "  0.12508464344712192,\n",
       "  0.11641809292310892,\n",
       "  0.1152967069038125,\n",
       "  0.1179457962108675,\n",
       "  0.11632958461725434],\n",
       " 'train_acc': [tensor(0.5725, device='cuda:0'),\n",
       "  tensor(0.7983, device='cuda:0'),\n",
       "  tensor(0.9054, device='cuda:0'),\n",
       "  tensor(0.9148, device='cuda:0'),\n",
       "  tensor(0.9254, device='cuda:0'),\n",
       "  tensor(0.9254, device='cuda:0'),\n",
       "  tensor(0.9311, device='cuda:0'),\n",
       "  tensor(0.9335, device='cuda:0'),\n",
       "  tensor(0.9405, device='cuda:0'),\n",
       "  tensor(0.9396, device='cuda:0'),\n",
       "  tensor(0.9384, device='cuda:0'),\n",
       "  tensor(0.9434, device='cuda:0'),\n",
       "  tensor(0.9440, device='cuda:0'),\n",
       "  tensor(0.9440, device='cuda:0'),\n",
       "  tensor(0.9474, device='cuda:0'),\n",
       "  tensor(0.9488, device='cuda:0'),\n",
       "  tensor(0.9478, device='cuda:0'),\n",
       "  tensor(0.9459, device='cuda:0'),\n",
       "  tensor(0.9516, device='cuda:0'),\n",
       "  tensor(0.9484, device='cuda:0'),\n",
       "  tensor(0.9563, device='cuda:0'),\n",
       "  tensor(0.9554, device='cuda:0'),\n",
       "  tensor(0.9517, device='cuda:0'),\n",
       "  tensor(0.9535, device='cuda:0'),\n",
       "  tensor(0.9549, device='cuda:0'),\n",
       "  tensor(0.9540, device='cuda:0'),\n",
       "  tensor(0.9556, device='cuda:0')],\n",
       " 'val_loss': [0.7882640548050404,\n",
       "  0.37738933973014355,\n",
       "  0.2325846515595913,\n",
       "  0.6930666752159595,\n",
       "  0.44020075537264347,\n",
       "  0.26048702001571655,\n",
       "  0.28336931578814983,\n",
       "  0.1948098223656416,\n",
       "  0.4251547362655401,\n",
       "  0.22284780256450176,\n",
       "  0.24640438705682755,\n",
       "  0.15701466938480735,\n",
       "  0.14363415259867907,\n",
       "  0.12720171129330993,\n",
       "  0.14240949833765626,\n",
       "  0.16383147006854415,\n",
       "  0.2223415868356824,\n",
       "  0.1266223951242864,\n",
       "  0.42944202199578285,\n",
       "  2.4991693943738937,\n",
       "  0.1812930703163147,\n",
       "  0.13319239439442754,\n",
       "  0.41791667602956295,\n",
       "  0.1428813566453755,\n",
       "  0.17506689857691526,\n",
       "  0.29499489814043045,\n",
       "  0.15912526892498136],\n",
       " 'val_acc': [tensor(0.5075, device='cuda:0'),\n",
       "  tensor(0.8601, device='cuda:0'),\n",
       "  tensor(0.9189, device='cuda:0'),\n",
       "  tensor(0.6649, device='cuda:0'),\n",
       "  tensor(0.7594, device='cuda:0'),\n",
       "  tensor(0.9308, device='cuda:0'),\n",
       "  tensor(0.8776, device='cuda:0'),\n",
       "  tensor(0.9344, device='cuda:0'),\n",
       "  tensor(0.8286, device='cuda:0'),\n",
       "  tensor(0.9122, device='cuda:0'),\n",
       "  tensor(0.8957, device='cuda:0'),\n",
       "  tensor(0.9489, device='cuda:0'),\n",
       "  tensor(0.9494, device='cuda:0'),\n",
       "  tensor(0.9561, device='cuda:0'),\n",
       "  tensor(0.9437, device='cuda:0'),\n",
       "  tensor(0.9417, device='cuda:0'),\n",
       "  tensor(0.9220, device='cuda:0'),\n",
       "  tensor(0.9556, device='cuda:0'),\n",
       "  tensor(0.8198, device='cuda:0'),\n",
       "  tensor(0.5761, device='cuda:0'),\n",
       "  tensor(0.9406, device='cuda:0'),\n",
       "  tensor(0.9577, device='cuda:0'),\n",
       "  tensor(0.8529, device='cuda:0'),\n",
       "  tensor(0.9479, device='cuda:0'),\n",
       "  tensor(0.9339, device='cuda:0'),\n",
       "  tensor(0.8782, device='cuda:0'),\n",
       "  tensor(0.9391, device='cuda:0')]}"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_save_folder = os.path.join(project_folder, \"model_save\")\n",
    "model_name = 'Gender_Young_original.pt'\n",
    "\n",
    "model = copy.deepcopy(Resnet18).to(device_cuda)\n",
    "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay = WEIGHT_DECAY)\n",
    "Train(model, optimizer, loss_fn = CRITERION , n_epochs = N_EPOCHS, scheduler = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Accuracy, PPV, NPV, Sensitivity, Specificity, F1, Loss) = 0.943, 0.903, 0.971, 0.956, 0.934, 0.928, 0.148\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9427381572097866,\n",
       " 0.9558232931726908,\n",
       " 0.934412265758092,\n",
       " 0.9026548672566371,\n",
       " 0.9707964601769912,\n",
       " 0.9284785435630689)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = copy.deepcopy(Resnet18).to(device_cuda)\n",
    "model_save_folder = os.path.join(project_folder, \"model_save\")\n",
    "model_name = 'Gender_Young_original.pt'\n",
    "load_name = os.path.join(model_save_folder, model_name)\n",
    "model.load_state_dict(torch.load(load_name))\n",
    "\n",
    "Test(model, loss_fn = CRITERION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Epoch:1/30.. Train Loss: 0.7765.. Train Acc: 0.5495.. Val Loss: 1.1977.. Val Acc: 0.4239..\n",
      " \n",
      "Epoch:2/30.. Train Loss: 0.4648.. Train Acc: 0.7784.. Val Loss: 0.3445.. Val Acc: 0.8585..\n",
      " \n",
      "Epoch:3/30.. Train Loss: 0.3306.. Train Acc: 0.8520.. Val Loss: 0.2285.. Val Acc: 0.9143..\n",
      "1\n",
      " \n",
      "Epoch:4/30.. Train Loss: 0.2895.. Train Acc: 0.8739.. Val Loss: 0.5421.. Val Acc: 0.7326..\n",
      " \n",
      "Epoch:5/30.. Train Loss: 0.2752.. Train Acc: 0.8846.. Val Loss: 0.1903.. Val Acc: 0.9370..\n",
      "1\n",
      " \n",
      "Epoch:6/30.. Train Loss: 0.2635.. Train Acc: 0.8904.. Val Loss: 0.2098.. Val Acc: 0.9319..\n",
      "2\n",
      " \n",
      "Epoch:7/30.. Train Loss: 0.2589.. Train Acc: 0.8944.. Val Loss: 0.3343.. Val Acc: 0.8554..\n",
      "3\n",
      " \n",
      "Epoch:8/30.. Train Loss: 0.2432.. Train Acc: 0.8985.. Val Loss: 0.3152.. Val Acc: 0.8704..\n",
      " \n",
      "Epoch:9/30.. Train Loss: 0.2414.. Train Acc: 0.9027.. Val Loss: 0.1880.. Val Acc: 0.9350..\n",
      "1\n",
      " \n",
      "Epoch:10/30.. Train Loss: 0.2275.. Train Acc: 0.9093.. Val Loss: 0.2036.. Val Acc: 0.9231..\n",
      "2\n",
      " \n",
      "Epoch:11/30.. Train Loss: 0.2237.. Train Acc: 0.9101.. Val Loss: 0.8633.. Val Acc: 0.6835..\n",
      "3\n",
      " \n",
      "Epoch:12/30.. Train Loss: 0.2246.. Train Acc: 0.9108.. Val Loss: 0.3052.. Val Acc: 0.8766..\n",
      " \n",
      "Epoch:13/30.. Train Loss: 0.2134.. Train Acc: 0.9161.. Val Loss: 0.1550.. Val Acc: 0.9484..\n",
      "1\n",
      " \n",
      "Epoch:14/30.. Train Loss: 0.2081.. Train Acc: 0.9141.. Val Loss: 0.3029.. Val Acc: 0.8606..\n",
      "2\n",
      " \n",
      "Epoch:15/30.. Train Loss: 0.2066.. Train Acc: 0.9149.. Val Loss: 0.3748.. Val Acc: 0.8343..\n",
      "3\n",
      " \n",
      "Epoch:16/30.. Train Loss: 0.2083.. Train Acc: 0.9157.. Val Loss: 2.2116.. Val Acc: 0.5854..\n",
      "4\n",
      " \n",
      "Epoch:17/30.. Train Loss: 0.1996.. Train Acc: 0.9222.. Val Loss: 0.1736.. Val Acc: 0.9417..\n",
      "5\n",
      " \n",
      "Epoch:18/30.. Train Loss: 0.1993.. Train Acc: 0.9193.. Val Loss: 0.4613.. Val Acc: 0.8420..\n",
      "6\n",
      " \n",
      "Epoch:19/30.. Train Loss: 0.1958.. Train Acc: 0.9223.. Val Loss: 0.2032.. Val Acc: 0.9298..\n",
      "7\n",
      " \n",
      "Epoch:20/30.. Train Loss: 0.1960.. Train Acc: 0.9209.. Val Loss: 0.1799.. Val Acc: 0.9303..\n",
      "8\n",
      " \n",
      "Epoch:21/30.. Train Loss: 0.1872.. Train Acc: 0.9245.. Val Loss: 0.5914.. Val Acc: 0.7372..\n",
      "9\n",
      " \n",
      "Epoch:22/30.. Train Loss: 0.1889.. Train Acc: 0.9250.. Val Loss: 0.2294.. Val Acc: 0.9303..\n",
      "10\n",
      "Early stopped\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'train_loss': [0.7764799123810183,\n",
       "  0.46481839174865397,\n",
       "  0.3306074824224236,\n",
       "  0.2895026513004816,\n",
       "  0.27523774012763014,\n",
       "  0.26349550693906765,\n",
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       "  0.24321482426697208,\n",
       "  0.24143668536537438,\n",
       "  0.22752611102756634,\n",
       "  0.22369780719921153,\n",
       "  0.22456146384118705,\n",
       "  0.213378543174395,\n",
       "  0.2080963635717028,\n",
       "  0.20660282098638114,\n",
       "  0.20826915859855633,\n",
       "  0.19958861396517805,\n",
       "  0.19926700384546353,\n",
       "  0.19575830965593297,\n",
       "  0.1960318435023549,\n",
       "  0.18723920508418032,\n",
       "  0.18885860483973257],\n",
       " 'train_acc': [tensor(0.5495, device='cuda:0'),\n",
       "  tensor(0.7784, device='cuda:0'),\n",
       "  tensor(0.8520, device='cuda:0'),\n",
       "  tensor(0.8739, device='cuda:0'),\n",
       "  tensor(0.8846, device='cuda:0'),\n",
       "  tensor(0.8904, device='cuda:0'),\n",
       "  tensor(0.8944, device='cuda:0'),\n",
       "  tensor(0.8985, device='cuda:0'),\n",
       "  tensor(0.9027, device='cuda:0'),\n",
       "  tensor(0.9093, device='cuda:0'),\n",
       "  tensor(0.9101, device='cuda:0'),\n",
       "  tensor(0.9108, device='cuda:0'),\n",
       "  tensor(0.9161, device='cuda:0'),\n",
       "  tensor(0.9141, device='cuda:0'),\n",
       "  tensor(0.9149, device='cuda:0'),\n",
       "  tensor(0.9157, device='cuda:0'),\n",
       "  tensor(0.9222, device='cuda:0'),\n",
       "  tensor(0.9193, device='cuda:0'),\n",
       "  tensor(0.9223, device='cuda:0'),\n",
       "  tensor(0.9209, device='cuda:0'),\n",
       "  tensor(0.9245, device='cuda:0'),\n",
       "  tensor(0.9250, device='cuda:0')],\n",
       " 'val_loss': [1.1977353245019913,\n",
       "  0.3444694112986326,\n",
       "  0.228476632386446,\n",
       "  0.5420688986778259,\n",
       "  0.19030171539634466,\n",
       "  0.20984957460314035,\n",
       "  0.33433697558939457,\n",
       "  0.3151940517127514,\n",
       "  0.18800110835582018,\n",
       "  0.20358160976320505,\n",
       "  0.8632581904530525,\n",
       "  0.3051512986421585,\n",
       "  0.15497917542234063,\n",
       "  0.30285146553069353,\n",
       "  0.374772678129375,\n",
       "  2.211586244404316,\n",
       "  0.17364390194416046,\n",
       "  0.4612962640821934,\n",
       "  0.2031919751316309,\n",
       "  0.1798761603422463,\n",
       "  0.5913796685636044,\n",
       "  0.2294414760544896],\n",
       " 'val_acc': [tensor(0.4239, device='cuda:0'),\n",
       "  tensor(0.8585, device='cuda:0'),\n",
       "  tensor(0.9143, device='cuda:0'),\n",
       "  tensor(0.7326, device='cuda:0'),\n",
       "  tensor(0.9370, device='cuda:0'),\n",
       "  tensor(0.9319, device='cuda:0'),\n",
       "  tensor(0.8554, device='cuda:0'),\n",
       "  tensor(0.8704, device='cuda:0'),\n",
       "  tensor(0.9350, device='cuda:0'),\n",
       "  tensor(0.9231, device='cuda:0'),\n",
       "  tensor(0.6835, device='cuda:0'),\n",
       "  tensor(0.8766, device='cuda:0'),\n",
       "  tensor(0.9484, device='cuda:0'),\n",
       "  tensor(0.8606, device='cuda:0'),\n",
       "  tensor(0.8343, device='cuda:0'),\n",
       "  tensor(0.5854, device='cuda:0'),\n",
       "  tensor(0.9417, device='cuda:0'),\n",
       "  tensor(0.8420, device='cuda:0'),\n",
       "  tensor(0.9298, device='cuda:0'),\n",
       "  tensor(0.9303, device='cuda:0'),\n",
       "  tensor(0.7372, device='cuda:0'),\n",
       "  tensor(0.9303, device='cuda:0')]}"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Aug\n",
    "model_save_fdolder = os.path.join(project_folder, \"model_save\")\n",
    "model_name = 'Gender_Young_over.pt'\n",
    "\n",
    "model = copy.deepcopy(Resnet18).to(device_cuda)\n",
    "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay = WEIGHT_DECAY)\n",
    "Train(model, optimizer, loss_fn = CRITERION , n_epochs = N_EPOCHS, scheduler = None, OVER = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Accuracy, PPV, NPV, Sensitivity, Specificity, F1, Loss) = 0.928, 0.912, 0.938, 0.902, 0.945, 0.907, 0.159\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9281624154086413,\n",
       " 0.9022757697456493,\n",
       " 0.944633730834753,\n",
       " 0.912043301759134,\n",
       " 0.9382402707275804,\n",
       " 0.9071332436069987)"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = copy.deepcopy(Resnet18).to(device_cuda)\n",
    "model_save_folder = os.path.join(project_folder, \"model_save\")\n",
    "model_name = 'Gender_Young_over.pt'\n",
    "load_name = os.path.join(model_save_folder, model_name)\n",
    "model.load_state_dict(torch.load(load_name))\n",
    "\n",
    "\n",
    "Test(model, loss_fn = CRITERION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VNQGQ5GN0r9tFdH0zOed/A3BvVf04yQ1072OYw/f+vjJC30A3CnlQksfQHcTfmKL9jvw4/3y6ywt/fGj9W+kC6fD+sfcb2HbvwO372fM+WB/o/23vGj1beeix8/CB21M9tx36Wtlf1XM98IfABUOb/w44p6qeDvzeCI+9O8zG8TU513s0XaDfRDeqG3X+fLIvHuCh/fIAMD/JscCvAkf3I/Fr2daXP65tJ6jMVj/O5TG2li6g96+qu+i//bCtb6fb12DfPNi3VfUA2/ppzt77+0qg/xvwyCSvggev8X428OGq+sFQ2yuAV/TtVgCPnWHfn6L7lL9saP0C4Dv9C/1KurNs9zaDfXEscEc/lfBNuhEO6f4zkyXT7aR/03w/3WUhoLt8xEzOpptOuHNo/QLg2/3tV4+wn91hNo6vtXQjyIVVdXsfIhN0l6peW1Xfo/t2+Ny+/SuBz0+9qyktAO6qqh8keXL/WHNhLo6xK+kGA1/pl6+ne/5Povtw/hLw/HS/sJpH9y1iR/t2Tt77+0Sg92+GE4GXJvk68DXgR3RfYYe9E3heki8DL6CbB51u39+vqjP7UeWg9wOvTnI13Veue3bxacyFdwDLk1wP/CXbAvQC4Kf7r4y/T9efM3k9sCrJVXQjoO9N17iqNlTVR7ZT0yfS/WR0zi5/Omg2jq8+oCZ46C98rgIez7YgejVwVv/6HMG2OdxRXEo3Ur+ebnrr6h247zi9g91/jK2lm2a5Ch78m8XtwLqqeqCfnnob8B90ff3lqrp4B57TnL33PfVfu0WSR1fV3f3tM4AnVNVb5rgsNcRjbM+bu1W7XpzkbXTH3LeA18xtOWrQPn+MOUKXpEbsE3PokrQvMNAlqREGuiQ1wkCXpEYY6JLUiP8HozDW6xHy+ToAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = copy.deepcopy(Resnet18).to(device_cuda)\n",
    "model_save_folder = os.path.join(project_folder, \"model_save\")\n",
    "model_name = 'Gender_Young_original.pt'\n",
    "load_name = os.path.join(model_save_folder, model_name)\n",
    "model.load_state_dict(torch.load(load_name))\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "\n",
    "    pred_old_man = []\n",
    "    pred_young_man = []\n",
    "    pred_old_woman = []\n",
    "    pred_young_woman = []\n",
    "    \n",
    "    for x, y_t, att in test_loader:\n",
    "        x, y_t, att = x.to(device_cuda), y_t.to(device_cuda, dtype=torch.float32), att.to(device_cuda, dtype=torch.float32)\n",
    "\n",
    "        output = torch.sigmoid(model(x).flatten())\n",
    "                \n",
    "        y_pred_om = torch.where(output[y_t == 1][att[y_t == 1]==0] >= 0.5, 1, 0)\n",
    "        y_pred_ym = torch.where(output[y_t == 1][att[y_t == 1]==1] >= 0.5, 1, 0)\n",
    "        y_pred_ow = torch.where(output[y_t == 0][att[y_t == 0]==0] >= 0.5, 1, 0)\n",
    "        y_pred_yw = torch.where(output[y_t == 0][att[y_t == 0]==1] >= 0.5, 1, 0)\n",
    "\n",
    "        pred_old_man.extend(y_pred_om.cpu().numpy())\n",
    "        pred_young_man.extend(y_pred_ym.cpu().numpy())\n",
    "        pred_old_woman.extend(y_pred_ow.cpu().numpy())\n",
    "        pred_young_woman.extend(y_pred_yw.cpu().numpy())\n",
    "        \n",
    "acc_om = np.sum(np.array(pred_old_man)==1)/len(pred_old_man) * 100\n",
    "acc_ym =np.sum(np.array(pred_young_man)==1)/len(pred_young_man) * 100\n",
    "acc_ow =np.sum(np.array(pred_old_woman)==0)/len(pred_old_woman) * 100\n",
    "acc_yw =np.sum(np.array(pred_young_woman)==0)/len(pred_young_woman) * 100\n",
    "\n",
    "index = ['Old Man', 'Young Man', 'Old Woman', 'Young Woman']\n",
    "accs_original = [acc_om, acc_ym, acc_ow, acc_yw]\n",
    "\n",
    "plt.bar(index, accs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VNQGQ5GN0r9tFdH0zOed/A3BvVf04yQ1072OYw/f+vjJC30A3CnlQksfQHcTfmKL9jvw4/3y6ywt/fGj9W+kC6fD+sfcb2HbvwO372fM+WB/o/23vGj1beeix8/CB21M9tx36Wtlf1XM98IfABUOb/w44p6qeDvzeCI+9O8zG8TU513s0XaDfRDeqG3X+fLIvHuCh/fIAMD/JscCvAkf3I/Fr2daXP65tJ6jMVj/O5TG2li6g96+qu+i//bCtb6fb12DfPNi3VfUA2/ppzt77+0qg/xvwyCSvggev8X428OGq+sFQ2yuAV/TtVgCPnWHfn6L7lL9saP0C4Dv9C/1KurNs9zaDfXEscEc/lfBNuhEO6f4zkyXT7aR/03w/3WUhoLt8xEzOpptOuHNo/QLg2/3tV4+wn91hNo6vtXQjyIVVdXsfIhN0l6peW1Xfo/t2+Ny+/SuBz0+9qyktAO6qqh8keXL/WHNhLo6xK+kGA1/pl6+ne/5Povtw/hLw/HS/sJpH9y1iR/t2Tt77+0Sg92+GE4GXJvk68DXgR3RfYYe9E3heki8DL6CbB51u39+vqjP7UeWg9wOvTnI13Veue3bxacyFdwDLk1wP/CXbAvQC4Kf7r4y/T9efM3k9sCrJVXQjoO9N17iqNlTVR7ZT0yfS/WR0zi5/Omg2jq8+oCZ46C98rgIez7YgejVwVv/6HMG2OdxRXEo3Ur+ebnrr6h247zi9g91/jK2lm2a5Ch78m8XtwLqqeqCfnnob8B90ff3lqrp4B57TnL33PfVfu0WSR1fV3f3tM4AnVNVb5rgsNcRjbM+bu1W7XpzkbXTH3LeA18xtOWrQPn+MOUKXpEbsE3PokrQvMNAlqREGuiQ1wkCXpEYY6JLUiP8HozDW6xHy+ToAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = copy.deepcopy(Resnet18).to(device_cuda)\n",
    "model_save_folder = os.path.join(project_folder, \"model_save\")\n",
    "model_name = 'Gender_Young_over.pt'\n",
    "load_name = os.path.join(model_save_folder, model_name)\n",
    "model.load_state_dict(torch.load(load_name))\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "\n",
    "    pred_old_man = []\n",
    "    pred_young_man = []\n",
    "    pred_old_woman = []\n",
    "    pred_young_woman = []\n",
    "    \n",
    "    for x, y_t, att in test_loader:\n",
    "        x, y_t, att = x.to(device_cuda), y_t.to(device_cuda, dtype=torch.float32), att.to(device_cuda, dtype=torch.float32)\n",
    "\n",
    "        output = torch.sigmoid(model(x).flatten())\n",
    "                \n",
    "        y_pred_om = torch.where(output[y_t == 1][att[y_t == 1]==0] >= 0.5, 1, 0)\n",
    "        y_pred_ym = torch.where(output[y_t == 1][att[y_t == 1]==1] >= 0.5, 1, 0)\n",
    "        y_pred_ow = torch.where(output[y_t == 0][att[y_t == 0]==0] >= 0.5, 1, 0)\n",
    "        y_pred_yw = torch.where(output[y_t == 0][att[y_t == 0]==1] >= 0.5, 1, 0)\n",
    "\n",
    "        pred_old_man.extend(y_pred_om.cpu().numpy())\n",
    "        pred_young_man.extend(y_pred_ym.cpu().numpy())\n",
    "        pred_old_woman.extend(y_pred_ow.cpu().numpy())\n",
    "        pred_young_woman.extend(y_pred_yw.cpu().numpy())\n",
    "        \n",
    "acc_om = np.sum(np.array(pred_old_man)==1)/len(pred_old_man)  * 100\n",
    "acc_ym =np.sum(np.array(pred_young_man)==1)/len(pred_young_man) * 100\n",
    "acc_ow =np.sum(np.array(pred_old_woman)==0)/len(pred_old_woman) * 100\n",
    "acc_yw =np.sum(np.array(pred_young_woman)==0)/len(pred_young_woman) * 100\n",
    "\n",
    "index = ['Old Man', 'Young Man', 'Old Woman', 'Young Woman']\n",
    "accs_over = [acc_om, acc_ym, acc_ow, acc_yw]\n",
    "\n",
    "plt.bar(index, accs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del model\n",
    "#torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ACC(male)과 ACC(female)의 차이 계산\n",
    "diff_biased =  [accs_original[1] - accs_original[3], accs_original[0] - accs_original[2]]\n",
    "\n",
    "# 데이터 시각화\n",
    "categories = ['Young', 'Old']\n",
    "#diffs = [diff_oversampled, diff_biased]\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.ylim(-30,30)\n",
    "plt.bar(categories, diff_biased, color=['blue', 'orange'])\n",
    "plt.xlabel('Old')\n",
    "plt.ylabel('Difference in ACC (male - female)')\n",
    "plt.title('Difference in ACC between Male and Female in Original dataset')\n",
    "plt.grid(axis='y')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# ACC(male)과 ACC(female)의 차이 계산\n",
    "diff_oversampled = [accs_over[1] - accs_over[3], accs_over[0] - accs_over[2]]\n",
    "# 데이터 시각화\n",
    "categories = ['Young', 'Old']\n",
    "#diffs = [diff_oversampled, diff_biased]\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.ylim(-30,30)\n",
    "plt.bar(categories, diff_oversampled, color=['blue', 'orange'])\n",
    "plt.xlabel('Old')\n",
    "plt.ylabel('Difference in ACC (male - female)')\n",
    "plt.title('Difference in ACC between Male and Female in Oversampled Datasets')\n",
    "plt.grid(axis='y')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}