4 Classificaion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>mean</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.59</td>\n",
       "      <td>-54.72</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.60</td>\n",
       "      <td>Labeled_-3.59_-54.72_2016_08_01_0.60.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.01</td>\n",
       "      <td>-54.76</td>\n",
       "      <td>2021</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.63</td>\n",
       "      <td>Labeled_-4.01_-54.76_2021_05_01_0.63.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-3.45</td>\n",
       "      <td>-54.50</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Labeled_-3.45_-54.50_2018_08_01_0.15.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-4.31</td>\n",
       "      <td>-54.90</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.56</td>\n",
       "      <td>Labeled_-4.31_-54.90_2017_07_01_0.56.npy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-3.77</td>\n",
       "      <td>-54.74</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Labeled_-3.77_-54.74_2018_08_01_0.15.npy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lat    lon  year  month  day  mean  \\\n",
       "1  -3.59 -54.72  2016      8    1  0.60   \n",
       "2  -4.01 -54.76  2021      5    1  0.63   \n",
       "6  -3.45 -54.50  2018      8    1  0.15   \n",
       "9  -4.31 -54.90  2017      7    1  0.56   \n",
       "10 -3.77 -54.74  2018      8    1  0.15   \n",
       "\n",
       "                                    filename  \n",
       "1   Labeled_-3.59_-54.72_2016_08_01_0.60.npy  \n",
       "2   Labeled_-4.01_-54.76_2021_05_01_0.63.npy  \n",
       "6   Labeled_-3.45_-54.50_2018_08_01_0.15.npy  \n",
       "9   Labeled_-4.31_-54.90_2017_07_01_0.56.npy  \n",
       "10  Labeled_-3.77_-54.74_2018_08_01_0.15.npy  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "import torch.nn as nn\n",
    "\n",
    "from Data import load_df, RandomSimetry, ImMean as IM, ImSTD as IS\n",
    "from Network import ConvolutionBlock\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "path = \"datasets/\" + \"data6/\"\n",
    "device = \"cuda\"\n",
    "\n",
    "IM = IM.to(device)\n",
    "IS = IS.to(device)\n",
    "\n",
    "df = load_df(path)\n",
    "df = df[df[\"mean\"] != 0]\n",
    "df = df[(df[\"mean\"] < 0.2) | (df[\"mean\"] > 0.5)]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(611, 153)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class SatelliteDataset(Dataset):\n",
    "    def __init__(self, path, positions, df):\n",
    "        self.img_dir = path\n",
    "\n",
    "        self.positions = positions.copy()\n",
    "        self.images = df.copy()\n",
    "\n",
    "        self.transform = transforms.Compose([\n",
    "            transforms.ToTensor(),\n",
    "            transforms.RandomCrop((64,64)),\n",
    "            transforms.Normalize(IM, IS),\n",
    "            RandomSimetry(),\n",
    "        ])\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.positions.shape[0]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        \n",
    "        position = self.positions.iloc[idx]\n",
    "        series = self.images[(self.images[\"lat\"] == position[\"lat\"]) & (self.images[\"lon\"] == position[\"lon\"])]\n",
    "\n",
    "        filename = series.sample(1)[\"filename\"].values[0]\n",
    "\n",
    "        img = np.load(self.img_dir + filename)\n",
    "        img = self.transform(img)\n",
    "\n",
    "        return img[:-1, :, :].to(device), img[-1, :, :].to(device)\n",
    "\n",
    "positions = df[[\"lat\", \"lon\"]].drop_duplicates()\n",
    "\n",
    "train_positions = positions.sample(frac=0.8)\n",
    "val_positions   = positions.drop(train_positions.index)\n",
    "\n",
    "train_ds = SatelliteDataset(path, train_positions, df)\n",
    "train_dl = DataLoader(train_ds, batch_size=32, shuffle=True)\n",
    "\n",
    "val_ds = SatelliteDataset(path, val_positions, df)\n",
    "#val_dl = DataLoader(val_ds, batch_size=16, shuffle=False)\n",
    "val_dl = DataLoader(val_ds, batch_size=len(val_ds), shuffle=True)\n",
    "\n",
    "len(train_ds), len(val_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApsAAAFICAYAAAAf0DV4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABXfElEQVR4nO3dd3iUVfYH8O+kEkgyIZSEFppUkd4iKooIorIgWFfXxk9XBRVZV8W17yqWVVks2BELorhiQ0EWBBs1iIBo6BBKQk0mlPT39wcaOOdezWSYN5kk38/z5Hk4d85952UyufNmcu4Zj+M4DoiIiIiIXBBW2SdARERERNUXLzaJiIiIyDW82CQiIiIi1/Bik4iIiIhcw4tNIiIiInINLzaJiIiIyDW82CQiIiIi1/Bik4iIiIhcw4tNIiIiInINLzaJiIiIyDURbh34+eefx5NPPonMzEx06dIFzz77LHr37l3mvJKSEuzcuRNxcXHweDxunR4R1WCO4yA3NxeNGzdGWFjo/s4d6DoKcC0lIneVax11XDB9+nQnKirKef31152ffvrJuf76652EhAQnKyurzLkZGRkOAH7xi1/8cv0rIyPDjSUwKE5kHXUcrqX84he/KubLn3XU4ziOgyDr06cPevXqheeeew7A0d+wmzVrhltuuQV33333H87NyclBQkICkAEg/tfBnZbEf6v4aUvOGBU/p2LvH55KcL2p4mF+zNml4ka2pAEqtr3rMVPFTS05I2VYvEaEDcIvNGYkIlLEO7HYyClEoojzcKmIH7KcyVg9sNXM+fEeGQ+qc728n/feNyfVU/GFfzJSrj3tdRF/c5c8zoaxlidawQYZLzNT8JmKz1LxV+aUHORYDqTtFVHrJq1F3GaHOWORH0cNSC0Vn2bJ+Z8fx+mm4iMq7mOZU1hHxluuNHO+LzwuKADwNrKzs+H1VuRC4L8TWUeBY2vpaTgPEepntboLj48TcVGH5iLe3VM9XwDgtGwRjm67wEg5O2aLiJMjYgM5PQLQ+ZNrjTFPsXwH/scLXzdyKktq2kXGWMPno0UcvizdyAmrHSNiT53aRk5xUoKId3eXz9+wYstlmhqqvzLXTPnhZ3kcfd+tU4w5+zrHy7h7sZFTt+nR16biw/lYe81zfq2jQf8zekFBAdLS0jB+/PjSsbCwMAwcOBCLFpkvc/n5+cjPzy+Nc3N/fcDicexi03wMgSgVxweYU1H088ufcznozxz9LYy25ISXMQcA5A8EiuVxwsLNxTlcPcAe42oD8JTxHzdnWP6bcWZOrHrt9ETpb7aFfpc/ynwBjqot7z08TD0utfRjaTmuP6/rfvzkxfv1JMkXkf5Lhmt1Mjb6L7WB3rl+iHVs+1brPxNHWJMs00Lzz8vlXUeB319LIxCJCE8Nu9j0qO9/hFxpwqMtK09tuebFxJpP4Lja8gcsPiJ0SzBCXViM5fWiSP48xseFzuMbXtt8bY3QzyvLz1mYei56wszjeMLVcaJk7M/FZkR4gZmizkefC8LNczHuO8a82AyvnSdif9bRoH8n9+7di+LiYiQlJYnxpKQkZGZmGvkTJkyA1+st/WrWrFmwT4mIqEop7zoKcC0lotBVoW982IwfPx7jxo0rjX0+36+LZDRK34mok2dOXKfilywH13+qbBP4eZ4w/Q78u5acuSrWbwr2tcy58ksZN/zSzNmtB9ZaDjTHMnZMVu9nzLHT1YAubQAADFfxLyL6AlcYMxo+1F7EN8z51MgpXGT+CbxMQ1X8k/mEmPOC/NNOrJ409kbzuK/+Q8Yfppo5o+bJ2PjrUKDvsDUQ0Wf1zhDxq4nmn3W++zFLxCX+3I3tuiVDxUkq7mCZo9+l3GTJWV7GufxkG1R/BjjvWTPl+eP+fQTAHWXcTxXz+2tpzVPs84nY8/2PIk7+3jJJLXHvx7c3Ut7teK6INw03/+LTuvc2Ed/XQq5f3aKKjDm1w/z4y0w1U2uX+Vei/Hp+rUaVYlVv80X7wNuHRfxAVn8jp1kt+XwYEfejkROplv+UAMozthfpP4UCV197m4gj5qXJhJXmtUDiShVP+f37LHIKf/9GJegXm/Xr10d4eDiysuQLWlZWFpKTk4386OhoREfb/vRLRFQzlXcdBbiWElHoCvqf0aOiotCjRw/Mm3fsnZySkhLMmzcPqamWd3yIiEjgOkpE1Ykrf0YfN24crr76avTs2RO9e/fGxIkTcejQIVx7rbn7jIiITFxHiai6cOVi89JLL8WePXtw//33IzMzE127dsXs2bONYnciIrLjOkpE1YUrfTZPhM/nO9qv6R4c64tzvyUxQcXZlpzBKtYtdD4o9+kFrrWKNwZwjJWWsa4BHKci6U1El6jYthlEb2j63NL76AzVD+sbP87lPBVfcLmZc7Nt59Yxf9P9JgDMVkM/YY9l5lMyvPRxETZ4X/YJBYDdePkPz8Xm4uFdRJw+e5WRs1rvt1tgOVD/+mpgr5nzoYp1uaB+vAFghopftORYeo6W23DL2EfmUE5ODuLjK7Mnmnt+W0vPxLAa1/qoUoXJjS/h9WSv4YOntjSmZKbKOa3UJiOg7I1GVW2TUa9/3GSM7e8iF9ONl9gWCLLJt2zWOeWtW0Xc8u7gdlkucgqxAB/7tY6GThMrIiIiIqp2eLFJRERERK7hxSYRERERuabSm7r/riM41m3a7PsNvOPHMfRHdesP4tafuQwAs/w4rqY/TvP/LDnvBXBcrWsQjuGvW1Rs6ZFtNO22lWz0ULH+aHFb42/98e6DLJ9X6k+NpqbrZG9aYubc/MeHuBDTjbHvPZeJ+PQ9Dcyc+rIZfHFXWbwY+/7sP77j36OaAc8YqRoGx8km+gDw9lsLRfzMMLN2asUHqkZzoOW+R+gBXRdoafirfw5aWI6r63wD+V7bPreAqCKUyI/3K94ja7hjPjZrult+LGMnzGx4/kjiOSL29T9JxDsuMJvFD+ss14M/J5o1e3mOO/W84erjIg6VyKLuBt+Yn4S1r0dDV86lqvupwLxYGbpgjIjb/sf88JtWq5eJuDI36PCdTSIiIiJyDS82iYiIiMg1vNgkIiIiItfwYpOIiIiIXBO6G4RKcOIbhHJUfJuKbQ3F/aE3tVh6jpepk2XsVhXrfRvFCI6+ljG9mWqXigsscyar2PZs0htunlHxBZY59VR8ryVH15avVnEtmO5WcZdNZo6ep87/tKdtT0bVHP6Lv5gpf1Hd7DvJYv7NdTLMOYcsd6Xpim9dX/9We2PKlZBjOTklRs6Kc9R/3LZB6CIV/9WyIUhbpuJulpymZR+mTOuCcAyiylJiLvbFe/eJuM5/Zdz2v+Zh0uvUEfGDsedb7qtyto0U79lsjDkR5ubKUHbzDvli+uO+xkbOxye/JeL64XWMnLL86cPbjbF298oP7Cg5fLjcx61IfGeTiIiIiFzDi00iIiIicg0vNomIiIjINaFbs7kTx3pEW3pv++XPKr5KxW8HeNyfA5x3vDWWsRuCcFyzPzfwgoqvtOToWsV+Krb1/U1ScYvfP61jGsnwM10caqHrMQHgkjJyzP62wB0qtpUqRatYN6H3mPWNxnGuesvM2W4ZO54/9ZkW+/GwiEcNlrWgzS4/15jzv3flN9evp7PZhxqJN9aV55KdLxMOWWqI9M/k45b7CqQGmogMJYfUwqLjKuhrtbb/NU3WyE/t+boxp3e0O43r582RRect7jWb5v+532gRJ//b3CuwKae+iH158oXoniEfGXPePqWPiGsNzTJySvJsL4SVg+9sEhEREZFreLFJRERERK7hxSYRERERuSZ0azZnBOEY08q4/STL2IYg3G+g5qh4sIptvxroEkLd+xIwazZttYq6l+VzKm5nmXOpHjjTkrSljNjiiIptNXy6faSuObWVquxTse7nCZi9WbVAW9J9peL+iWpgv2VStooTjIxErBdxGt4V8awt+o4BP7phmn40h/b3OSAHYlXCR5bjZKu4viVnr78nRUQ1zdq8JiJOuVgW7F/6urlxYfO5r7pyLquunSTiH/5svkj//W+ytnLPQLOHap0jW2Ws+qx+UKe1MaeWWihDqT7Thu9sEhEREZFreLFJRERERK7hxSYRERERuYYXm0RERETkmtDdIFQRbJuB9OX3IEvOFhXvVLEvwPPRG4K0Vyxjo/w4rkfFbS05ZW2m+siP+3FyLYMpKt6i4trmlG9UM/Aoy2H1Zp+/qvg/ljllHcNNc1Xcw58fvcYqtjRJx3IRZSSq4vPkb80pTVS8w3JYvX+pkSVHf9iC7im8xTInGGwbxuJVbPs/EVGVd2X8RhF/0kZuwEFBxb2HNumA3Kn64aPnGDl5TeT55I7qYuTUWyM/ECOvnnx9KI7SL+JAwjrZoD+soMjI8RySm4aKN2yW53JZX2NOnV3yXMIW/mDkBILvbBIRERGRa3ixSURERESu4cUmEREREbmmZtds2jRU8d8tOf9Vsa7962eZk65iW43kAcvY8Wz1mbqWTpdIAmZt3boy7sdfupaydZqZs1EP6BO0FGQOUm3HVx8yc3qrnGYqXmWpb2ym4jd1oScAvGQZc8HE3WWmdFTd7ddas1Rd5/5fZCxLdI7yp55R95i39ZzXtZNbLTlu+Jdl7NYzZTxrgZlzgQvnQkQ11g858vUsftpiI0eXk9uE1ZKfSlIrqYGIixt4jTmHm8j9Dnu6mZdzRTHyU0iKa8sLnDUXyqb0APBVnjzju1+8zsgpSDh63JK8POCfHxu32/CdTSIiIiJyDS82iYiIiMg1vNgkIiIiItfwYpOIiIiIXMMNQlqmis/2Y47e86I35ABmg+talpyynGoZ+17FdS05d6j43wHct02BijdZcvTenjp6d0p9yyTV6P2UaDNluYrPU7839ciDYUaJjBMtm4Em6kLsHMv5BYHl9LSf/TrQPBE1Vh38d67094QCYOvhf7xzLWN6E5E//8kGKj7SzpIkl7Ja/ScYGXnnv3wsKCwBvqyoHU1E5I+2r8tNka1qm5tTIqNl8/LWezNkgqde0M/r9yREyfPdeFWqkVPve3lR4cnTL5xA0Xb5uliyVf2fdAwgRr0GtlrdwshxYuRr5/p/xIi4dpi5QbeWR262DSs0UlDU4ugLWMlhP17IfjuO35lEREREROXEi00iIiIick25Lza//vprDB06FI0bN4bH48FHH30kbnccB/fffz8aNWqEmJgYDBw4EOvXrw/W+RIRVXlcR4moJil3zeahQ4fQpUsXXHfddRgxYoRx+xNPPIFJkyZh6tSpaNmyJe677z4MHjwYa9euRa1agRQqumivZayrirf7cZxtAdy3/6UOx+j6TBtbDdyGAO4rWPRTZE6xGsgq+xgLLGM7VF1ngWrivtQy52UVW+sFE2XYU9VsDrRMeUbF+bbjKvrDAppYcsb6cRzlHBXb2u1m+3OgM1S88FIjpYdnrojTdOf32f7ckUUHFevvU+JOy6RrRZQXu8JMmWXrcF85qtU6ShQsS1eLsM1VZU/Rryj1llkua4YGfkq/2VV00Bhbvlt+UsirD+sXA+CkCPme3tJ88+f3nzfI9StinuUDUsqwv0+yMbZ3qLzQeKrX+2Ue55XM/iJOnrTITHKONnUvcgr9vvwp98XmkCFDMGTIEOttjuNg4sSJuPfeezFs2DAAwJtvvomkpCR89NFHuOyyy8p7d0RE1Q7XUSKqSYJas7l582ZkZmZi4MBjb/94vV706dMHixZZro4B5Ofnw+fziS8iopoqkHUU4FpKRKErqBebmZlHt/gnJSWJ8aSkpNLbtAkTJsDr9ZZ+NWumP8CaiKjmCGQdBbiWElHoqvQ+m+PHj8e4ceNKY5/PV3GLpK3Foz/mqLibihui8jiWMd2q7EU/jvOlipdZcv4RwHF0302zfRjQto6M51qKIGsnyDhO1WzaCkn8alyp6vp0P08d23TzmGNb1TfmSX/OpfwmqAauUwMqDgbwtQzjZpnvqC3HPhGfjYtFPB8fBHbfZX2fbrA0+Lxe1VM9V3ZtUnVTqWspUYiok6mrOAOz4Ih8L+6+v//NyKn7oWyqfdPFtxk5b/77KRGfGWP2try7UaSIdbdnf3g/MOvU46fLJpkvtZBV/WNvN+s8myyQcW3H1ji8/IL6zmZy8tETz8qSmz6ysrJKb9Oio6MRHx8vvoiIaqpA1lGAaykRha6gXmy2bNkSycnJmDfv2Kea+Hw+LFmyBKmpZmd9IiKSuI4SUXVT7j+jHzx4EBs2HOuls3nzZqxcuRKJiYlISUnB2LFj8a9//Qtt2rQpbdnRuHFjDB8+PJjnTURUZXEdJaKapNwXm8uXL8dZZ51VGv9WI3T11VfjjTfewJ133olDhw7hhhtuQHZ2Nk477TTMnj2bveGIiH7FdZSIahKP4zi2LSWVxufzwesNpDy2ApX1iFn2h7hGl2XZup3cpeLH/TjurSqeZMlJUPE/LTm3qPgFFd9kmbNGvaB+YdnkovtzX6LiXyzHVf3Z8ZAlp7OKjdrotpZJ6yxjweeU+cQD9JMvaE/FZ82hzkfkk2TVvaqLe0HFPC4nIicnp9rWNv62lp6JYYjwRJY9gagaiGhqfkLGuIVybTo7puxNRB2/v1LEzS5aE9D5lJzWVcRFdcz3+GLS5Ava/sFtROx917JDt0T+H9a90stI8UTJnDZXWz7s4gQUOYVYgI/9Wkf52ehERERE5BpebBIRERGRa3ixSURERESuqfSm7iFPNyUHgJNUvLEiTuR3+POJdLo8Za4l5xwV22o0tWwV69pQm+dVbKvZTFI1mrZG6rqERZdSTrPM0b3h61lyyuxfW5l1iFMtY1erOE7FlgbogdC1twBW6SfJKSrh3Rhz0l1HZDzrhM6KiEgo2r7DGLt9tSzqX9X73bIP9GNwarnDvl0pYrOlO3D4fFlvOeWRp0X8t2V/MeY423eJeOrZrxg5UerF/+GTLhJx8Qa9+cE9fGeTiIiIiFzDi00iIiIicg0vNomIiIjINbzYJCIiIiLXcINQWQYFMCfBMtZdxfMDOG6g/q3iggCOcadl7AkVH/bjOO39yHlNxbssObqu+XYVp1jmbPPjvstiq+4O5PEMwOPGAw7cFas2CB3Uu6nauXdC2moVn33EzAlXsa1P/ccqHh7wGUn/d9y/CwC8GaTjElFIK1peVw70rpzz+D3Rs+SO1/PPlS9oHQrMF8Hiw/IF98MDPY2ciY3k60H/mbIx/fvPDTTm1H9p0R+fbID4ziYRERERuYYXm0RERETkGl5sEhEREZFrWLPphmzLWEXWaGr9VGxr2N5KxZtUbJYL+qe2ipuqeK9lzjUqtvWdzVZxhopViQ4AoImKAylNqaD6TJu7sdYYu+ugGhgju9vX2m4eJ++j4J3TH/qnZUzX49qa76vn2nnqV+L/lHQxplyGH0WcZjufV22DRFTdJf6iP9mkbHdf8b6In+qhP/kEKFoiX2jqrS0ycuLSZJN5W9P5iOQkEYfVk59AUrSl7A0HX2zoaIzpms276q0X8eX/+MGY89fv/k/EJWt+KfO+/cF3NomIiIjINbzYJCIiIiLX8GKTiIiIiFzDms2qTpdpmGV9wHd+HEfXaOo+lYH2qGyt4nEqrm+b5JGhrX9nfdWgMVrdfp5lzioVx1pydA1kVfOcDI8M+sBI8eAiOXC65TjfBOFcbvAjZ4VlTJZfIkfd3BzxxhSPURzsT9NXIqoJdgwqKfecq+LlhoKrer9rJql+nflOoZGyJD9SxM/tPNvIaRidLeIin5yz8++nGnNKZAru7/qeeX5lSIkwXwTPmb5UxJ/+bYCREzVH93MuG9/ZJCIiIiLX8GKTiIiIiFzDi00iIiIicg0vNomIiIjINdwgVNXZNgQFQ6qKbRuEwlXsteTofRr/U/F1ljnb1OafxCQzp1htG0nOk7Fu8g4Auhd4M0vOs5axkGFujCnTl4ONof/DuSLeFdnVyJmFiWokz8gJiqfMBu3oKHcIbVbP8bqW3UuH9MCllvsqf/08EVUH4U7ZOUEQ7Yk0xs6opeJW88o+kP4Akg6Bn1N5jUuUu4X7TN5g5Dx05bUAAKcoD1jysV/H5TubREREROQaXmwSERERkWt4sUlERERErmHNZlXS1jK2zqX78qcHbrGKsy05r6lY16LkWuZsbSrjaZYDO6qGsJu6vb/luBtV/KklR1P1NtbHpcCP4wRBFHwBzDKb9t6Iv4v4wfnPW+adeI2m7rMPAJfjfBG3e2SgkfPjPVtFXLhtj4jjkuXtALAiShYVr1pVZN550c7jDgrgE8sJEhFRqX61zPckz3tlIQAg72ARFvY2brbiO5tERERE5BpebBIRERGRa3ixSURERESu4cUmEREREbmGG4SqEtumF7c2CM0IYI5t88yFZcx51DI2vqWMF5obY8JOTpB3HblQJoyyHPcOFe+z5Oi+6YHsyXGJfR9Svgwbqm05jc0ZPX4aIOLiwomW49ZWse7OD6Ceiv+tzmxTnDEl7uGuIk7cYDaq76k+HWBTiowdtDbmtMR2Ea/q/J2Rg7eO+7cP3CBERBSAsXW3AAB8ESX4h59z+M4mEREREbmGF5tERERE5JpyXWxOmDABvXr1QlxcHBo2bIjhw4cjPT1d5OTl5WH06NGoV68eYmNjMXLkSGRlZQX1pImIqiquo0RU05SrZnPhwoUYPXo0evXqhaKiItxzzz0YNGgQ1q5dizp16gAAbr/9dsyaNQszZsyA1+vFmDFjMGLECHz3naWGisrnlco+ARdYa05l8We/jpOMjG+XnCHig3fK4so7G79uzJkFeZxGkZuNnCW+PnrEdoIhY5nqTN+rw0UyoZll0gEZXnJwvJESt+8UEb+PyeZx9qkDjVa3Hyo0pmzCfhHXPamOkXNQNZQ/rApP16GXMecnZKqRX4wcxBz3YBSWANhh5lQArqNEFctzKLyyT6HGK9fF5uzZs0X8xhtvoGHDhkhLS8MZZ5yBnJwcvPbaa5g2bRoGDDi6CWHKlCno0KEDFi9ejL59+wbvzImIqiCuo0RU05xQzWZOTg4AIDExEQCQlpaGwsJCDBx47CPo2rdvj5SUFCxatMh6jPz8fPh8PvFFRFRTBGMdBbiWElHoCvhis6SkBGPHjkW/fv3QqVMnAEBmZiaioqKQkJAgcpOSkpCZqf/MddSECRPg9XpLv5o1s/3dj4io+gnWOgpwLSWi0BXwxebo0aOxZs0aTJ8+/YROYPz48cjJySn9ysjIOKHjERFVFcFaRwGupUQUugJq6j5mzBh89tln+Prrr9G0adPS8eTkZBQUFCA7O1v8Vp6VlYXk5GTrsaKjoxEdHW29jaqBh1S8WsWptklyE0R77DVTesswdpZsDv5C7lhzzn1qzPbsf0GG+z0y3t7d3FQU+UOsiH/4+w1GzodZX4n4v2/mWO68/N7CeyLutVBtELrSMukkGXa8qLmRsuymgSKOt2yU8mGeHFB93xv9Rz14AHbctlPEJdhp5DRS3eKz1e/EP2CVMecI9EYj874hGr87ltsrVjDXUYBrKdHvaTZX/bxfZM8j95TrnU3HcTBmzBjMnDkT8+fPR8uW8pNeevTogcjISMybd+xFKD09Hdu2bUNqqvWqgoioRuE6SkQ1Tbne2Rw9ejSmTZuGjz/+GHFxcaX1Q16vFzExMfB6vRg1ahTGjRuHxMRExMfH45ZbbkFqaip3UBIRgesoEdU85brYnDz5aK+9M888U4xPmTIF11xzDQDgmWeeQVhYGEaOHIn8/HwMHjwYL7yg/jZJRFRDcR0loprG4zhO5RcvHcfn88Hr9QJvAaj96+DIyjyjCtRVxSsDOIbtu9lSxVsCOG6gdOmc6gOOyyxzvpBFhatU43IAOKWkvRzYohK+guldFS+z5OhSyotVbJZsAn9S8f2WHNXMvJ2nnYjX2epSA+DoJ8BdlqRcGW5+fL+RMj+2tohn/c/8Hsx8/RI5oB/fxjCk7Bgk4jPQwMiJgGzAvFf9TvwLoow5XdFCxBuRbuT8gCnHAh8A79G2Q/Hx8UZudfDbWnomhiHCE1nZp0NBFlarloh3TtcLPdC/6QYRHyk2f3aqmyLHrA5sU3u3iO+pb64PVH6+3BLUbbvJr3WUn41ORERERK7hxSYRERERuYYXm0RERETkmoD6bFaIbgDiKvskKthKFf+fil/14xi2+tauKt5iyTlbxfMsOYF4VsXvqHg2LGQz6sawfBJKkYrHqPgLy2H7qLiNJedpFeepOBamhSr+hyXnERmmGzWFtpq6BSpeZ8lJsIwdJ8Yyps635XeJRkrx4F0i7jSwk5GzpP61It757hSZkGXe9Tb1vd1iPMBAFBqJuDYKRNzGKKwFeqnmoVHobOT8IJY7B0CxeYJEVURxD1m3vrzXa0ZOpCfcGCOqDHxnk4iIiIhcw4tNIiIiInINLzaJiIiIyDW82CQiIiIi14TuBiFzP0LN48+GIO3DAO8rkA1BHVW8y5JzWxn345hNvYHBIopHHTPlYRXbNgRpS1Q8wpKzvIxjJFjGDql4piXnJbUJR2/cOWK7s6FlnIwfrrGMfaTiyWbKgAM/iXj+mq+NnOzWZTxYZo9p1FW7/g5jjZGzA1tFnKI2DEVbdg5Gqk8PyMMvlhOqd9y/SwDsseQQVQ1OmHzOhxmfoEEUOvjOJhERERG5hhebREREROQaXmwSERERkWtCt2aTQt9gFb9iyemg4i0q7mnWzSXUkU28I3dbjmuWEJath4o3W3J+UHFtFZ9smaNrVw9bcn5W8Wl+zFlhGSuv5pYxXR+qzw1Ag4XJIs6JN4tKD1+3+g/vOnK9OdZTdXq39H1HHeSLuFgtU63R3ZizW9VffmN9gtQVR2XNJlVlhXHy5yLcw/eOKHTx2UlEREREruHFJhERERG5hhebREREROQaXmwSERERkWu4QYgC94wfObpn9zUq/tGcUvfpdnKgoeW4V6g4WcVNLHPyVPziTktSYxnqjTtJlintVWxpZo7vVVxPxYWWOcHgs4zdoeK5Zoq3ifxUhdYXRhs5cW/IOFf1Wldt7AEAxfCKOAYtjJx8yB1h2ep34mKEG3NWY4OI96DIyGly3G6vEhRbP4OAKFQdGtlHxBc9PKeSzoSo/PjOJhERERG5hhebREREROQaXmwSERERkWtYsxlKvCrOqZSzcFW4U3ZOfdVs22wnDsT8VQ3o+HHLpFgZ7sdeIyVR12zqwkNbPWZPFX9nyXlaxRtUHGOZc1DFP1lyyvKqZWycijubKUc8m0TcICzdyBnyiVw+FvaRdZL2hu2rVHyukVOiCnBjVWf9n7HDmFMA+UEAJ1tqQWsdV4BbjCLWbFLIWvdCb2Ns5Z8mitgbZls0iEIT39kkIiIiItfwYpOIiIiIXMOLTSIiIiJyTejWbOacCsT/enrZX5u3163Y06kQukZT947MrKgTCaJzZFiMOiLug9uMKWMxRMR3Ww77n7Lu9y5zaAU+E/Hm0wuMnP7nywLG+rql4xmW+zpNxcssOdtVPFPFhyxzalnGtNYq3qjiA34cw2xJiR3JstflppW64SjQrFAWwZ4WkS3inB7mceMwWMQZ8Bg5xYgXcR1VzBxnKXA9rGo2o8y7RtRxD2iRa41NiU5cUov9xhhrNKkq4zubREREROQaXmwSERERkWt4sUlEREREruHFJhERERG5JnQ3CA34/tjZfWi5XTcHv8mSo3cJTDrhs6pYVXFDkDZXD8idMInGDhegEI1E/I8A7jbH0hF/F0pE3KRerJGzJ+NbGRsZtY2ROMhdRE07dTFP6EEVN1GxbUfLbhXbGuJHWsaON8My9k8VtzFTTiqWrfS/STAfz9gdeSKumytvj0Y7Y04TNBVxGLKNnINqZ9RB9XzwWDYVRavddA0sPzxRxy13ugk8USjZt6aBOdi1wk+DKGj4ziYRERERuYYXm0RERETkmnJdbE6ePBmdO3dGfHw84uPjkZqaii+++KL09ry8PIwePRr16tVDbGwsRo4ciaws2yckExHVTFxHiaimKVfNZtOmTfHYY4+hTZs2cBwHU6dOxbBhw/DDDz/g5JNPxu23345Zs2ZhxowZ8Hq9GDNmDEaMGIHvvvuu/GeWdty/dX0bAAxTsVlSBiwo/936ZbiKP3LpfvyhO56nWXKMuslKlC/DrOgMI6UOdoi4oaWusyxe1QgcANrhJBH/0ORbI6cYceq+5ZzD8BlzmqKPHLA1Y39MxdNVrJu+A8ARFdvqOtdbxo6Xbhn7RMWplpw8OegbYH6f8ufJms0SWY6J2kgx5hxQDdvDLIWoTREt4kRVs7nXMme/erC+snzqQ+PjcopRbNxeUSp0HaUqKeKgWZdMVJWV62Jz6NChIn7kkUcwefJkLF68GE2bNsVrr72GadOmYcCAAQCAKVOmoEOHDli8eDH69u0bvLMmIqqiuI4SUU0TcM1mcXExpk+fjkOHDiE1NRVpaWkoLCzEwIEDS3Pat2+PlJQULFq06HePk5+fD5/PJ76IiGqCYK2jANdSIgpd5b7YXL16NWJjYxEdHY0bb7wRM2fORMeOHZGZmYmoqCgkJCSI/KSkJGRm/n4PnwkTJsDr9ZZ+NWvWrNz/CSKiqiTY6yjAtZSIQle5LzbbtWuHlStXYsmSJbjppptw9dVXY+3atQGfwPjx45GTk1P6lZFh1oYREVUnwV5HAa6lRBS6yt3UPSoqCieddHTTRI8ePbBs2TL85z//waWXXoqCggJkZ2eL38qzsrKQnJz8O0cDoqOjER0dbd4QC5T2bs41b8bHfpxsdxWv8GOOPz4K0nHKcpGKe1hyxqvYtpdGN/4uDPiMTpz6VjdEkZHSAvX9OFCJisv+vekkdBTxD/jGyDkIueklWTVx36c2L1nZ+oXrDxQ4YNyx6eey7yogo1V8oyVnnNzldLbaKAUAb50ldzX5wuTmn2TL8nJQNfXPMTrXAw3RXMR7sEnE2ZYdWGvQScS7YO7ezsPq0n87ldzUPdjrKPAHaylVOU3nHzbGiv8q17xwDzsXUtVxws/WkpIS5Ofno0ePHoiMjMS8efNKb0tPT8e2bduQmmrb7kpERADXUSKq3sr1zub48eMxZMgQpKSkIDc3F9OmTcOCBQswZ84ceL1ejBo1CuPGjUNiYiLi4+Nxyy23IDU1lTsoiYh+xXWUiGqacl1s7t69G1dddRV27doFr9eLzp07Y86cOTjnnHMAAM888wzCwsIwcuRI5OfnY/DgwXjhhRdcOXEioqqI6ygR1TQex3HMDsmVyOfzwev1Am0BhP866FbtWqiprWLdCPx9yxyzL3loU32+H53/spHSofX1Ih7+mdnC5dM2X4g4up3sXegxHkzgHBV/BPMFPEIVT3b/vLGIvzhvvzHnQsh3nBLvbGTk4Em1M/hsdbutYftGFa+z5ATDhZYxvbekvyXnr++K8OU2s0W8AWZj6k2q+fphVSMLAHXRVsQ+9YkN9SwN+zepZvzpMHduF2Fz6b8dXxEOeP+HnJwcxMfHG7nVwW9r6ZkYhgiPLtymUObpdrIxNuXjl0TcKCK2ok6HyMqXW4K6bTf5tY6ywpiIiIiIXMOLTSIiIiJyDS82iYiIiMg15e6zWWHcqk8LFTGWsUtV/KaK91jmnKbiiqzh1GVFP/kxZ5sMx590vT3veCPNWpC0q2S/xoPtvhbx5TjLmLMb6SJ+H3OMnI6vthFxSmF7EcfONb8JGf22iDgxLd/IMRprzpPHRW/L731d1Y/nITPFn7afhptVbOs/O0TFD9sOdLmIblAx8KMx4xp8LuI92GvkRBq9V+uJaIOlN2cmtoq42NKcNwzHnkclldpsluiPOT+Yi+mLB/qI+KEG/iy4RKGB72wSERERkWt4sUlEREREruHFJhERERG5hhebREREROSa0N0gVN0dsYxNKWPOuZax7iquyA1CgdSn11GxbdOL9l9zqPujchNJodo0kosPjTlLkSXiC1XzcAB4up3c1PLlQ0+JuGtnvQkGaNxUbmDKKDSbmTczNr1sVycXbcxB7SYytm0q03unzP73JrVXCYstOafK8GV8aaT0RYKIO6O3TNjYxZhzXuuVaqSxkfM+fhFxHdQX8R4cNuYUoFjEjmVpixMbhApgtucnIiI38J1NIiIiInINLzaJiIiIyDW82CQiIiIi17BmM5Tpery/W3IGqPgUS45ZZlhpah9KCcpxTmp7hoh3YaeIV2OiMaehKkQ8GSOMnEVnPCni0fpHpLN5LvvXNBVxt29sTd13qVjXcLaBYYHlMMHwrh856vTiUWCkLMBcET+mDjwefzbmXIKry7xrXfE6Ga+J+LDl0w0SVEFrBszvwX7EHhfZvkdEoevNJXL9eugCNnWnqoPvbBIRERGRa3ixSURERESu4cUmEREREbmGF5tERERE5BpuEAplukH32ZacZ1W8N0j3XVvFGyw5uh/3GZacr2V4GLeV/1wcc6gB2os4X3WHb4ILjTmv7n9bxKffGWnkdHo4VcQvTJHNwtFjoTEnsv1FIk5KKjFy+mbpLv66u73t9z69icXS+N0tk2S44+psI2V/90QR71Kne07uZGPOJeqJNMmye+1iFffHKBGPxcPGnB9Vg/kmqGvkJCKn9N/FOIK1RgZR6ArP4cs1VV18Z5OIiIiIXMOLTSIiIiJyDS82iYiIiMg1LAKp6m5x6bhJKm5kyflcxUMsObLXNi7La29JUsxe4IYGKBRxhmr0nQKzHrPpixkivue1O4yce2COCSMKjaHvYx4T8UlTxxo5fc+tp0aSZdixlnlfqaq9+WtmiknVmCLcn0llemreN8bYhd3PEbHPly7i1vvNx2o6vhDxPqQbOZ1xv4jvUv+naep2wCwpvh//M3L2HXecIoTXmJpNT7cO8IQfrfd10tgIvKryrq/sMyAKHN/ZJCIiIiLX8GKTiIiIiFzDi00iIiIick3o1mz2xrGzu9Fy+1UVeC6hrIuKUyw5nwZw3L+peK4l53UVf23JyZPhGfhEJZxnzun2h2d21AJZk3nwTFkfWGeublIKDModIeIVbz5u5KRdpwaKVByzB9pi1ZS0/2o9CQDayjBa9eLsGGVOOajG6lsOq3uQ7tM9PoNTs1nrzm3GWNzfZa/QK0pko9X0druNOadC9uYcgkuMnAEq/g82iriN0YAWOA89RDytcKCR8+/IVaX/zsNBzDcyqqctt4QjrPbRxbSl2daUqojEtbJXb7Ejf9bDPXzviEIXn51ERERE5BpebBIRERGRa3ixSURERESu4cUmEREREbkmdDcIza0FxP/W1Lqjeftf9svYs9n1UwpJP5YRB2pMkI5zoQxLZt5c5pS0ke+JuNWOC42csG3yqZuwQ24a+eUTc0PL06f8V97PXyx3nqXiv6tY9VkHALTeKsIDsXMsSb1lmNBYxlvNJvTG3h5LirGByZp04h7EOGPsPSwWcUSS3A22G4eMOX0wSMQDFlju7CwZ3ja1jYhz+5tTPl8gO14vutr8QfgnLir9tw8+3Ge56+poUb83ER939H2Fzu+NMm6v96Hc4JawwtzYVbx+kzsnVxN45KLh6Wq+nh1qESvinaebC82IM5eImBuCqCrhs5WIiIiIXMOLTSIiIiJyzQldbD722GPweDwYO3Zs6VheXh5Gjx6NevXqITY2FiNHjkRWlv7bJBERAVxHiaj6C7hmc9myZXjppZfQuXNnMX777bdj1qxZmDFjBrxeL8aMGYMRI0bgu+++K+c93AQgGgDwl03djVvrvzBUxM84z5uHePcOGf+5nKfgr2gV57t0P1XRTBlOxAIRD4GsxwOAtS0zRByW/rORE3bQK+LCqfL3pqunmw3b8z5XA381UkyDVPyVJUf1GB+8sbMlKU6GWTEy7vaxOWXbcBmbJZBAHcuYC67COcZYwdvLRfzNlY1EvAM/GXO+nLNWDpxraeqvXS3DOEvKkGbyeRS5NNbI6fj8vaX/Lg6RH1L311Eg2hOJ6F/r+9JPf9NMOF2GK/PNx+bzXHl+b/zUV8QxS8wnYsMfZBPyyJ/MOurivfus5xyqIlrIT83YeG1TIyc/RT5+o3rI79kNdV825jQMr6AfZAqqjw7JdWZ3UbyRc4N3Z0WdTkgL6J3NgwcP4oorrsArr7yCunXrlo7n5OTgtddew9NPP40BAwagR48emDJlCr7//nssXrz4D45IRFSzcB0lopoioIvN0aNH4/zzz8fAgfIj4dLS0lBYWCjG27dvj5SUFCxatMh6rPz8fPh8PvFFRFTdBXMdBbiWElHoKvef0adPn44VK1Zg2bJlxm2ZmZmIiopCQkKCGE9KSkJmZqb1eBMmTMBDDz1U3tMgIqqygr2OAlxLiSh0leudzYyMDNx222145513UKtWraCcwPjx45GTk1P6lZGRUfYkIqIqyo11FOBaSkShq1zvbKalpWH37t3o3v3Yhp3i4mJ8/fXXeO655zBnzhwUFBQgOztb/FaelZWF5ORk6zGjo6MRHa132AB/fukqRMUc3Qow5Y7W5kRVw/70s38zcwrk2C518xLMNabcgnUiPhXNjJzZ+EHEvvzJKqMa7Bq9XMXvBuewG3CbiFs7Lxo5D+AqEZ+ff8DI2XKBbOqfmeGIOO8fljvf6+dJijtS8Z1mSv/Nsst43Wm2xuo5KpYbZfbNXmHM+B5yM8fQ9h+ah22kYv0ktziEV0RcB9eXPcmi8fuy8XRK554izm5x0JhTlNPXGCs3y3LgUYc962P9wACPfvNA6b8PF/twBf594ucSADfWUeD319Ly6mo5RtfodBHf01/GsDTa1/RmCgD4z5azRbxzqfywgyYLC405tbbI9aB43cay79wPJf27iXjvuCNGzkud3xJx7+hAPkSBm4Gqogf2nGyMLb+gpYhLvOZz/OxZr4q4daSZUxOU62Lz7LPPxurVq8XYtddei/bt2+Ouu+5Cs2bNEBkZiXnz5mHkyJEAgPT0dGzbtg2pqanBO2sioiqK6ygR1TTlutiMi4tDp06dxFidOnVQr1690vFRo0Zh3LhxSExMRHx8PG655Rakpqaib98gvKNBRFTFcR0lopom6J+N/swzzyAsLAwjR45Efn4+Bg8ejBdeeCHYd0NEVG1xHSWi6sTjOI5TdlrF8fl88Hq9yOmQg/jwXxukrgnwYKpZMS5SsdlbF9iu4hQz5eomsm5n6p6/iPh12ckEADDqdVnfhj2W+65M96hY12huDtL9dFTxSEvOvzuI8I3v7zBSuraX3da79JS7ej0/jSjzVG65daIxNilC1pR6nvYYOdqVGCPiS9DVyBmKUWpE1nAW4H/GnCX4VsR1rtWFtEC3fb1F7FGdcR7fc5cx5248IeIpMGuIrkGuMaYNadRKxHk754nYC7OZcZtr+4n4yTcsB05UcU8VX2iZo55XhSvNlG9WHmsFdKjAhz+90ww5OTmIjzcbMVcHv62lB9a1Qnxc9flk4lUFeSJ+eY9ZMPr54q4ijtkZLuK+w1YZcx5vMkfE9dlovUY5UHxYxN0/l68FHf6xyZijP5QgrI75nPm/H2TJzMjY6tOSzJdbgrptN/m1jlafFYiIiIiIQg4vNomIiIjINbzYJCIiIiLXhG7NJnIQjxOrpbp6z1YRv1lf1qE5kDtCAQCfytBzdi8z5ylZVLa6u6z1GXT+QmPKLvwo4stwq5EzHRPlwDzZixGPTDXP5asCc6wstj7SV6nYVs9aWZ5uagxNuPkpEd9d61ERn6oebwDQH/SXcNOlRs6BmOkivu+da0U8ecjHxpx9b5h9QLVr8IiIp+BUEftU71YA+BwrRdwelxk575+SLeIJq29QGWavS22MZexZlL0shEPWs0bOGCfiay962JjTsU2MiG/Z4Mfvu7pG85Alp62Kba1uj/ux9JX44N3rZc0mUQ108w6zq8PqR7uIuPbMJUG5r50zZUH56j7TgnLcUMCaTSIiIiIKCbzYJCIiIiLX8GKTiIiIiFzDi00iIiIick3V3SCkem1f0mO/kTJj7udyIGGFCB/E08acB9S+Hc+k18z7XqoacGerjtKDPjTnoLaKnzAyWqldDZt0R/nd8vwBYNBLV4o44QNzY8z7q041xlxxpWXsExW71M82XMXF/kx60hzKaCx/HJq2lLfXf+NmY86+lyf7c2/CWWgv4gfRzcjJQraIL8EX5b4fG/1MPGzJcfzYIPQEzhLxS7XyRfyPof805tw9Q26weq2++U0YujdaDnykErZZTsbcb/eHfPDBC24QIqpqCh25uj+5T77+3lTX3GwZ6ZHP/4svuM7IKVm51hgLhvXP9RHxphEvuXI/lYEbhIiIiIgoJPBik4iIiIhcw4tNIiIiInJNRGWfwO/ZeBIQ+2sh3tp08/Yvnc0iXvL8HCPn7wk3ivhnnC3iZy33+8AkPVJoJvXuqQbuULGlWTx2qXiNkRGBgWpE/h/jfmptzNn4004RD+2fauRcPErW38X9+zkjZ1XGDBEvx9dGTpneLv+UYPGrRlP7uzn0r1XfiLjlKZEi3vfsZ4Hck+Er/PKHMQCMMqorgyNYP/R34isR982ThZMpH7Qz5uzBOyJ+Nqy5kTMUfxPxZPXjtmOY+dz8V8YZcuAZIwWF5x3370IgSCWwRFSBIj2yQv+zR2Xt+Ntt5Os8AFx9yVwRO2vWBf/Efkfjr9TAiAq765DCdzaJiIiIyDW82CQiIiIi1/Bik4iIiIhcw4tNIiIiInJNyG4QuvWVuYiIrQMA8H1mNjNf8fEHcqD3diNnBy4Q8adoKuK7LPebojbuNMCFRs4emA1hpU2WsV4i8iDRyMhEpprRRMRpZ5kd0TN+knMWrGxk5DRJPyjirMZmAXWrTLk5KbvweRFvwHRjDrBXRCdbNkb9ZNkIVZZWOF/EO7HayMmDbFTfByeLePVo1XgfQP2oJBFvq/u+kfNSutpo8oJKeNeY4h/V9x9+9A9+zdpu/cT501f/dZwj4usw93cyj6mHAhG3cJoaOW1V8/rMi2caOU9MkRuN7moyVGXsNub86wm1Row0zy+y73H/Nm8moipo34VynWx13UYjZ/bS/iKOKlru6jkdL25jrog3Fh40clpHxlbU6VQavrNJRERERK7hxSYRERERuYYXm0RERETkmpCt2fy2+2dAfNTRoN7LZoJRSmnWQK5QNZDTVM1mF8v97lR1h0lYb+QkqCbu96GOTMiTDdIBALVk024HS40UH+JErGtOS/CzMaegraxfW5lg3vWOtfJ3ivw9i8z7LtyvRuTjeU5/s76x2TB53LN9Zr1oxw9aiXhudImIP402H4ervm8jYgdeI6f3qFoi/s6bL+J3hw4z5vjSZd3hts3m/wkXqfh1MyUgftRohpJFkDWvZVUpA8B8vCjiky05iWgh4sVfvmbk3HWv/tAEXffbAtrz6nxH99YfkACIH6dCAObnQFR7u4sPGWOvHugu4r/VM+usoz2scqXQ9GwPWUj/eN+rjJyoORVXo2nYmCHClfmNjZTWkf5U0ldtfGeTiIiIiFzDi00iIiIicg0vNomIiIjINSFbs4n1bwC/tZ5q788EXXMI/OKMEfEVOEnELWDW9W3GxXLgozZGTskQOXbvlLNkgu7NCMCzSleIvmomIVtEO3fulDc/7TGn7FA1WBPMHmN7MuPlwKafLPet7gs9RLSvdga0C7fK2pif08x6sF2d5FNsV9Q6Ebfbas7ZiyIRD0YtI6eLLAXF5h7RIj43qq4x594LVH/GZ40UYJ3sA9nnHdnz85xHzd/PvqydJ+KlD39tOfASFTuWnOBraBkzu1Sa9padAkB+72ZAfm+342NjxmKoGs3mlsO+tkrGl6vbW5h9bPepnrR41HLc48s481BjajZ7vPF/CK919GeoxSc5xu1OmlwPZs+5xMj5+hSzHypRKBhUu1DEvd4wF/Y+b/1NxC3vMfctuKUkV/bZfPCnC4yckX2mVdTpVBq+s0lEREREruHFJhERERG5hhebREREROQaXmwSERERkWs8juNUzE4FP/l8Pni9ZhPvgPxDxf/SCS0sk2JVbDY41ptneqlND8v0JggAQJqKu1tyPpDhO6qY/7/1zClFctMTEi1bQvaqhrGzfjFzaqvHvLbsbt5pr9w4AwAD0FXEP+JHI6dL+/NkPFzevm2zeSrh++VmpHYvmP/vBim1ReyEy9vn7C025nydOVvEi1c8aOT0XCEb9sc8Kw/c7xXz8U3aJDcjNcxsbeTExcjzzZy1WsQ9tmUbcz6BLBp/SDVNr0iOdS+hLMw/C7K58gL8uewDD7eM6R+5kSp+zJzScOMDIs664EEz6binvQ8+eOFFTk4O4uPjzdxq4Le19EwMQ0Q5mrJHNGtqjA2d84OIb0zYccLnR1RRzv1FbvR0BlTe83fjO+Zr6YazplTCmZw4X24J6rbd5Nc6ync2iYiIiMg1vNgkIiIiIteU62LzwQcfhMfjEV/t2x9rgpmXl4fRo0ejXr16iI2NxciRI5GVlRX0kyYiqqq4jhJRTVPupu4nn3wy/ve//x07QMSxQ9x+++2YNWsWZsyYAa/XizFjxmDEiBH47rvvgnO25fWIioeouN+WAA8s6y+XqdpFZFqmzFPxFVGWJNWI/Ap1oCtks3MAqP1pAxEf3m4pwf1MtfF+fIuZ0zNOxre9JcI1e83a1TXQzWlTjJxRXbeKeINq0P/unz415gzKbyniH08ya0xrQdag3bDzTBEP/EwVcQLI2iqbr8c2eMrIqXuS/JHIhjz/8DPM71viqmYijkmqbeS0VL32z2l8iohnbjOmoBamy/sxUywfZeCOp2E+98ap+DxsEPECfw78kR85C1V8YTsjZfeqN0X886YHjZwOx382gAMg34/7dkkor6NFGduNsbfvlz/r/f/9jIg7RJnPeaLK8IbPrKv33C7rCR2YNZueXnJN3jpEvia2eEbW2QNmw3Z/OCXlnlItlPtiMyIiAsnJycZ4Tk4OXnvtNUybNg0DBgwAAEyZMgUdOnTA4sWL0bdv3xM/WyKiaoDrKBHVJOWu2Vy/fj0aN26MVq1a4YorrsC2bUffkklLS0NhYSEGDjz2mXDt27dHSkoKFi36/Y+Gys/Ph8/nE19ERNVZsNdRgGspEYWucl1s9unTB2+88QZmz56NyZMnY/PmzTj99NORm5uLzMxMREVFISEhQcxJSkpCZqbt78pHTZgwAV6vt/SrWbNmv5tLRFTVubGOAlxLiSh0levP6EOGHCt67Ny5M/r06YPmzZvj/fffR0xMTEAnMH78eIwbd6z6y+fzcZEkomrLjXUU4FpKRKGr3DWbx0tISEDbtm2xYcMGnHPOOSgoKEB2drb4rTwrK8tam/Sb6OhoREdHn8hp+O80Fb9rydF9s9+w5LRQm2mgioS/sMwx9lcssCS1t4wdz3xn45KhsjC/I1oZOendZFP0pLRTjJysl+QOloV75KaiWPxkzFmp4iZoY+RktPpIxNMjZGP19amqcT2A7rhMxNugH2/gyXVnivjgEnn7j7qHPoD03XVE3Pa6BCMnpoHc5NL8yyYiTsjWTf+BvbFbRLw5zdxEdMjpKOKvVsndKSthbpR6Dy+IuNDIAE5X8Qj0EvFf8LQxp42adcBy3ED8HXeJ+E7cbyadrGLzaYXwJBmnnCQ3weVMNRuUD9h1pog7fG45weM36RUC+NCSUwmCsY4C7q6ldT6QP2CXNpcffrDqb/K5SlRR8h25Mr5181AjJyJG5qyfan6oyrwzJ4m4ZaRc68/pbznu7fL1oWSV5QNTlMT5tczBs8ucVuWdUJ/NgwcPYuPGjWjUqBF69OiByMhIzJt3bEVPT0/Htm3bkJqaesInSkRUHXEdJaLqrlzvbN5xxx0YOnQomjdvjp07d+KBBx5AeHg4Lr/8cni9XowaNQrjxo1DYmIi4uPjccsttyA1NZU7KImIfsV1lIhqmnJdbG7fvh2XX3459u3bhwYNGuC0007D4sWL0aDB0T9zPfPMMwgLC8PIkSORn5+PwYMH44UX+OcVIqLfcB0loprG4ziOpQt45fH5fPB6vZV9GsfcZBkra93fahmbquLDlpzH/viwsUgwxu7D1SJOhfnuRyyyRbwXZp1kovq9o3CNrHHZfGSvMWf79l0i3u8x/1N18uT5LGooa2UiIsxGuXf873oR99tp1mxuVY1xW86R8XW6gT+An/fJetHEx83aygvSBoi4VpJsH9Nno2wODACbo2XV4+FlZtfeotny8Xx3g6y/3QJzTjOsFfE1uNzI2R4hm9d3U7XBZxozTDPgMcYuUbGtofw+/PHS4YHZWB/q/9nWLIFF7EEZ7zpHxi3GmetD83Pl922rpQl99HG11UW+Inzr/RY5OTmIjze/p9XBb2vpmRiGCI9Z5xpM6980a+A2DXzd1fskAoBi1SX9v4fqGjmDa8v11hsW+Ea846XlF4j4msljjZzGT3wv4kMj+xg53z77UlDOp6L5cktQt+0mv9ZRfjY6EREREbmGF5tERERE5BpebBIRERGRa3ixSURERESu4QahQFyn4gtVfEGAx12g4jObi/B+DDampKoG3Xtw0MjZhT0iPozNRs5SrBBxR3X76fiLMecAZPftTBQYOfshm6QXX7lMxAnvyP8jAOy8ThYaT+78qJGDe1RjXNuGK6Xzc/NEPODSlkZOt9myKf5Zp8rbUyzNwn9Okf/vtMVLjJzCqBQRxz4kN1cdhtlo3xMhm9AfiTR3nmXHy2b80S3lRpmxi83z9cc8tWnoWUvOR2VsEBqKOsbYZ358o/S2rWIVt+9tzulw8kkizh1hbhLI7Hjsd+vi3CKs6ZrGDUJBEtG0iTHWcqbcVPhcE/Pngqi6uz6jn4gXzu9s5Ky7enJFnU5QcYMQEREREYUEXmwSERERkWt4sUlERERErmHNZiAaq/gyFdsu4fXY42XfTQdcJOK3jTsC2kDWpn2JlUbO99ghYs9xza1/kwzZdPysbFlXUrw/2ZizZUG2iFutaWTkNH9GNtNdhqUi3gjZNB0Auqm6vtjkgUbOyUdkvWjk4StkQkNjClBbhoX3mimRusxQn14hDIujZbzfs8HIef+qLBE3y9gm4sPfmc/5n3/8WcTFHrMJfbu9slZu3CsjRNzCmBGY+ZbG7wPKqNm08ViOU15mNSZwepz8xkV0N2sI19Y/9pgXFzpY/0k+azZdVDC4p4hfeGmSkdMhqrYxRkRVA2s2iYiIiCgk8GKTiIiIiFzDi00iIiIick1EZZ9AlaRL51TNHib4cQxrzaasRvtZ3fqTpd5tF2Qt4EjVdxMARv7YTQ58fMS867lxMlaNNvfmyhpDANjw7psi3g/zuPuxT8TtUSLinZB9IgFgfNIWEY/aa9YGHimSJ9gT8vwjY/5kzNH9UCNPNlOM1pApKp5qTumrSjT39DnJyNmoij0zm8WIeFNP/d0GGmfL73fkVrOus07vWBG3WKQS1hhT/DIBm0T8JM4ycvYHdugTdsAyVpwrnyMxC82sX+rnHQtKjJspyKLmLBfxzX+91chx/iZ7cU5qO13EnaNUP10iqpL4ziYRERERuYYXm0RERETkGl5sEhEREZFreLFJRERERK7hBqFAbFHxUwEcI80y1kNvapCbZ9ZjuzGlExLlwP5e5nHHqDg3x0g5HLNMxL/slrteUr5T9wMgX216ycFOI+dAfbmppfnepiI+HeHGnFrxrUV8WlY7I+db1Zh+VfhnIv4rLBuE1LN9Vw8zxWxLL2XfZI4VqbiBZd4t6CDiHaqvfpOPu5uT1H4g9dABAGYVqY1bHVRCgBuE2qGViPvinQCOUmyMJOJGEe/HiwEc17RDfRDAShUDAAqO+3dIfZRFzaA3DAEA5shwfPOLRbz5L82MKT/e9KyIIz3mGkJEoYXvbBIRERGRa3ixSURERESu4cUmEREREbmGNZvBUFB2iuFry5hRQ7heROnIMKb0g+pM/oHluN9+K8ISfGWkTGouT2j+4oUiPhldjDkXqPteaHS7Bxo22iHi1d7GIh60sb0x55L1stHzMsvvRHtUpeSGJDnnww2TjDk9m8ha0C8tj2cM+or4AnQVsWyhf5TZwt1Clcl6D8uiwYOnmw37Y3vLuL6lF3/yDZFyYIaurTQfX7SST7T3RpspEep59PmisqpZbcYZI/v1kjP4THPaHNUl31KrrH2hiv/+A/M/9VjeccdlzWZIKtoqfybjNzcxclijSVT18J1NIiIiInINLzaJiIiIyDW82CQiIiIi1/Bik4iIiIhcww1ClSXBn6SDIvpWNXkHgOsQL+I9r+wzcr7D5yLujR1GTtJWn4h7oLmID1u2xsRCNpD/k5oDAN+u/kXE7RAt4ng0NOYUIk/EJdhr5BTEyP/neQdOEfE/Lec76rEWIs4YUcvIWZO8QMTpa2qLuHOnEmPOdshNOj0PtTZyvEtlvOWUH0S8o8VWY04jtQGrc4y5FWnw23rjzhUi+vEhc3PNuOXniXj+5lVGTvdFZ4j4WUwzchbgbTVypYpHGHOGq01vo+YMMHKGGpt79PcpzjLnaRGvb1rHyEHmcd9LxwFg2XFFISV72KHKPgUiCgK+s0lEREREruHFJhERERG5hhebREREROSaqluzeb2KZ1lydgZw3KYqLrufdGCuM4eS1Vim7MWOpH5mY+0+f5U1m9elDzZyliXuEvHy/acaOS1U7WdTVRfXCrKGDwBaQ3Udb7vHyMldJ2s0t0DWJmZZ6kcHorOIT7cUuHY4ImsI62OQiD89+Xtjzpc534i4xX1JRk5Jm1wRz/nybhFHrBtpzIn9RNau/tzkS/N8P5Wd/3ekrxDxhsvNms2z0uSP549pOUbOl61lDeTfN6aIuMsDpxlzJqoa3v6WJ2MaXjfGtMcg63zvNjL6GyPD8YqI3wszv/8o2WWOHS82zxjacPpiEeccMh8r136WQ1xR/65AxNG61/D8YuP28DRZV11SUGgepMSc54aIRskifqLbfyvkfokq0+ZCuT8jLkx+yEf9cEsNehXDdzaJiIiIyDW82CQiIiIi15T7YnPHjh248sorUa9ePcTExOCUU07B8uXLS293HAf3338/GjVqhJiYGAwcOBDr16//gyMSEdUsXEeJqCYpV83mgQMH0K9fP5x11ln44osv0KBBA6xfvx5169YtzXniiScwadIkTJ06FS1btsR9992HwYMHY+3atahVy+xr+LtyAFVGGHxrLGNbVLzIkvNo8E8FADL1gCq364jpxpwVaCDimY7Zl+6axrLew+xsCYSrusj60PWh7cxJteV9PTzufSPlrYd+EvHWk2Tt4pMbU405A3fepUZeNHLq4yzzfI7TpZ5jjH1XR9XFpJk5HTfIPpqJS2WPzx2H5xlzou6W37ncCeFGToF3t4hrz1om4jrLzZq4HTvTRNxkzRVGzp92yR6fX0H272yODsacA5D1onegpZHjj7txc7nnFOIdEb/9nCVpvoo/ULGlfCl7uarRHGTm4PgyKPNbX2EqdB0F8OwLkxEXd/R9hVoe8/Yl+bJO8vENZt135tZ6Im60QL5PUXepsXqhaNOWcp0nAOwZJJ+Lf6ozu9zHIKpq6qgazVoe8zWkqivXxebjjz+OZs2aYcqUKaVjLVseWxwcx8HEiRNx7733YtiwYQCAN998E0lJSfjoo49w2WWXBem0iYiqJq6jRFTTlOvP6J988gl69uyJiy++GA0bNkS3bt3wyivHdpdu3rwZmZmZGDhwYOmY1+tFnz59sGiR7S1CID8/Hz6fT3wREVVXbqyjANdSIgpd5brY3LRpEyZPnow2bdpgzpw5uOmmm3Drrbdi6tSpAIDMzKN/SklKki1lkpKSSm/TJkyYAK/XW/rVrFmzQP4fRERVghvrKMC1lIhCV7kuNktKStC9e3c8+uij6NatG2644QZcf/31ePFFs6bOX+PHj0dOTk7pV0ZGRsDHIiIKdW6sowDXUiIKXeWq2WzUqBE6duwoxjp06ID//vdo493k5KOF5llZWWjU6NgGk6ysLHTt2tV6zOjoaERHR1tuGQTgt80Plo7tuu9zvuUQLax3eUwny/12UgeaZqYERG9YMPt+m1SD+XcO7zdSlj+tNqxs3W3kvNFczmuWb/6/799/pRxo112EWZeZzbeTt9whB2bsM3KM75OKxxq7P4Cx6mnZcFB7I+faLzuJ+DG18WRfP3MjT35hoojzdnYzchrlyw1NyYflnLU4bMxZ1Elu5BmUZv5Yhc2Xv9ftXLVZxA7kBgwAiHRk8/K6+XFGTgMMEHE7y3G0VjhHxGcYn2Tgnv9T8fXmUxoYrmL9FLGd7l9VbPb0h/isgGLYNwhWADfWUeD319IWkbGIj/z99xWGR8iNc8O7WBqpd1Hxn2R42eYB0A6cpnYjOWXvytrbvRJ3bhFVkmiP/PlcUyA3fvYt357AkFSudzb79euH9PR0MbZu3To0b94cwNEi9+TkZMybd+yF3ufzYcmSJUhNNXcdExHVNFxHiaimKdc7m7fffjtOPfVUPProo7jkkkuwdOlSvPzyy3j55ZcBAB6PB2PHjsW//vUvtGnTprRlR+PGjTF8+HA3zp+IqErhOkpENU25LjZ79eqFmTNnYvz48Xj44YfRsmVLTJw4EVdccaz/35133olDhw7hhhtuQHZ2Nk477TTMnj273L3hiIiqI66jRFTTeBzHj0KaCuTz+eD1emVT9w2WxNdVfKklR9cZaYWWMdVIHUvLOIZF1KlDjLEHn/6XiO8Z3MOcmGMOCZGWMV2/ttmS4493ZVi7zQgRH47cZM6ZuFLGU8wUnKriAyr+2Y9z88NZbf4s4q+uXG4mTVwnY1tnmGL9IMsnScKFfYwpg9e2EnHt5uanEbRbLItVc33ySV0cfbIxZ+hJsrl2j87XGjn4SFbCRB8xU8qyCa8aY62M6spgkQWYnm7Xmylds2Wsn1f9LIfV/eW3WnKyjvt3PoAXgZycHMTHu/3pEZXjt7X0wLpWiI9z95OJW8240Rhrc9viMud5VI1p6tJcET/QYO2JnRhRFbC7WH5Ayvd5shPFcPWBJKHCl1uCum03+bWO8rPRiYiIiMg1vNgkIiIiItfwYpOIiIiIXMOLTSIiIiJyTehuEPohAoj7tSlwimUnzyEV2zZ7pKhYN37XtwPAGBXf/3tn+vtKPjMfUs9MNfCaOe9t3CTiv+DEPlHkhOj9IV9YcnSjetuGK9UfekhETxFfUiw3IgHArk6y0Hj+0rlGzv/wseXOKsAZljHdy/4nS46mN6J1N1P61Je7Xg7MiDJy1q2WT6xzul4g4i9XPme587dENNGyS2uvalRxCj4zcprjBxH3RfmXEg88ZScF4knL2PF7TQoAvMMNQoE6XFIg4qFX32TkRMxLM8a0woFyo+QXU+WaF+2x7Yokqt7mHQkX8Zm1zBfXcE/lv1fIDUJEREREFBJ4sUlERERErilXU/eKUPpX/YPH/UnO9idy/Wf0XEuOnqf/jF5imZNnGSsn32HzhD0FlkTlCPxIqij6VGyPlc6x/Rld/WW10CkW8RHHfMDzVK/LIuuBK0mRZazYMlbe4+jnJoCiPPkAFxfb/kwtvzFFxXKOz/rDI5tx5lnuPF/9pw5b/pNmJYvtviqJ7ef4+Ifm16dUiFURBdVv/zffQdsP74k5XKKed0WWB9wp++dWz/PlyuNGe4J/7kSh7pDql+wrNH8Owl2qQCqP39YWf9bRkKvZ3L59O5o1a1bZp0FENUBGRgaaNtWfjFA9cC0loorgzzoachebJSUl2LlzJ+Li4pCbm4tmzZohIyOj2hbxVyafz8fH10V8fN11Io+v4zjIzc1F48aNERZWPauJfltLHcdBSkoKn4cu4c+5u/j4uqui1tGQ+zN6WFhY6RWyx3P0feL4+Hg+yVzEx9ddfHzdFejj6/V6XTib0PHbWurzHS1v4PPQXXx83cXH111ur6PV81d6IiIiIgoJvNgkIiIiIteE9MVmdHQ0HnjgAURHR1f2qVRLfHzdxcfXXXx8/cPHyV18fN3Fx9ddFfX4htwGISIiIiKqPkL6nU0iIiIiqtp4sUlEREREruHFJhERERG5hhebREREROQaXmwSERERkWtC9mLz+eefR4sWLVCrVi306dMHS5curexTqpImTJiAXr16IS4uDg0bNsTw4cORnp4ucvLy8jB69GjUq1cPsbGxGDlyJLKysirpjKu2xx57DB6PB2PHji0d4+N7Ynbs2IErr7wS9erVQ0xMDE455RQsX7689HbHcXD//fejUaNGiImJwcCBA7F+/fpKPOPQwXU0OLiOViyuo8FX2etoSF5svvfeexg3bhweeOABrFixAl26dMHgwYOxe/fuyj61KmfhwoUYPXo0Fi9ejLlz56KwsBCDBg3CoUOHSnNuv/12fPrpp5gxYwYWLlyInTt3YsSIEZV41lXTsmXL8NJLL6Fz585inI9v4A4cOIB+/fohMjISX3zxBdauXYunnnoKdevWLc154oknMGnSJLz44otYsmQJ6tSpg8GDByMvL68Sz7zycR0NHq6jFYfraPCFxDrqhKDevXs7o0ePLo2Li4udxo0bOxMmTKjEs6oedu/e7QBwFi5c6DiO42RnZzuRkZHOjBkzSnN+/vlnB4CzaNGiyjrNKic3N9dp06aNM3fuXKd///7Obbfd5jgOH98TdddddzmnnXba795eUlLiJCcnO08++WTpWHZ2thMdHe28++67FXGKIYvrqHu4jrqD66g7QmEdDbl3NgsKCpCWloaBAweWjoWFhWHgwIFYtGhRJZ5Z9ZCTkwMASExMBACkpaWhsLBQPN7t27dHSkoKH+9yGD16NM4//3zxOAJ8fE/UJ598gp49e+Liiy9Gw4YN0a1bN7zyyiult2/evBmZmZni8fV6vejTp0+Nfny5jrqL66g7uI66IxTW0ZC72Ny7dy+Ki4uRlJQkxpOSkpCZmVlJZ1U9lJSUYOzYsejXrx86deoEAMjMzERUVBQSEhJELh9v/02fPh0rVqzAhAkTjNv4+J6YTZs2YfLkyWjTpg3mzJmDm266CbfeeiumTp0KAKWPIdcLieuoe7iOuoPrqHtCYR2NCMpRqEoYPXo01qxZg2+//bayT6XayMjIwG233Ya5c+eiVq1alX061U5JSQl69uyJRx99FADQrVs3rFmzBi+++CKuvvrqSj47qom4jgYf11F3hcI6GnLvbNavXx/h4eHGLrOsrCwkJydX0llVfWPGjMFnn32Gr776Ck2bNi0dT05ORkFBAbKzs0U+H2//pKWlYffu3ejevTsiIiIQERGBhQsXYtKkSYiIiEBSUhIf3xPQqFEjdOzYUYx16NAB27ZtA4DSx5DrhcR11B1cR93BddRdobCOhtzFZlRUFHr06IF58+aVjpWUlGDevHlITU2txDOrmhzHwZgxYzBz5kzMnz8fLVu2FLf36NEDkZGR4vFOT0/Htm3b+Hj74eyzz8bq1auxcuXK0q+ePXviiiuuKP03H9/A9evXz2gxs27dOjRv3hwA0LJlSyQnJ4vH1+fzYcmSJTX68eU6GlxcR93FddRdIbGOBmWbUZBNnz7diY6Odt544w1n7dq1zg033OAkJCQ4mZmZlX1qVc5NN93keL1eZ8GCBc6uXbtKvw4fPlyac+ONNzopKSnO/PnzneXLlzupqalOampqJZ511Xb8LkrH4eN7IpYuXepEREQ4jzzyiLN+/XrnnXfecWrXru28/fbbpTmPPfaYk5CQ4Hz88cfOqlWrnGHDhjktW7Z0jhw5UolnXvm4jgYP19GKx3U0eEJhHQ3Ji03HcZxnn33WSUlJcaKiopzevXs7ixcvruxTqpIAWL+mTJlSmnPkyBHn5ptvdurWrevUrl3bufDCC51du3ZV3klXcXqR5ON7Yj799FOnU6dOTnR0tNO+fXvn5ZdfFreXlJQ49913n5OUlORER0c7Z599tpOenl5JZxtauI4GB9fRisd1NLgqex31OI7jBOc9UiIiIiIiKeRqNomIiIio+uDFJhERERG5hhebREREROQaXmwSERERkWt4sUlEREREruHFJhERERG5hhebREREROQaXmwSERERkWt4sUlEREREruHFJhERERG5hhebREREROSa/weHdEtK0/mOhwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a, b = next(iter(train_dl))\n",
    "for i in range(4):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(8, 8))\n",
    "    axes[0].imshow(a[i][3:6].permute(1, 2, 0).clip(0,1).cpu().numpy())\n",
    "    axes[1].imshow(b[i].cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Network(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            ConvolutionBlock(7, 16, 3),\n",
    "            ConvolutionBlock(16, 16, 3, residual=True, bias=False),\n",
    "            ConvolutionBlock(16, 16, 3, residual=True, bias=False),\n",
    "            nn.AvgPool2d(2),\n",
    "        )\n",
    "        \n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Dropout(0.4),\n",
    "            ConvolutionBlock(16, 32, 3),\n",
    "            ConvolutionBlock(32, 32, 3, residual=True, bias=False),\n",
    "            ConvolutionBlock(32, 32, 3, residual=True, bias=False),\n",
    "            nn.AvgPool2d(2),\n",
    "        )\n",
    "\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Dropout(0.4),\n",
    "            ConvolutionBlock(32, 64, 3),\n",
    "            ConvolutionBlock(64, 64, 3, residual=True, bias=False),\n",
    "            ConvolutionBlock(64, 64, 3, residual=True, bias=False),\n",
    "            \n",
    "            ConvolutionBlock(64, 1, 1),\n",
    "        )\n",
    "\n",
    "        self.bias = nn.Parameter(torch.zeros(1))\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "\n",
    "        x = x.mean(dim=(2,3)) + self.bias\n",
    "\n",
    "        return x\n",
    "\n",
    "model = Network().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/7001 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.5900:   0%|          | 5/7001 [00:00<13:54,  8.38it/s]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.6922\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.5655:   1%|          | 57/7001 [00:02<04:12, 27.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.5769\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.5406:   1%|▏         | 105/7001 [00:03<04:06, 28.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.5678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.4975:   2%|▏         | 157/7001 [00:05<04:14, 26.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.5621\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.4972:   3%|▎         | 205/7001 [00:07<04:03, 27.87it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.5474\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.5204:   4%|▎         | 257/7001 [00:08<03:59, 28.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.5545\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.4794:   4%|▍         | 305/7001 [00:10<04:03, 27.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.5269\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.4641:   5%|▌         | 357/7001 [00:11<04:00, 27.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.5350\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.4453:   6%|▌         | 405/7001 [00:13<03:55, 28.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4853\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.4740:   7%|▋         | 457/7001 [00:15<03:56, 27.69it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4889\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.4450:   7%|▋         | 505/7001 [00:16<03:57, 27.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.5076\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3959:   8%|▊         | 557/7001 [00:18<03:52, 27.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4932\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3974:   9%|▊         | 605/7001 [00:19<03:46, 28.27it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4397\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3672:   9%|▉         | 657/7001 [00:21<03:49, 27.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4244\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.4101:  10%|█         | 705/7001 [00:23<03:48, 27.60it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4418\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3674:  11%|█         | 757/7001 [00:24<03:39, 28.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4330\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3600:  11%|█▏        | 805/7001 [00:26<03:39, 28.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4207\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3813:  12%|█▏        | 857/7001 [00:27<03:39, 28.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3380:  13%|█▎        | 905/7001 [00:29<03:39, 27.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4009\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3166:  14%|█▎        | 957/7001 [00:31<03:37, 27.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4028\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2995:  14%|█▍        | 1005/7001 [00:32<03:34, 27.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3784\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3221:  15%|█▌        | 1057/7001 [00:34<03:30, 28.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3226:  16%|█▌        | 1105/7001 [00:35<03:30, 27.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.4154\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2868:  17%|█▋        | 1157/7001 [00:37<03:27, 28.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3788\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3971:  17%|█▋        | 1205/7001 [00:38<03:28, 27.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3851\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3170:  18%|█▊        | 1257/7001 [00:40<03:26, 27.88it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3293:  19%|█▊        | 1305/7001 [00:42<03:23, 27.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3655\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2586:  19%|█▉        | 1357/7001 [00:43<03:22, 27.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2507:  20%|██        | 1405/7001 [00:45<03:16, 28.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3474\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3445:  21%|██        | 1457/7001 [00:46<03:22, 27.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3986\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2275:  21%|██▏       | 1505/7001 [00:48<03:20, 27.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3318\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2932:  22%|██▏       | 1557/7001 [00:50<03:14, 27.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3830\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2457:  23%|██▎       | 1605/7001 [00:51<03:11, 28.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3430\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2689:  24%|██▎       | 1657/7001 [00:53<03:11, 27.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3303\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3098:  24%|██▍       | 1705/7001 [00:54<03:11, 27.63it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3117\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2172:  25%|██▌       | 1757/7001 [00:56<03:08, 27.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3206\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2737:  26%|██▌       | 1805/7001 [00:57<03:07, 27.72it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3282\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2317:  27%|██▋       | 1857/7001 [00:59<03:04, 27.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3117\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2268:  27%|██▋       | 1905/7001 [01:01<03:01, 28.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3543\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2812:  28%|██▊       | 1957/7001 [01:02<02:57, 28.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3062\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3261:  29%|██▊       | 2005/7001 [01:04<02:57, 28.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2980\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2018:  29%|██▉       | 2057/7001 [01:05<02:57, 27.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2656\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2491:  30%|███       | 2105/7001 [01:07<02:57, 27.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3026\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2423:  31%|███       | 2157/7001 [01:09<02:55, 27.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3113\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2002:  31%|███▏      | 2205/7001 [01:10<02:50, 28.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2886\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2146:  32%|███▏      | 2257/7001 [01:12<02:53, 27.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2843\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2508:  33%|███▎      | 2305/7001 [01:13<02:49, 27.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2670\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2243:  34%|███▎      | 2357/7001 [01:15<02:47, 27.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2793\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2542:  34%|███▍      | 2405/7001 [01:17<02:41, 28.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2856\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2122:  35%|███▌      | 2457/7001 [01:18<02:42, 27.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2864\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2115:  36%|███▌      | 2505/7001 [01:20<02:43, 27.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2892\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1569:  37%|███▋      | 2557/7001 [01:21<02:36, 28.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2546\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1910:  37%|███▋      | 2605/7001 [01:23<02:36, 28.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2617\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2311:  38%|███▊      | 2657/7001 [01:24<02:35, 27.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2628\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.3808:  39%|███▊      | 2705/7001 [01:26<02:33, 27.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2612\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1906:  39%|███▉      | 2757/7001 [01:28<02:32, 27.88it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2872\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1938:  40%|████      | 2805/7001 [01:29<02:30, 27.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2687\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2236:  41%|████      | 2857/7001 [01:31<02:30, 27.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2604:  41%|████▏     | 2905/7001 [01:32<02:26, 27.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.3051\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2339:  42%|████▏     | 2957/7001 [01:34<02:24, 27.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2693\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1812:  43%|████▎     | 3005/7001 [01:36<02:24, 27.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2817\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1983:  44%|████▎     | 3057/7001 [01:37<02:19, 28.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2757\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2009:  44%|████▍     | 3105/7001 [01:39<02:19, 27.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2017:  45%|████▌     | 3157/7001 [01:40<02:17, 28.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1879:  46%|████▌     | 3205/7001 [01:42<02:14, 28.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2302\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1855:  47%|████▋     | 3257/7001 [01:44<02:14, 27.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2353\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1531:  47%|████▋     | 3305/7001 [01:45<02:13, 27.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2610\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1480:  48%|████▊     | 3357/7001 [01:47<02:10, 28.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2325\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2634:  49%|████▊     | 3405/7001 [01:48<02:07, 28.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1726:  49%|████▉     | 3457/7001 [01:50<02:06, 28.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2228\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0933:  50%|█████     | 3505/7001 [01:51<02:06, 27.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2419\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1724:  51%|█████     | 3557/7001 [01:53<02:03, 27.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1771:  51%|█████▏    | 3605/7001 [01:55<02:01, 27.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2471\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2216:  52%|█████▏    | 3657/7001 [01:56<01:58, 28.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1373:  53%|█████▎    | 3705/7001 [01:58<01:56, 28.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2736\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1216:  54%|█████▎    | 3757/7001 [01:59<01:55, 28.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1327:  54%|█████▍    | 3805/7001 [02:01<01:52, 28.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2412\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1341:  55%|█████▌    | 3857/7001 [02:02<01:52, 27.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2096\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1392:  56%|█████▌    | 3905/7001 [02:04<01:51, 27.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2464\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1243:  57%|█████▋    | 3957/7001 [02:06<01:49, 27.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2301\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0908:  57%|█████▋    | 4005/7001 [02:07<01:46, 28.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2372\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2326:  58%|█████▊    | 4057/7001 [02:09<01:44, 28.15it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2138\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2533:  59%|█████▊    | 4105/7001 [02:10<01:43, 27.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1115:  59%|█████▉    | 4157/7001 [02:12<01:41, 28.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2549\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1940:  60%|██████    | 4205/7001 [02:14<01:41, 27.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2555\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1039:  61%|██████    | 4257/7001 [02:15<01:38, 27.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2367\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1230:  61%|██████▏   | 4305/7001 [02:17<01:38, 27.47it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2304\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0669:  62%|██████▏   | 4357/7001 [02:18<01:35, 27.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2206\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1818:  63%|██████▎   | 4405/7001 [02:20<01:32, 27.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2535\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1457:  64%|██████▎   | 4456/7001 [02:21<01:36, 26.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2145\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2550:  64%|██████▍   | 4504/7001 [02:23<01:37, 25.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2336\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1986:  65%|██████▌   | 4556/7001 [02:25<01:25, 28.52it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2563\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2225:  66%|██████▌   | 4604/7001 [02:26<01:31, 26.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2330\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1120:  67%|██████▋   | 4656/7001 [02:28<01:22, 28.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2303\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1700:  67%|██████▋   | 4704/7001 [02:29<01:27, 26.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2612\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1392:  68%|██████▊   | 4756/7001 [02:31<01:19, 28.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2701\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0612:  69%|██████▊   | 4804/7001 [02:32<01:24, 26.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2166\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0919:  69%|██████▉   | 4856/7001 [02:34<01:15, 28.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1814\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1130:  70%|███████   | 4904/7001 [02:36<01:20, 26.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2310\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0758:  71%|███████   | 4956/7001 [02:37<01:13, 27.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2540\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0823:  71%|███████▏  | 5004/7001 [02:39<01:18, 25.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2316\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1629:  72%|███████▏  | 5056/7001 [02:40<01:10, 27.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2242\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1566:  73%|███████▎  | 5104/7001 [02:42<01:12, 26.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1775\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0657:  74%|███████▎  | 5156/7001 [02:44<01:05, 27.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2089\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1861:  74%|███████▍  | 5208/7001 [02:45<01:03, 28.15it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2492\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0642:  75%|███████▌  | 5256/7001 [02:47<01:02, 28.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1902\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1266:  76%|███████▌  | 5304/7001 [02:48<01:05, 25.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0789:  77%|███████▋  | 5356/7001 [02:50<00:58, 28.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2076\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0749:  77%|███████▋  | 5404/7001 [02:51<01:01, 26.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2496\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1124:  78%|███████▊  | 5456/7001 [02:53<00:55, 27.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1863\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2827:  79%|███████▊  | 5504/7001 [02:55<00:57, 26.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2068\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0984:  79%|███████▉  | 5556/7001 [02:56<00:51, 27.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2091\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1144:  80%|████████  | 5604/7001 [02:58<00:53, 26.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2131\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0550:  81%|████████  | 5656/7001 [02:59<00:48, 27.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2049\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1066:  81%|████████▏ | 5704/7001 [03:01<00:50, 25.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1348:  82%|████████▏ | 5756/7001 [03:03<00:45, 27.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1965\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1631:  83%|████████▎ | 5804/7001 [03:04<00:46, 25.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2353\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0705:  84%|████████▎ | 5856/7001 [03:06<00:41, 27.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1809\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1756:  84%|████████▍ | 5904/7001 [03:07<00:42, 25.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1697:  85%|████████▌ | 5956/7001 [03:09<00:36, 28.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2428\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0481:  86%|████████▌ | 6004/7001 [03:11<00:38, 25.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1948\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2330:  87%|████████▋ | 6056/7001 [03:12<00:33, 28.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1196:  87%|████████▋ | 6104/7001 [03:14<00:34, 26.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2253\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1060:  88%|████████▊ | 6156/7001 [03:15<00:30, 27.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1838\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2856:  89%|████████▊ | 6204/7001 [03:17<00:30, 25.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2146\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1242:  89%|████████▉ | 6256/7001 [03:18<00:26, 27.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1949\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1781:  90%|█████████ | 6304/7001 [03:20<00:26, 26.27it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1883\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0769:  91%|█████████ | 6356/7001 [03:22<00:23, 27.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1986\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1400:  91%|█████████▏| 6404/7001 [03:23<00:22, 26.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2093\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1557:  92%|█████████▏| 6456/7001 [03:25<00:19, 28.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2187\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0432:  93%|█████████▎| 6504/7001 [03:26<00:18, 26.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2609\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1161:  94%|█████████▎| 6556/7001 [03:28<00:16, 27.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2058\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.2169:  94%|█████████▍| 6604/7001 [03:29<00:15, 25.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2145\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0540:  95%|█████████▌| 6656/7001 [03:31<00:12, 27.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2310\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1082:  96%|█████████▌| 6704/7001 [03:33<00:11, 25.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1965\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1392:  97%|█████████▋| 6756/7001 [03:34<00:08, 27.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0575:  97%|█████████▋| 6804/7001 [03:36<00:07, 25.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.1732\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0862:  98%|█████████▊| 6856/7001 [03:37<00:05, 27.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2275\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.1076:  99%|█████████▊| 6904/7001 [03:39<00:03, 25.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2290\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0925:  99%|█████████▉| 6956/7001 [03:41<00:01, 28.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2289\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.0993: 100%|██████████| 7001/7001 [03:42<00:00, 31.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Loss: 0.2386\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "#loss_fn = nn.BCELoss()\n",
    "loss_fn = nn.BCEWithLogitsLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
    "\n",
    "pbar = tqdm(range(7001))\n",
    "def closure():\n",
    "    global pbar\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    im, lb = next(iter(train_dl))\n",
    "    lb = lb.mean(dim=(1, 2))\n",
    "    lb = (lb >= 0.4).float()[..., None]\n",
    "\n",
    "    out = model(im)\n",
    "    loss = loss_fn(out, lb)\n",
    "    \n",
    "    pbar.set_description(f\"Loss: {loss:.4f}\")\n",
    "\n",
    "    loss.backward()\n",
    "    return loss\n",
    "\n",
    "model.train()\n",
    "for i in pbar:\n",
    "    optimizer.step(closure)\n",
    "\n",
    "    if i % 50 == 0:\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            im, lb = next(iter(val_dl))\n",
    "            lb = lb.mean(dim=(1, 2))\n",
    "            lb = (lb >= 0.4).float()[..., None]\n",
    "            \n",
    "            out = model(im)\n",
    "            ls = loss_fn(out, lb).item()\n",
    "            \n",
    "            print(f\"Val Loss: {ls:.4f}\")\n",
    "        model.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-3.3368053 , -1.394316  ,  3.294561  , -2.680512  , -3.4611201 ,\n",
       "         4.6263556 ,  1.5369735 ,  2.3557591 , -3.0142422 ,  2.51012   ,\n",
       "        -3.4611201 ,  3.901919  , -2.87363   ,  1.1349988 , -3.4611201 ,\n",
       "        -3.4611201 , -3.2639425 ,  4.114445  ,  2.7148237 , -3.4611201 ,\n",
       "         3.337932  ,  0.58255434,  2.990603  ,  5.14066   ,  5.068938  ,\n",
       "        -2.1729546 ,  3.4713526 ,  3.8619266 ,  3.960752  , -3.4611201 ,\n",
       "         4.6458507 ,  4.743709  , -3.3724656 , -2.9973838 , -3.4058633 ,\n",
       "        -3.0219936 ,  0.322186  , -3.4390562 ,  0.60693216, -3.4611201 ,\n",
       "        -3.4219594 , -1.0285025 ,  4.9004216 , -3.4611201 ,  0.14958191,\n",
       "         3.865603  , -3.214913  ,  1.3388014 ,  1.1648717 , -2.6964095 ,\n",
       "         4.311459  ,  2.5348434 , -3.4611201 , -3.4311934 ,  1.2943063 ,\n",
       "        -3.4611201 , -3.4611201 ,  4.991347  , -2.4253185 , -3.4606557 ,\n",
       "        -3.4611201 , -3.4611201 , -3.4089072 , -3.4611201 ,  3.1397128 ,\n",
       "        -2.8722992 , -3.3970735 , -2.0544834 ,  1.2348676 , -3.4542255 ,\n",
       "        -3.4611201 , -3.4611201 , -3.4082477 , -3.4611201 ,  0.761538  ,\n",
       "        -3.4611201 , -2.7637334 , -3.4183452 ,  3.3729672 , -3.4611201 ,\n",
       "        -3.4611201 , -2.72488   , -0.23360825, -3.4611201 , -3.3733015 ,\n",
       "         1.5933471 , -1.3263614 , -3.4611201 ,  4.97535   ,  2.9533038 ,\n",
       "        -3.4611201 , -3.2738109 , -3.4611201 ,  4.64016   , -3.4611201 ,\n",
       "         1.9583888 , -0.9280393 , -3.4611201 , -3.4611201 ,  2.0719552 ,\n",
       "         4.5747075 , -3.429779  , -3.4611201 ,  4.8320336 ,  1.4846382 ,\n",
       "        -3.0698586 , -3.4364295 , -2.5989454 ,  0.95573854,  5.0319543 ,\n",
       "        -3.375826  , -3.3649218 ,  4.750733  ,  3.7577658 , -1.6117836 ,\n",
       "         4.9251313 ,  1.6808362 , -3.4611201 , -3.4611201 , -2.8862705 ,\n",
       "        -2.9191742 ,  4.012667  ,  4.3205447 ,  4.404371  ,  3.4306073 ,\n",
       "        -3.4611201 ,  3.2300224 ,  0.7950463 , -1.798206  , -3.4611201 ,\n",
       "        -2.745016  , -3.4611201 , -0.6023996 ,  1.6078262 ,  0.7312379 ,\n",
       "         3.9565825 ,  3.5089111 , -3.3440933 , -3.4425716 ,  1.0533834 ,\n",
       "        -3.4611201 ,  4.6119504 ,  4.91454   ,  4.9941163 , -1.0380039 ,\n",
       "        -0.7246232 , -3.4611201 ,  1.2580357 , -3.1951098 ,  3.6794534 ,\n",
       "         2.2812743 , -3.4611201 , -3.4611201 ], dtype=float32),\n",
       " tensor([False, False,  True, False, False,  True,  True,  True, False,  True,\n",
       "         False,  True, False, False, False, False, False,  True,  True, False,\n",
       "          True,  True,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "          True,  True, False, False, False, False, False, False,  True, False,\n",
       "         False, False,  True, False,  True,  True, False, False,  True, False,\n",
       "          True,  True, False, False,  True, False, False,  True, False, False,\n",
       "         False, False, False, False,  True, False, False, False,  True, False,\n",
       "         False, False, False, False,  True, False, False, False,  True, False,\n",
       "         False, False, False, False, False,  True, False, False,  True, False,\n",
       "         False, False, False,  True, False,  True, False, False, False,  True,\n",
       "          True, False, False,  True,  True, False, False, False,  True,  True,\n",
       "         False, False,  True,  True, False,  True,  True, False, False, False,\n",
       "         False,  True,  True,  True,  True, False, False,  True, False, False,\n",
       "         False, False, False,  True, False,  True,  True, False, False,  True,\n",
       "         False, False,  True,  True, False, False, False,  True, False,  True,\n",
       "          True, False, False], device='cuda:0'))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "im, lb = next(iter(val_dl))\n",
    "a = model(im).cpu().detach().numpy().flatten()\n",
    "a, lb.mean(dim=(1, 2)) > 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()\n",
    "\n",
    "im, lb = next(iter(train_dl))\n",
    "im = im[0]; lb = lb[0]\n",
    "\n",
    "c1 = model.get_submodule(\"conv1\")\n",
    "c2 = model.get_submodule(\"conv2\")\n",
    "c3 = model.get_submodule(\"conv3\")\n",
    "\n",
    "features_blobs = c3(c2(c1(im[None, ...])))\n",
    "\n",
    "im = (im[3:6] * IS[3:6, None, None]) + IM[3:6, None, None]\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(8, 8))\n",
    "axes[0].imshow(im.permute(1, 2, 0).clip(0,1).cpu().numpy())\n",
    "axes[1].imshow(lb.cpu().detach().numpy())\n",
    "axes[2].imshow(features_blobs[0][0].cpu().detach().numpy());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im, lb = next(iter(train_dl))\n",
    "im = (im[:, 3:6] * IS[None, 3:6, None, None]) + IM[None, 3:6, None, None]\n",
    "\n",
    "fig, axes = plt.subplots(1, 5, figsize=(16, 8))\n",
    "for i in range(5):\n",
    "    axes[i].imshow(im[i].permute(1, 2, 0).clip(0,1).cpu().numpy())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}