soap_side.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "from torchvision.transforms import v2 as transforms\n",
    "\n",
    "from aivolved.dataset import get_img_dataset_normalisation, CocoBinaryClassificationDataset, SiameseDataset"
   ],
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-08-02T04:50:35.991347Z",
     "start_time": "2024-08-02T04:50:33.226467Z"
    }
   },
   "id": "initial_id"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "DATA_PATH = \"data/soap_side/train/\"\n",
    "\n",
    "train_dataset = CocoBinaryClassificationDataset(DATA_PATH, {2, 3},\n",
    "                                                transform=torchvision.transforms.Compose(\n",
    "                                                    [torchvision.transforms.Resize((224, 224))]))\n",
    "mean, stdev = get_img_dataset_normalisation(train_dataset)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-02T04:50:42.961236Z",
     "start_time": "2024-08-02T04:50:35.993248Z"
    }
   },
   "id": "da058665fc33f321"
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=0.01s)\n",
      "creating index...\n",
      "index created!\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n"
     ]
    }
   ],
   "source": [
    "train_transforms = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.CenterCrop((100, 100)),\n",
    "    transforms.ToImage(),\n",
    "    transforms.ToDtype(torch.float32, scale=True),\n",
    "    torchvision.transforms.Normalize(mean, stdev)\n",
    "])\n",
    "\n",
    "train_dataset = CocoBinaryClassificationDataset(DATA_PATH, {2, 3}, transform=train_transforms)\n",
    "valid_dataset = CocoBinaryClassificationDataset(\"data/soap_side/valid/\", {2, 3}, transform=train_transforms)\n",
    "test_dataset = CocoBinaryClassificationDataset(\"data/soap_side/test/\", {2, 3}, transform=train_transforms)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-03T04:12:01.880400Z",
     "start_time": "2024-08-03T04:12:01.866282Z"
    }
   },
   "id": "81070ec4cfedeb5d"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "{0: {'id': 0, 'name': 'SIDE1', 'supercategory': 'none'},\n 1: {'id': 1, 'name': 'bottom', 'supercategory': 'SIDE1'},\n 2: {'id': 2, 'name': 'dent', 'supercategory': 'SIDE1'},\n 3: {'id': 3, 'name': 'fuddher', 'supercategory': 'SIDE1'},\n 4: {'id': 4, 'name': 'machine_part', 'supercategory': 'SIDE1'},\n 5: {'id': 5, 'name': 'side', 'supercategory': 'SIDE1'}}"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.coco_dataset.coco.cats"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-02T04:50:42.980956Z",
     "start_time": "2024-08-02T04:50:42.973731Z"
    }
   },
   "id": "a7748dbc2589de6f"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGgCAYAAADsNrNZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABzW0lEQVR4nO29e5BdZ3nm++777nurW1a3ZEu27JiR8WUwviGb3LAmPoxJ8OBDwilnxoHUMAlywHYVBGdiT40TI5JUJR5yHBgoxiEVCIPPDJeBM+ZQAjxjMDYWMWAMsnyJJUtq3fvevW9rnT/a3ut9n6/Xt/bqbrO27OdXpar96Vt7rW/d9ur1Pu/3vLkwDEMhhBBCfs7ksx4AIYSQ1yZ8ABFCCMkEPoAIIYRkAh9AhBBCMoEPIEIIIZnABxAhhJBM4AOIEEJIJvABRAghJBP4ACKEEJIJfAARQgjJhFfsAXTffffJOeecI9VqVa666ip57LHHXqlNEUIIOQ3JvRJecP/1v/5X+Tf/5t/IJz7xCbnqqqvk3nvvlQceeED27t0rGzZs8H43CAI5dOiQDAwMSC6XW+uhEUIIeYUJw1BmZmZk06ZNks973nPCV4Arr7wy3LlzZ7vdarXCTZs2hbt27Ur87oEDB0IR4T/+4z/+47/T/N+BAwe8v/dFWWPq9brs2bNH7rjjjvb/5fN52bFjhzzyyCPO8rVaTWq1WrsdvvRC9n++6zYplSsiIlIsF6L+ILDbW2yadr4QPW17+qqmr1i2u3vwmRdN+8CBn7Y/Ly7Omz58GwtX+OKI68lJDhcwzXw+2veL//l201fuLZv2g1/5+/bnmZmTpi8M7XETNf4A+nDfgqAlcaz0OCyHPjZ6v7FvubZvHNinj4Xb59uftdtXwfPuW9JzTSSB513vn7uv8fuXy9m/YvG7hUJ0b/X3D5u+np4B0y6V7HXbaNTbn2s1e9/h+O2xwGsi/i9t5/pPAa7XPRbRuluthukLWvj7FB2n4WEbDbr8yl8z7fd88P8y7Wop+u5D/+sHpu8H37Dtgwefbn+enZ00fYuLs6Zdr0e/v011LkREcvD2UiqVTLu/f6T9eXT0zGg9zYY8/vj/KwMD9twja/4AOn78uLRaLRkbGzP/PzY2Jj/72c+c5Xft2iX/8T/+R+f/S+WKlNsPoGiY+ACSwJ5w/QAqV/wPoFKpYtr6JtKfRbrjAfTyA/llytDWr7pu+DL+B88ZA/b/nEKhejtJD5zVjWll33VPeZprYOXjX92+d76s75JO2qY9d/ZHC0Mw+ADV/bgs3u7+a8T3AIrtSiT5ARR9DgL/srqNxwF/j/r6+027Wo5+/Ks9vd7vFgol9dn+lqU5/vgAwu/qdReL9uEkknzdrPkDKC133HGH3H777e329PS0bN68WcIwbP/Imx97/IEu2ANUm68t+3k5ZmdPmXaz2YxZ0n3g+A6sb1nfTSLi/gU1MLCu/Rn35/lnfmrauD92TPF/CfvecJbD9yMQBPHbSXqI6373L9bO82WSzlU+X4xd1v8m5X9TTHONpHsA4Y9Y/PFH8Ac8DKNzvZq3SvxRK6gfJvwR0z+GIiLFgvtD9TIN+As8COoxSybfSz7S/QHpf3tqqbccvJfwB7ynJ3qojG042/Rd9IsXmvbUwoJp//ipZ9ufn33iWdOH976+VnFMeI/q/iCE8Qf2OLVatn9xca79+cSJg2q5+N9SzZo/gNavXy+FQkGOHDli/v/IkSMyPj7uLF+pVKRSqTj/Twgh5NXNmqdhl8tlueyyy2T37t3t/wuCQHbv3i3bt2/3fJMQQshriVckBHf77bfLzTffLJdffrlceeWVcu+998rc3Jy8+93vXtH6mvXodS4pTKBfEVsN+7pYr9tX2rm5adNOE4rS281jKMCRdVQ/CvwQ1qlW+0x748ZfaH8+fvyw6du3b0/8mJzUR3uqQ7Wv+WLnonZa0gjDNj7ujz2vhjSCtC9Uk0aDSBq/L0zrC5UljSFNEgK29Tkol3tMX6Fg96dajUJL69bZSMfw8Bmm3ds7ZNpBEN3fMzM2lISCuQ7R1euLps9JANChJU9oOC1u2Fn95kDoCZcdGIhE+/O2XWL6RjaOmvYLz0KS1M+i9tEDNsKEx6LZjI5To2FD93icbAgR7434e3Rp3dF2m81ovZ3+lr4iD6Df+q3fkmPHjsldd90lExMT8oY3vEEefPBBJzGBEELIa5dXLAnhlltukVtuueWVWj0hhJDTHHrBEUIIyYTM07DjKJbyUiwtxZlDlQqYy4MG1LCx3GIx2iWMv87N2ZROnPTmSwP2xYwDsfFOjPfr1NeklMjR0U2mXe2N5jINtEZM32WXXQfbjf6eKORB83FiudEYKz12vlTfoNWh+oZtu6om+KJWszhnY9H1xeiYBy1M/7RtMx+hgKm88LdSHlOr4/+WKuH8r0o0ETJf9M9ZMXoLbLMA2lmxZLej551hH2ZP59T+ob5SgDHq1F53DPBd2K4+B/rciFitFdc9dIbVbYb77TUx1BvNSxnssXpRH2S59lft9aaZq1m9YgZSkadVG5etwTSKVqBTkeOvtSSS0uubSneu162+gsdUXxN4nk8dsfrX9AmrUR87cKz9GSea11Q6tIjVyrQ2I+LqVL6J2Qj+Lurt6OPiaknLwzcgQgghmcAHECGEkEzgA4gQQkgmdK0GlC8UJP9SjLRQUWakOO+naWONBRUDx9h5pWL9k8IQ9Zj43HVfTBPjyW6MVc29gG30gXHjBrDn+PX3/Xr78xXnnWv6esrW1LGoYsr5BKcX7f9WgPFXUMvwGIOmmU/RhH0/NWc1OB1Lr8K+9eK+emL4jvXOmvrIrWw9aWx6uhE8hq8UZySYV2qSjmFDaUKn5u21tlCHuTFgOVNQN1AJbIcqRWgrg84S6ndwnWoN67FnrZ3Osfox0545OWPaJw5H/ahfN5pgWeSxwFoNqAG1Wsv/LlIDIoQQ0tXwAUQIISQTujYEp9GpskHT/2pnXjfh1RPtwnOCKbe6bcNFPvsW5zUUwy0qhFWu2BTVkRGbdv3GHW807TNHIjfsKtTiWNdnQ4oYKtP40k5fyfCKqfET+P/eeXpiov154rhNM8V04jOGBk17vQrdDECaL4ZFdHhvoFqBZf1lODSpjtsqnKe7gWAV40sKgRpndrQDWkX4VIfG1vXae6UBKdsnZ23Ksw6VLTZsGnOjFR+qx2sNw9nTC9E0hYMvTJg+dJo+ccjeA9PTx6MxgL1OmuvHLcfQuTu8r20/MwRHCCGki+EDiBBCSCbwAUQIISQTTgsNSONaxkPKrbK6mJm0Vu7Hj1t78ybYkvvAFG1fzBXL45aKkebQ02vTTLec9wumjVVZn/zpc+3PR6dtnBotTXS8GbUMTActePK0sUS3Lw7fSki31LYsw33x9i0iIhdv3tz+jFrAT360z7R/OmGrwWqrnt4hu51Kjz0f2g6l0gPp3mBDNDIQlRnA490DmlyaCqh4TFdqDbMa7QiPMa5LXzNFsELCa6SVosxDHa5xbaGDx2WoFzTTvv7YvjJovHmlx5QgdXpsyFoLoVaj7zW871ATWqipEhFgb9QAa57F2QXVZ49DfcF+9+RhqwEt1iK7naRyB1jmReOWOonaYYg2Yv77O073oQZECCGkq+EDiBBCSCbwAUQIISQTulYDCsOwHV/0leTWpQFe/t7LoJ35woLVhJw4qvpuswFW9WBprucUYYy7XLZj0vOL5uamTN9Pf/S4aU8csDrVGeNReePBUTv3BefG6BIGqI0lzUPxgWUI0ugV2g6pb6jf9G3YssG0t2yJ9hX1obNft9m0BaxTjh860f48d8BampSrVufBa0aDMXxdCqRUtRoDaktYQsLG1v1WPHb6GvSh/uIpyZ20nTTXiLFzSTjn+hpJuj6c7casR8TqOCL23Gl9TkRk4/CwaWudx9FLQRPqr9j+Zl+kYeShDHVP2Z53XTLiWAi/ObML0I7mAeFxOPKCLbOt5/2IuPY7GrS+6bQkdhJJJd5XC9+ACCGEZAIfQIQQQjKBDyBCCCGZ0LUaUKvRklZuKY5Zm498j8oQh+9db/P5tV7UwHz9BWtvPj9v47U6btoCzScP82oa9SiWizn39bqN++o4fLls5y5Uq1braMHcpNmpSLdqNmxct1ju3LfMp+P4YvJJ4HpRK9BjOjlhSw4ffv6waT8z+Ez78xmbzzB92F5/1nrTriht4PhBGzvHMuGNWnSM8RhiW19PuB59XYq4WpO+VlHLCDFm34qfR+PoOEV97hL0Fo9+5Jx3PJdp5jX5ymM411689uRuE3zY1Lmbg3l+h4+eMG19Pvqg7PyGQaunjvZbPUmXEceS4mUsx6DaWCb8VNPes3quD875O/ysvR9mZ61ejF5xmiaUY9AlYVAfwvXo/iSNx6c1rWR+Gt+ACCGEZAIfQIQQQjKha0NwYRi9xunXbl9IQUSk1YhePefmJk0fpkBjqEy/tmL6Yatuwy++VFj8rrbm6esbNn3r1m007cFBG1rSISHcdx+OHUoL2jqNuRhf8XSpbdeNacG+7+oxu+fKhgJmT0UhUgx3nYCw2uB6G0LpH44sjkbGR0wfpsLOT0fprPraEnHDaH3K1gfH26hBqj5Yq+g05mLZho6xWq8Oq2F4zomK6PIFsGwh4Vya8UJ4yA2rqXBd0jXiS63G6wfDtHnfvdR5eFjf+yIiC6qN18DJE/a3oASh196+KFSu7aRE3ErE+hzUmvHWO7js/JRNq56dtmPCsJpdj7+as7XFsX1YsVmH/fF6SmPFsxL4BkQIISQT+AAihBCSCXwAEUIIyYSu1YByuSj+W6pE8fMC6A+1hVpsu1az8VfUfOqg68SlFC6Nx8aiC6qMrUBMuwK2Hv1K9xkaWh/bJ+LunzcGjjqP+pykF+n1JsVxnTGksGhJEyPWi6I2M13H9PQ50670RNb1A+tsSm2ltwLLRjH8OmwHdQS975iinS+CpQ+kZbe0xuKs12pCpapet9VbghbG5eOPKS7b8ug8afSiNFrMWn7Xty6f7vTSwrFjcEpEwHWgSyVMT9l0b0w518cYtaa5aavz6O/ismi1g1qNKV0eoK4Tr/OkuwdRf+zceicUpmETQgg5TeADiBBCSCbwAUQIISQTulYDarWCdlxTW5rgPA2M7da01UXDxuRR82lAvyaft/HxQgFi9qVoHGivMzg4atpDQ5GNTH//OrteKCPsm2MTQgkCtE7xLot4/vRw4+XQb/oStqNCyLmCf46HTyrA4xKC1qHnW6AuiNdMT390vrA0QwF0Hm2Rg/OA8jCXB7WmoBmNEedXoA1LTk35wDlDOEdF02qhpY9/XpAGLXEcrcY7t6fzeT/eE5uwXmeMes6Qx/YJl02LTyPFc6n1ynnQfHBuWM9AdO255bvtdRsEMK9MgWNCCy/924bjXUtC4TwgQgghpyF8ABFCCMmErg3B5fP5dsqiDklU+224C9ONdQhisWZTdd1tYJitGNuHYbbe3sj6ZWDAWr+gnY5OtS6WbDgoKQxiXKshhIWhDZ/tDeLr94U9cLtp3JhTVWFNGj+G5DxOvBjqMI7pkH7bO2DPswmrJYwJw11lNX0A96cBoZmWCsk1Fu2YQgjBlVUaealiryfHgsnTTnK0thY5An2eKq0J17QvzJbGXR2PN4ZEDUkh6RQEQXxITrtdi7jHTY8Zl21CGA3Pna2wiyFdDMHVY5dFq7A09yWG3HzXUyfwDYgQQkgm8AFECCEkE/gAIoQQkgldqwEVirm2Zb2vYifGHbW9xdycrXhaLNp4ebViq5GWylFKbgX6eqrW3qW3LyoHgCUWKqAXlcqRjoBxasfiB+PaHg1lNVVOfenePi0Ax5G4HT3mFGmxzhhSaFrOcYF91W2Ms2MKtw7i9w/ba6BQgiqnmJKutIJCKV7HEbHp3ljmAfUirR3geio9NhW8CNsNPaUcfCSdD930aYbLf9dz7mC75npLo5+WcfzSMa0mpLqjbY/S7BpgGYUanbbawmV95RdE7P64lUmbsW206fFWrwV9KMxhGZf4cg25XHzKfxx8AyKEEJIJfAARQgjJBD6ACCGEZELXakCtZiit/FJ8UVulYywd0TFJ1HEw9lmt9sW28bvVil22rEouoE2Pnk8kYnWfQsFvge/TK5LmU5hYekJZB6+VSsp5HN4+T7ll33fxXGEJ62W+HLsd1NX0upLKSeh5QnPTdl5ZD8xJQ/1FV1XAODxiSo6AtlQEDaiptAO0B6rn/DqCtrUq5kEfQl2hped42PWksblJnAfkvcZhZWq7yfeSKqWBc/4SNK28ajcK/tIH2lbJsd6Ba0RvB89dGk0uab6XXheWbkD0vbaakugrgW9AhBBCMoEPIEIIIZnQtSG4hdl5aZZedsOOUhkxnIKvrTrtcXRko+lDqwsMwZVKUVgNXz2LGGYDF2uNLx03zCdYguCrtbb9gD8XvFUePdVScVlnPS27tD9M6K80mdMWOTAGZ7u+dPvAHwrQERXfvoqgUziEPTwhB8ciJ/CtV6RUjbfiwTBOrh71u6nVtq3TghfnrMN7E1J7F6Eiqg4p4naKHudstJ9BN3IfuRxawXSewo0hd9/t46YXR+2mQBgNfjfyeI37Up4hLVunxePxRydzvT+43qSwmg71ofu1b12OJOCEH/W+YgVdf6jS/uSwIiohhJDTBD6ACCGEZAIfQIQQQjKhazWg2enpdtXRwXxURbTV9NtKlFTphp7eQdMXtGyKZD4hNqoJQStoqXWlSY8WGD+mS4OruomfYzmGwJNKijFhv/2GP/UV4/J63alSqzHFHDUtNeQAtZn44pBL61Kxdq82JraKKGpLmO5t1CKsagopt3NTNk07PxutCyuvYlufW9QR8JrX9jpYhbUIKdxNT6ovjt9JX/dofchqbflj14ulD/R1gdeE59csDP33aADXuF5VA46/tvsScaugmvVAdVt9nh29FMcEeoy1UcLU8CYsG2+Rk4f0e/FoN04pB891oMdEDYgQQkhXwwcQIYSQTOADiBBCSCZ0rQa0sDAjjcZS/HTd+tH2//cN9sV9RURgToFTviAppz1FHFvHONPEvBNsbjrdpIidY4Pj8MbOE7brbse2jatJkv7lseJJZemT1L/Cct9J6/FqZwl2NNraBkt/O9tVWg7qOGiRo8s14DwZtP8vgTuQKQGA2lgzXnPAOUKuthF/nIKWf76LrxwDbjfnse1xSjcovQW1C9+8HxGRkrL5qTesvoJzr+ZnIg3IWU81fr5gUjl7hw51laVFo2WTyjwYeyDQktz5hGtX2lyEb0CEEEIygg8gQgghmcAHECGEkEzoWg2oVltoxyN1XHtk04hZDv25Jo9Ntj+jfxvOm0HynpKyPv0on8c8+3g/q8TyC6gRecaM3/VpGziHyEtibN1XzhvGv8JyEmmPS9EcY4Fl43WepH0rePY1SXfS20XdAzWhlvL5KhTtbYlzfdBfTINzlXD82lcOdcImaECBajeC+LlUIracRLli7zssU4HXovETg+3gslrLQR3HmcfkKTNQwOsJNKKmOo7zUIbjxKETpj17arb9uW8I/CXxXKlxoNbn3g9w7eXj+xxNUR3URgNKvEM7jRaLv3V6HGHoL/uwHHwDIoQQkgl8ABFCCMmErg3BBUFDXnas16+BA8P9sJx9ZT9+8Hj788xJSIFsxb+mitiwTtLrsA7d5IvxfdjGcFBSaCldRVTpGO++pqiIiuP3pZm7NvDxA05zXBAnjRz602zXVFrF0CpU5EyyVjHbwdCSx0YJw3V6O2XYRgnSo33hx2LF3v5lCJU1VfqxY9HiSa1u1P2+SVgGoq+iUtATwuRNT9VQPNwFdc8WE6qnYnmGxUZ0zCePTpm+w88dNm1djqF3ACqgwv7ofceU+aS0cm0nlHgv6SkZYKfjq5Ca9LuH10HqVHKAb0CEEEIygQ8gQgghmZDqAbRr1y654oorZGBgQDZs2CA33HCD7N271yyzuLgoO3fulNHRUenv75cbb7xRjhw5sqaDJoQQcvqTSgN66KGHZOfOnXLFFVdIs9mUP/qjP5Jf+7Vfk6eeekr6+pbSD2+77Tb52te+Jg888IAMDQ3JLbfcIu94xzvkO9/5TqqBLS7Ot0vJzk5FaY5zYH3eN2zTHnX57iKmQIIjBcZGdYphYokCpUGgFuBNIU5KHS3Gp3QnWb8YvWIVFjm+VGrEp3fhdnz6Co7Dl2KeNMakWLp33+H463Pr6DYpNK0kayG9v85xArT+Ul/026xgCre+vpLGVDH2QPZeQtse1K18YImI2VZkbdNTideHsJ1YdkPd3wXQMgJcFsa/sFhrfz7+4jHTNz9lf4N0qex6guVSTzUaP9r0YNkExOqpRejD+zB+Xb6SC256NwiqzneX306n5RhSPYAefPBB0/7bv/1b2bBhg+zZs0d+6Zd+SaampuTTn/60fO5zn5O3vOUtIiJy//33ywUXXCDf+9735E1vepOzzlqtJrVadLKnp6fTDIkQQshpyqo0oKmppeyQkZGlyaF79uyRRqMhO3bsaC+zbds22bJlizzyyCPLrmPXrl0yNDTU/rd58+bVDIkQQshpwoofQEEQyK233irXXHONXHTRRSIiMjExIeVyWYaHh82yY2NjMjExsex67rjjDpmammr/O3DgwEqHRAgh5DRixfOAdu7cKU8++aQ8/PDDqxpApVKRCsR5RUQW5qfbccwTx6K8+4nn1pvlNp63ya5Pxa3REiRoYpwa4536s18H0boClnH25eS7+hBYvxQ9fxOsQttwV7WyuTy43aQ5REbb8NnaAD5r/Zc23PGYXD1JYpf1tdNoS0jSdsw5cOaK4TXiie+DlqHnqIiIlJXuoPVSEfca11pTkLfrrcB8o5KyD0KLnHrTzgtCvQWX931XazdlOA7Vkt2fqud81LHMdt0ep6lj0dwfPbdQxNW7aguRhlWbr3mXrZSi41bttWXZUUtGfHPqfPOCUA9yS33rzzi/y44hBE0or8ozaDujV7Qk9y233CJf/epX5Vvf+pacddZZ7f8fHx+Xer0uk5OTZvkjR47I+Pj4SjZFCCHkVUqqB1AYhnLLLbfIF7/4RfnmN78pW7duNf2XXXaZlEol2b17d/v/9u7dK/v375ft27evzYgJIYS8KkgVgtu5c6d87nOfky9/+csyMDDQ1nWGhoakp6dHhoaG5Hd/93fl9ttvl5GRERkcHJQ/+IM/kO3bty+bAedjYXGu/Vp54sTB9v8feWHMLLdu3Lpj69Rrn5O0yHKviSrcksI92n3bjLdkQbdffKV18ISAHOsXiV8W8YV80lQuTQPuO5KYZr5G6MOGNkpOSn2x83CjL6yG6d2+9PskGyLvsklhTl0pE9KhMaSlbWPQyqaAISC1P2Vw8y4lWBZhSrRvO5rFuk15rkHl0opKHS8VwHYI9hXHcGriVPvzHKRdt2A7jUYUdkPbJAyBVlUIDqeQlEo2JIf4wlp5n1VYiukCSbi2Psuv9xVJw/74xz8uIiK/8iu/Yv7//vvvl9/5nd8REZG/+qu/knw+LzfeeKPUajW57rrr5G/+5m/SbIYQQshrgFQPoE6eatVqVe677z657777VjwoQgghr37oBUcIISQTurYcg6bZjOKqszOnTN+piZOmrdOy0YYE47OYIrnS2ChqGz79KEk3WCvcTPDOt/OKaUDwBo3pxJ3GjZfDWszYPt/4i6BX+EpruKU04tO7cbuoLfnKciTpRSY1PGFMvtR3R1OEEgu6QqpP8xGxGspczaYiIxWs+FoqxSy5jL2OumedffWc50bL6ja63IKIyNSc1XlOHYl+Z/B3AqvB1usL7c8LswumbxHaet+HNwybvmrVakJ4fvQ4CqBp5bAqc07rhP707jS4Nj7RNbISDYhvQIQQQjKBDyBCCCGZwAcQIYSQTOhaDai3d7AduxwciOb6VCBOOn1yxrR7Bifbn8/YfIbpw5x8X6ljZK10kKS5PO5cGdVOsPGx63X+xzsODca8V1Vi3LMd3/H2zdFabr3ekhc+i5xX8k+wFPOa9P4610Detyz05TFGH19CuVDya01aP2piGWeouq3nCeG8H6TRglIOaoxV0IOKcM3rdhO0GNSpjDUMXP+LDftbcPLwCdM+diAqwYDHsNm0GpeeB7QIWtLs1JzEsQ40oJ6eftMuFMBKLKjF9pVLVu+u1aJx5Jx7B7VL3+9I52U2zHVJDYgQQkg3wwcQIYSQTOjaENzg4Gg71fDMs85v///4uRvNcs26jQUcfeFo+/P6s6xz9sDogGmfPGRTuLXFRho3bMR5+1QhraDlDw8FmNKtU26dFEjYUKA/+sNoOiQXNnGbsat1yMPfMC0cvwoJOSnBsOI0btmOnYvaLu5q2MI0+WiBVhOPE3w3r6tFQvg0IdSU18fCCavFp0/ncbxOWFN9D62DWv7zbr7b8Ffy9Tmm1/EiMZVv/dYvjnu8ChFheK4HQnK6ImpPjw07YcRTh+jmwe0abXymjtlCmHMqdIb73myiFU+07sWaDbnNnpo1bZ2iPrh+0PQNjtp2+RBMI1GhPjfdHiuk6lR9tJuyy+owW1Jl2yQJIS18AyKEEJIJfAARQgjJBD6ACCGEZELXakClUlWKxaX47/DYuvb/b9iywSyHMdYTKp3ywE/3m74N59hSDhiLDoIoJt4Cq/pcCO1SvO1ELvSlDON6/X8DWHsL1A3itQJcr2MXlKJ6Zx7EGt1y9h21szB+O7p0xnLfNdsEXQTdRZpKv0uyo9H7gxFvZ3+CeG3DOZfQH7Ti9SNHkct5UlhhkNrqydHCWv4Yvt4HrJ7a8qRsp7Fzce1awILJ83cvahCo3bSUXtELy/aUbUVUbXuDx//krP3dmDlpNSBt//Xy71B7DK0GLBuNUac/i4hMHZ8y7RPT0bSR9UNW80HN+rm9UJ5hPhojHlMco9Z58NyhBtQyNkWdp12vBXwDIoQQkgl8ABFCCMkEPoAIIYRkQtdqQEHQlOCl+LvWYzD22b/O2lf0DUZWPScnjpu+2tyiaWPe/akjk1EDHOLRql6PI41ND4b3cW6Pj6SS0HqSB5Y6cC39O9+Orz+pdIPWX3zzTNz1eofgHZO7HWfh2PWkKcntGwOOI6l0ttFbEsavtSbcZrFkb2mf3U6SfqevoULJr9f51ov7g+WxzbKeMg8idp5ZDUoqtEAT0hoQrqe2YO10pk9YDSgIIl2k2bDfrdft70hL6UWLi3Ye0NSxqdj2metHTN/Y2Vaj7u21v09TU5E9UBOshFDnKRYjPcyv+VgrHneej0B7dfN+EL4BEUIIyQQ+gAghhGQCH0CEEEIyoWs1oEa9JsFLvlCzU1HOfm3exm4HRqy/29jZ0TyhU0et11sNyjGsP9Pm3S/O1dSydjutnJ3zkUYDyqt5GzhXxC0r4PHjKqAGBB5PRV2GN6EsQop5QD5twNkOjl/PTcL1oNaRonwBbmeleourq8F2fHpXyhLdPvSctMRrpBA/fuca8YzZtemP1wnT+Llh6QPcDuIrpY2lHfSyJSjtXUBfRXWPYgluLOMyO2m1G+3vhhqKnvcjItJSelFQs78T05OnTPvURNSu/cJm04flY9aPWU3o5MnD0RhAA8KSCiVVnsEp3+3ch9ExDqHshp6fJmJLcK8FfAMihBCSCXwAEUIIyYSuDcE1W432q/zcTJQiidY71T5rV9G/LgrJ9Q/ZFO2pEzYkNwjhO53SvTC7YPowrVlbvyC+kFxSGCdNSndSSEvjWsyoRkLoy2dt49jceMpUJIXGTF/alGcdWkoYU8GXWu1LDc/7j3dSCQ/fmHyhvjRlEtLE/dxKt/FhWrTIQTsdHRrDyhNYqdSxD/IsW8S2Chdh6A5DfXpduE2ckrE4b9s61boIoT6ddi1i05pxO3Nzk6Z9/GA0NWR6wf7GjK1fZ9pnbbMhuhdfeK79eWHB/g5i5VIdgisVrUVRs2jDd4EKu+UgnTsXYnkP2x9gpdyU8A2IEEJIJvABRAghJBP4ACKEEJIJXasBBUGrHes2dueQHt2E0rp5pdWg1c7hF215hqP7j5r2yKbR9mfXpkSgndIrpkMcGxMdl0/QBtKURdZgfN+nmeB2U9n2JOhdJj06Ydk09jTedlIafIpjmsZex5e+nnycOte7fKniPpsebOM1kpRarUGLHNRufPYuAewPrsss67HxacL3UPOpLdi2/s3B1OQmWNnoshZNKNWA1jwntAYEJSC2vM5OC9l60VbT3vf4vui7U9ZmrN6w48+rtOxSuQrL2t/QfL6hPmMZFzj+TMMmhBDyaoAPIEIIIZnABxAhhJBM6FoNKJfLO/YSIq7m02pCuWg1CaFYtjUVdG68iMjJk0dMW88pqvTaZYOmjX2WVDnpes2OybU0j5/jgX8DoPWFSAoNIhe/LOKzyPGVecB1p7EhSqO3JJE4Zg8rLaXhkGIOV9JmzJgkSSNZxXHyfNVfHsNfJkG3sSx4koW/r5SGoz15xoRziLQ1D2pHjbrVcep1OydHl+RuCpbgBpsuZcWDpQ5wvs6Jo1FJhWMvWh2n8M/OM+3Xv+4c0953SaQJnTh+0PSdPDVh2g01DvzdK0Pb6l34ewrHP1zbkt18AyKEEJIJfAARQgjJhK4NwRUKhbZLq7Z/aDYgJRJCcrpyI74+DgwMm/aLLz5t2guzUSrjhi3WmXZuet609ev/4klb9TCU+NfYQt4e8mLJ2mRgamxJhRELRft6j9Uvi+X40+mrfulWT5XYZUVE6p40bFyXL2XbFzZLa8Xjc5P2fTcpPVqn9SeGG332Oh6X7aX+zivH5j3h0zwcfzwfvr40tlAtCEnrsBuG4FotWLbZeRjHuRZ9TuBo26PuB7TsQksvTJc2YSkIOzUaaGWj9h1Stms1+7uhq5oeeuaQHdObbSr1xWdZK57nf+ni9ucX975o+ubmbUq3Dim694qVJvRvUitvf2OwQrCuFIvrXkm1VL4BEUIIyQQ+gAghhGQCH0CEEEIyoWs1oHyuIPncUvyxqGKWGHtehAqplZ7oM8Yk+4ZteYbSEYgLz0TVCscKthph32CvaWtNaLFm48czM7YKoo4nY5y6XO4xbUyZ1O0KLIv6kY55J9nRaNBqJw1OfBl0KZuyDd/1aUAprIRE/FoTXge+NGyfBuSMMYXlj2uJs3IrHm/phoRz6df+4lO2MbzfAO1V6zqo+dShErEzlcKjHyH+Y2r3vac/ur/Hz91o+k4cPGHaNdCAdMmFFugeWBFV6z6oAeGyujzD4WcPm74XDllrsCvOtWnZv3TR69uf97/5gOk7edxOKVlYiCq+4hiKBZieUo5+Y7CaLe5PPh//yMAU9E4kIb4BEUIIyQQ+gAghhGQCH0CEEEIyoWs1IMnl2kFoHfdtNTC+jNbiykIe5huUKjb2uW7Y6jyTU1EMdn7K5u+fsdnOC9JzhqqVPtP34ot7TVvPBUB7IdR8KhWr82iNCJctFuOthopQhhdL6WrS2tH4NBTful7W9DoBNSDf+HG7q7GfSYOv5LbIyi1/0hxTt3x3vLaU/N34ZVGbadZRF4naeN816vYexbkx1srG3t84B0ffPzh+vB+GhqLyKr2D9h6dgVIItbqdg9NQukmrhZoVzGsyGpDfykbvO9rp/NOPnzftyUsvMe0zR0ban6/+lUtN3/M/fs609Xwj/VlEnBtC/8bgeFHXQRrq0OjrPQzDjsp18w2IEEJIJvABRAghJBO6NwSn0K+8mFKLrrZBEFlQNBvw+gjfHRwZMu1Tk1Eq49Rxa6+zbmzYtHU4r3/YVl7FMNrxY5FtBqZ0ojUPfrdSidK/sbJhoWC/Wy5F/Y7dBlhq+CpaYiom4vtubhWhPn+oKV1a9kqXTYM7JnA2T+Mc7MtZdVLFOz/GPlf0NNYpGHbC1F7tHo12LRiCazjfjdoYtnHDkfEhuLJzf0T3wPy0TbOem5sxbdyfhqow6obc7P7pMFXSMa2rUN/0zEnT9+wTz5r2T661Ibqxoej36tKzzzF9T+14o2kffTFyx3Zthuxvm75GMMyPITg8Pzrsqa/3Tq8tvgERQgjJBD6ACCGEZAIfQIQQQjLhtNCAdNyxBboOVirVsUdMFcWYcbXf6i1aUzl50lql9+63yw6MRLoPpq+OjW017RdeeKr9GSsk4pgwPq7b5Qba9thUax2vxTTsYtGe6pznbw8sJ+GL57qaA1rxFNRnv46TZjs+0mgbK7GQf5lCQmq4U4PBbBj0Ib2sr0/8FWmXqyIc990k9LFBLaDRgKqgLZ1KjRVEsQ0akCpv0HKsX+J1NtzXAljMaE1occ6mWWMFVNw/rfvg/qAOYlKX4dyhnqpLOWhbHhGRF1+wqdT/+OhPTPuSzVF5Bq0HiYi85U02Lfvgvkg/mp62tkO+0hN4vPE3BnVNfY3o309qQIQQQroaPoAIIYRkAh9AhBBCMqFrNaBisdiO6fpiizgPSJepRvuQpFLHPT0D7c/Hju03fZMnbM5+uRrlyzdqNkaMpb/Xrz+r/fnQoX3iw7X9iOLaOP8gDEvQjvbX0Qbgbw1v2eoQ9ZZ4nc1X6iAteZ9+gX0enSSHGpbEj9Edf/zcHWeej2e9S+OIPxbOcfJt19lOvO2Qz7oG8VnrLw1Jzb8DvQu1gjCM+sMAx4Bze5z65PFjcK4n3/mBeUHqHl2cs5qVr6TCUltrWnjf4fmIn6eIY9LrRT345ElbnuHJ//1j037iigvan//FxRebvq1nWKuwX/w/rmp/xrIPWC5mWln14PjxGsG5h6Zt7qvO5sDxDYgQQkgm8AFECCEkE/gAIoQQkgldqwHlcvl2nFnHUTEkjCW6S+XOd6m+CKVq1VwZn9eVSII/XcMuu2ljVFr3+PEXTR/m5KMdei4XzV9ADzavnxuMqQVzJLRXXNIcmzS6Dsb7W83ou0HCHJR0/m4+j7MUHmyrICne7wP1riDFmLX+gr5wjo7m8ZFDf0BE6zrSxLkwJWh3Pv40x8lXDgD1CLd8dHQssIS4no+z3Hb0/jj75pkL496/vrludkwz4A33wnO2rMv3vrmn/fn1Z55p+jaPjpr29vPPj9bztqtM3/HDtny3LkfeBA06aZ5ZnD5PDYgQQkhXwwcQIYSQTOjaEFyxWG5bfWtbGXx7xxBcS6Ve5/KQAglp2Zg+7QurYXq0Dl+Uq9au4tQJ+4o7OLi+/Xlk3bjpmzhiqyC66bnq9T6EfUVrdE8oIMhDaKCDaoXtZVOUQnBSVFUKdw7DE/D3T5gqBPfzsdtJs800lUwxeupLQc8XPPZGeG4gJIdhNv1dn60KtnFZt/Knsq6B6QJBQlXNleKGQCE8VIz2FW25Qky7dlKtO7fX8R2nnKcKMI6/CfZGJ05YO7Af/+8n2593X3iO6fvNX77GtId6ozIuv7b9MtOnbXpEbOivcdiGJjFVHNPvbUhXhy07u5f5BkQIISQT+AAihBCSCat6AH30ox+VXC4nt956a/v/FhcXZefOnTI6Oir9/f1y4403ypEjR+JXQggh5DXJijWg73//+/Kf//N/lksuucT8/2233SZf+9rX5IEHHpChoSG55ZZb5B3veId85zvfSTewQkmKy1jx5MBOB8sxmHINaIMBGhCmYftsyTEWqmPgPVDWIQ9xX53aOL7xXNM3OXXUux0TT2/4yxfodrFoNauiU6JbHVOP3f9Sf+clodOkJjtp5Ll4ixnnuwllB14J0pS7TrsuX3kMn1VN4npTlDlHtK7jlqyGMtsqrVmXsxYRaWE5BtBbfCW5Eb0/eJ9hCZIw0KVZQO9FDcijcWHfWtlP4b7ieubmbOnsf/qnH7U/P/o1a73zunPOMu2rzoumfmyBFO1ffet20z5x8Hj78+J37bSQWm3etFsttOrRmqLuewXTsGdnZ+Wmm26ST33qU7Ju3br2/09NTcmnP/1p+cu//Et5y1veIpdddpncf//98t3vfle+973vLbuuWq0m09PT5h8hhJBXPyt6AO3cuVOuv/562bFjh/n/PXv2SKPRMP+/bds22bJlizzyyCPLrmvXrl0yNDTU/rdZFV0ihBDy6iX1A+jzn/+8/OAHP5Bdu3Y5fRMTE1Iul2V4eNj8/9jYmExMTCy7vjvuuEOmpqba/w4cOJB2SIQQQk5DUmlABw4ckA984APyjW98Q6rVavIXOqBSqUilUnH+P5cvtOc0mDB2gPNzbKyx2YiPIfvmLojYeGapZPevXoe4ttKaKiMDpq+vf9i0dRy1r9eW0h0dtZYahw49EztGnIuE8X1tJ4LzP4ytytJ/tD9imQq0OHG0Al/pbI9+lKQ5mPg+zjfA+RQpdBFE2+cnlanwaSiohblW9vGlsxGfrVIqEuZspbHB0fcL7qurYa3mfOgyIn490vbZMVWrfaZdqka65/ys1TZc652VlxzR48e+JJ3HYn+f8H7XpbV/9sQTpu//++qYaY/8VnQsXrdxo+m74lyrQx/8jUgTOjlhSzW4JSPs3CQ9d9L8LrwSJbn37NkjR48elTe+8Y1SLBalWCzKQw89JB/72MekWCzK2NiY1Ot1mZycNN87cuSIjI+PL79SQgghr0lSvQFde+218uMf2yJJ7373u2Xbtm3yh3/4h7J582YplUqye/duufHGG0VEZO/evbJ//37Zvn37cqskhBDyGiXVA2hgYEAuuugi8399fX0yOjra/v/f/d3fldtvv11GRkZkcHBQ/uAP/kC2b98ub3rTm1INrFQqtdMqi+XoVRrtc5rgapsmJRJTSX3frdcXTHthNgrJrRu34YdKjw0p6vWiq/bY2DmmPTtrX4Hn52dix+TaYmiXZHtqMUW1XI5CjAVwytau4MutS4OhGAzfGauYFOneSfY/qUJAHkffpNRq3Y8h3KTQjC8E54bvOq/E6iP5uEX9SdVTtT1NA9Kw8X7QIeoypGHX6zacjfddSa07VRo/9PX32fB2UVnx4DYxrdyEksRWLk0Kq/lIcsdO812d6o42PY9+/WHT7huKQnDv/I23mL7NIyOm/csXX9j+fPhfWK1+bsZmJWNK/dR0VE3VhDU73M8194L7q7/6K8nn83LjjTdKrVaT6667Tv7mb/5mrTdDCCHkNGfVD6Bvf/vbpl2tVuW+++6T++67b7WrJoQQ8iqGXnCEEEIyoWvLMRQKZaUBRcNEDagO8eZiGB+fde3PIQ7siftiDHl2drL9eX5m2LudgkpzLpetPhSAdf0ZZ9iJuAcP7lPL2n3zxfddLSO+jZpPsWjHWMS07Fj7Db+m4mokWCoghdbhsfFxlvVoT2tlq7IcOibu6CsJetJKWUu7IK2LJFnx6HupXkebHr/+orWNMMRrPD79G/XGSqXXtPUhdTUfKBGRYLfTDeh9mJubNH36d0JE5DtfjuzBqn1Wg/v1a6827bOUm81111qtfuak1aCxgrP+zdT6dac6Gd+ACCGEZAIfQIQQQjKBDyBCCCGZ0LUaUD6fb8ev08zF8PU59hsh5v5r6xH/s3lhIYqNYpy05bEDQn0lDK3VhS7fLSJy/PiL7c9TU8dNH87j0LF03NdKBS1BdPlc21cqQYkLmCeEdjU+fPMeXMufaNm02oyZc5NgC4Mllc0YHG3Js6+euTvuiv3zc1aqOaTVfPQ1njS3Si+LOg7OB9HzhFBvcTQfsLXC8gwWv+6pQQ3INxXFOd5wfsJQ68Gd/+YkzyuLPz9p5gjhMUVN6IUXnmp/fugLrtWZ5m1viUwCzh+3lj5YumFuympAwXeiY3PoUKRD4byqOPgGRAghJBP4ACKEEJIJXRyCK7YtYHL56NUUQ0v4eux79cNQk2O/0YpPw8ZXZx1GQLsKtLbR9PTZMEFhzp4CdPQdGdnU/qzdcEVEms14p900FjNJzrWpLGYgbdZ80wn5pAhhOWNazXe107E/vJImLOIjySJnpSnoObRcgmuvAJVwrd1RfBq/CFZErcb2idj7ElN10Um+VsQqm1E4L8mxXp8fbScl4rrQB8o530kLTrjm9fJuuBTsddT5wLX6qgcn4QvT4m9XGNoQ6fR0FK5/5pkfmL7Cf8NwY7Tet/7KVabv0rPPNu2ZG95s2rWFaLv2emnIM8/skST4BkQIISQT+AAihBCSCXwAEUIIyYSu1YAqPRUplZbSB7WtOlp1uPY08Xbz9Zq1kMd03JZXA4IUVbVuLLGAGpBOmczloGojWPNgbHdkXVTI78SJg6bv5Elrna7j55imWa/HV+8MMZ7vpGVb3a2odIU09v9umm8TllX6RMq/jQJ13ELx60P6OK3GciWNPpSkleVVBVunpEUe29raqcf0VSq23QclCqp90fWmy5yIiDTr9jquzUfx/XoNSpcI6iDRmHQFYBGRWUgRXlxEPSm6DppNe03gurS+VAR9q1Sx7fpidA+gttQK/FY8pitJL1K/SXlZueaTtB3fNAVXG4/6Z2asdrxvn9WEgv9HaUsNu55f+cXLTPvCM20F57kbrll2rPXaonznO/9t2T4N34AIIYRkAh9AhBBCMoEPIEIIIZnQtRpQ/1BfO8+/qGK7qJGg/qLjpM6yLSjnDTqJtn5PM8/EZykjYm1Lkkr0OnMM1DwCLNWA84L03KRmwvi1dhY68XH/MS6VovLeqHel0UV8NiX5FGWoX/py58uuVPdJOFferybYA4lnfo7/a52XOcc26myo62g9pgX3TqOBJRXUtYf6Y83OA0L9xWpyMC8Lrk19nMqlCnTZfa3HzFHBbYq4erDfbid+vlSY85fv9s0Lcuek+bdrl4VSM3DPaiYnj5r2s8/+Y/tz8EU73oVZe+4uf/Mlpv0LGyPrntb10Ryihbk5+fT/HTuENnwDIoQQkgl8ABFCCMkEPoAIIYRkQtdqQMPj69pzGhbnojikU1LB4+8WwLKoCWEc21v2AXUSj4U8akJ6noCvVIOI38q+p2fQ9A0N2dINp04diV2vqxVEsWgsr1CAODXOt9C6j9aDltuOT8/w6RdpS0lr/S6p1IGZT+HRGJLAOWnOmPTfd6uYM5SGpO9655qE9ruFQrxnXqFgr5FWSx1/0LvyuKxn/pfzNzE09bXYC3OckJqa9+fzlBNZxkNSz5MTvH7ir3HfvMTVslL/Q98cIRGrCT333A9NX/1rVgOam7LlY/75r76h/Xl8/Ui0XNX+LsTBNyBCCCGZwAcQIYSQTOjaEFzfQK9Uqm4IDu1b3FfpRmwfhkww/OIrHYCv4dr6BcNFxTKmZUf9mB6ZlFau2zrVVURkaPAM09Y2JbgeTP8sFqNXZAyjlcHOpQTprqVSZKVSKtrvov2/r3QDkpiqbBaGY27sgRK2s8IQV9oqrb7SB86yafbdbgSaaLUPYRtfBWGPNRVWRK3Xra2VDstian4BrlsM6aaxRiqq8Pbg4Kjpw+/W67XYPsRJ01a/FfjdQj7+mLu/T/HlJdKGS31WPN71YIkL6NfnVpdxEBHZvx9ki2/ZczlzKgrJXfCmC9qfF+athVIcfAMihBCSCXwAEUIIyQQ+gAghhGRC12pAmvqCx1bdSTHUqbxW98iBBb5rraLis6H/2axtS9Ba32evg5YmSWnleky47+WKtbUfGIhi4rOzp2LHkESS1qHXFYCuVpD4cuQIng+bHg3HAVNhYV1pYutp8JU5T/5u9Hkt03FtOXXsiy8XvbS4Tq3228bg9dbpmHwWS8uNyaeVOVqHWheWr2/W7b2kS4HjelGHSqWpONdi/Ph90zXSluu2peRXXjremVIi8Vof/o4cOvSsaetU95OHTsauJw6+ARFCCMkEPoAIIYRkAh9AhBBCMqFrNaDFuZqEraXno453YtxXx3lF3Di2xmdNv/Rdbb/ht+4w8VjIrA+a8fZA7rwftN6xGpHZrhP3tTFkXY4Zy/DicdJxa9SlknQ23cY5Qr75R05ZakcrUPuTFJP/eZRfEHtu05YJN6yhFY9PO1hL/UufZ7xG8HrS5925pkEPaEB/Gq1J20RhOfJG3W4Hx6HBY4jXoh5SIR9vrSVir1tcrzvvLzqmjhaWYD+14vLxeE14fsu8vz8iEoZWE9LXxcLC7LL/74NvQIQQQjKBDyBCCCGZ0LUhuFbQaoeryj2R3UvfgHWEnpubst9Tr7hOSrPjnB1vi+OG5+JTVB1XbXiu65REfNV3w1vxr+z46p/P2dPns0NptayLrdk/eFvGkIMbOtNhNb/7tX6FR5dwN6Tgd5c220lIkzfbEaw06bGj8fSlNOjGQRh8KeiObRJW3PVW1fSn51rHcf/0AX2Nu1Vx7RSAZjO6xusQCsNrsQihMX2NJ6Xfa5uolysmv4y2ohKB0B9cp3gt4rHQ+54roL0RpK+L/t3o/H5AV/AC/hyvIrXafDcpdOdJ726G/lCaXl4fM8ddPAa+ARFCCMkEPoAIIYRkAh9AhBBCMqFrNaB8Lt+OV/cO9Lb/X38WESket/HlxcVI6wgDG0PF9E/UbjS+eLiIjVv70j2X+qM4KlYbxRi3a48SLPtZxNUkyuUoJbqnp9/0YXxc61Jo34LHybX8j7cewbYu15C0rGOVtEY4elEu/pj6wGXTlH1Ik8LtlPeAVHedbo/TEhxbKFhXb3+0fP86e41U+6ymklfaR7XXjqHab1OgtQ3O8YPW0v/4i7Y9O2NTeRfUPevrE7Fp/6jjoP1Ly+hJVpMooi4FZUXCUKcUx9vpLC0b3bN4T/qseZLssVCL9WpCK+1DUC9CTaiJUzZU2Yqgtez/++AbECGEkEzgA4gQQkgm8AFECCEkE7pXA8rnJN8ufRvFMAtFtMxAbUaX0rV9qNX44pT5hNLGpiQBlrx15gwpSxOn3IJ/jootxxC/XhE736JSsVqZY13f1CUuoOxuw29v5Nv3cjnewsixsfeUKHD1If/fSj49Zq1KGyfh14Q6n+OE5xU1CF0OW9vhLwdeX1NTkdZROWZ1HCynbu3/UX9Ey5noepqZtjZQ0zMnTbtWs+Wa9Xdxf/CeHRnZqPpgzpxPn4DznC/Y8ZdBZ9M6XBO0Yt9xcnXa+HlBvrIIuKyInfe3lpZLGuduSNCEAvV725D4sjlx8A2IEEJIJvABRAghJBO6NgQXBGHbndq4tTb8btL6NRyta9DBF0MbOq0Tw0WIDgkVCmjjEW/b4/R5nKbxu/jK7lgAqddyTCtF5+ByOToWOqQjklwpU48RU2Hx1VuHbtxQmEB75Y7ReA683/X93eUbg1OdcxVhEDwWHp8fxx7FOE/bsFNyanh0rufnp00fXnv63sJ7pwHtukqBTnJ4xxCcvkbQITrvsczxp11DCBFDYxBGK5bs/aLD2bidNCFdJ/Snxt/CZT1yggik2L9CITjHOijpCzr82NJyAUNwhBBCuhg+gAghhGQCH0CEEEIyoWs1oDAM2/HToKVs4evx5QpErO6DMVTUTDC1FPUMDaad6jTnPFhmuBVGVWwUNR9MBfdY8aAuhesKAl2OAUs1xFdPxfUkpUvrdRchll6CWLq2TsHqqWiHkkYHcTWgzlNUrZUQaEc+e5Q1TAX3j8k/ft+6kuyONBinR5sofc2jLQxeX7pEBKYe43oRnwaEKc891cg+KLGasLl3/JoElpfQ13FtEWy5UDuGMZoxeMt72OPilmLB6sJKd04o3ZBGT9VjTLbL8liUrWBKA9+ACCGEZAIfQIQQQjKBDyBCCCGZ0LUaUKPelHxuKQZaX4jy8Odn7JwVR7/QJW9bGBO2eouvtDHOfcHYZ0WVA8Z4OMamzTymhHk/WKY38Fico8blm5+D8WYNxrDR0sTVpeLz/X22RI6lD87dCePLCLtlte1XtX2NM8/HU5Y6l+vcImctWc2cpzTLoqaiCRLKAZhzB3PqnBLQZgxgY+VoijCmsPP909cqarrO/a6umaRSH1je25R9gPsD5wXl1HHC+yyN1ROeqybsj+4Pi3a9rn6tr/H4suCrxcyBsh0dfZ9vQIQQQjKBDyBCCCGZwAcQIYSQTOhaDWhhZl5ataD9+WXm5ibNcnWIx/p8sjDmXczBXAYVt2w2/dqATz9CXceWVMB4bLxv3FJ/fOkD1LQCTywa5+DoOD3Ox3Fs7Z2yFrrEeLwfHY7JHT+UuDbazNr9beSby7AaW/tU2gz8rYdznvS68Nx5dR1njhZ+N347bpkN9C2L7o9WCzRF0FS0Z1uzaa8npySBo3XEl6nGfS8W9JhA88G5Yp6/r3Hf8Zhr70T0VXQ0Xn0dJ8x58pIw58bow/4q4faeXsUcoSRWey/xDYgQQkgm8AFECCEkE7o2BFdfbIgES89HnXq9sDBrlvNVOcUU7SRMBUUMTwhap8eH6HyhDax26aQtO/Y68VUdcUw6NIYhBQwj6LBIC21tIO00cOzolTUSpoo27d80urIsHjNMOc+LtnMR25do59K5nUjc95JwQ2H+dG/fd3F/dOgMz5Xbjs5PT0+/6dMWSyIixTKWHYi2g2HAYsn+HBTLUTuAlP+5KVtSoVGLrgMstzA7O2naGKLWFXjn5myJCCwVokN9SdZamrQWM7qCcLlsw9eYhm1+gzwlXkT8VZgdeym0+FL3C953iL6/8+ErF4Kz69Uh9M6+wzcgQgghmcAHECGEkExI/QA6ePCg/PZv/7aMjo5KT0+PXHzxxfL444+3+8MwlLvuuks2btwoPT09smPHDtm3b9+aDpoQQsjpTyoN6NSpU3LNNdfIr/7qr8r//J//U8444wzZt2+frFu3rr3Mn//5n8vHPvYx+cxnPiNbt26VO++8U6677jp56qmnpFqtetYej46bYmq1Y2Wj2pg+nFQuV68byyRg7Lah4r6Ytoxj8uGMwbFkb+qFO14Xpt+WSmjNo8sXNGBZG/N2yp6btFn/mLRegcc0hyWHV5PCqnBtlMAaJsGCplMKhQSLGbNN/7KtpirV3PKn5mtdoQAp9NU+e4/li1hiQWkDeTuGUtVqTYOjg7FjaDZ8Kdy9pg/LeWNbp3jjccFr0W7Tf55F4vXTJHsaXW6lWrU6W70OpcBbeuqHf1qCDydlvmDb+vcMU9Abod2uplgC2548ltKIfw9Jc0+uJCU71QPoz/7sz2Tz5s1y//33t/9v69at7c9hGMq9994rf/zHfyxvf/vbRUTk7/7u72RsbEy+9KUvybve9S5nnbVaTWq16IROT087yxBCCHn1kSoE95WvfEUuv/xyeec73ykbNmyQSy+9VD71qU+1+59//nmZmJiQHTt2tP9vaGhIrrrqKnnkkUeWXeeuXbtkaGio/W/z5s0r3BVCCCGnE6keQM8995x8/OMfl/PPP1++/vWvy+///u/L+9//fvnMZz4jIiITExMiIjI2Nma+NzY21u5D7rjjDpmammr/O3DgwEr2gxBCyGlGqhBcEARy+eWXy0c+8hEREbn00kvlySeflE984hNy8803r2gAlUpFKhU3zluqFKX00hyGUiWKTaPVSJKuo3FtYuLn5Pjy9XE7qNtgjFuvF21JkvQjrVck7WtOPHMM4G8NHIdZ1il3bXUGn90RfhePhcanxWBp76QY/krxWdUk4TuGadHlDsKmPS54jeh5NtPTJ0xf7lDn1vtJOojebgO0jcXFOdPWc3lQB6nV7Fwe9xqPH2Nf3yC0h2K/58ztUecnbQkCfW71NkWW07Ci4+SWTPHPC0o1ppzW70AfQo3ON08IKoib8t4pytmvBanuoI0bN8rrX/96838XXHCB7N+/X0RExsfHRUTkyJEjZpkjR460+wghhBCRlA+ga665Rvbu3Wv+7+mnn5azzz5bRJYSEsbHx2X37t3t/unpaXn00Udl+/btazBcQgghrxZSvQ/edtttcvXVV8tHPvIR+c3f/E157LHH5JOf/KR88pOfFJGl17Nbb71V/vRP/1TOP//8dhr2pk2b5IYbbkg1sEKhIIWXUkitg298xU0Rf1gnKYTl+24R7Gl0iAjH5EvDdlJsnVTx+Aqjye7Fnlf00B9S9IH7HoZRqq+bdhrvfJzPYRgw3o4Gw37FIqSOpvnbKYVFTpoQQ5pwymqqUDo2Pvo4JrmGe65px3LJU+UUbZOca8Ljeo5WQmmczvG7et2uc3b89ZXkfu077zolW8S1P9J2QU2wB8JQmAmjOVWL/WE1fR3jtYeVffV38dxJE675krpHBSUOPMZrM1XiZVI9gK644gr54he/KHfccYfcfffdsnXrVrn33nvlpptuai/zoQ99SObm5uS9732vTE5Oypvf/GZ58MEHVzwHiBBCyKuT1IrY2972Nnnb294W25/L5eTuu++Wu+++e1UDI4QQ8uqGXnCEEEIyoWvLMTQbjXbVxfpi5JSAmoODirWn0XgQjLHqColL/VEM3CnN4NGaMBbttuM1IWd/ElI+bZ9fP/LhVIssReHUpHi+LhmBetdaEorHij+MH2MaZca9nvyW+Cu1YUn6nj6mOTymqIPAdRx674/4o4H6BF4TejtF1DVT6LQIVuv1lSfBrHifRpek/el147739g6Ytk6Ld6ql1iG1XesxTokO/5QAfcxDp8QCfjc6Tm7aO/yGqn1FXTDpAZHmul0OvgERQgjJBD6ACCGEZAIfQIQQQjKhizWgQPIvxTF98xMQozkk2On4YpY4ZwVLHRubeLQwCeLtdJKsOjA+bmL2CWXCTe7/GlqAOPFxMx/BagFYJrnVipZF2558vg7t+DkGzWbn8fE0pJmTgiTG7HPx5a/ddUXfxRIE2Nb7WsJy3WBhhPOpbCkHv/2/BNH11WiA/T+Updbt+XnraD8PZbZrdbTm0VY29vrHOThhit8Ccw9jiXc87878NaW9wn2GY+rrG25/TtJatY1Pkhbm11TwerLr0vsXprDtwX11xuDcd7or/T3INyBCCCGZwAcQIYSQTOjaEFwul2u//vksZhBfmuly24hdD7yKYihDW/Gg26+TSu0JjbkhOUzpjg8F+NJo0bEXXYX1vie5CqPtig6z5RNeu/X+JoXNdDgPQ2MFZzNY5TTUDduXcNxWCoZxMMymK6bmwvjwnIhIkIsPF2PIZ2BguP252g/TA6ACaqliz125J7pu121YZ/o2bNlg2oPro2umWbchuFNHJk179tRM+/OhZw+bvoPP7TftuTn7XZ3GXIfwnOvEHl17bio12Dyr81NICNG6vwXRdwOc3gDnXbtlY6VSn+M+pmxjiBrvu1QhOdUsCNr2xFuHOe72Ep+evjTG6HrS92yn9xjfgAghhGQCH0CEEEIygQ8gQgghmdC1GlCr0ZTmS3FyneKJab6ooeg4aVLFR8TYSkDs07U/VzHiBKsRX2q4Y5+TopyEu4DWmrBKq02btfZAOAbQJzzxc7TuKDjbjWL6Sfuu25UyxMMhFdl3PhBf7DydXY5dtoVW9SGmu/oq1Mant6J+tzA/Y9onT0YaSxGtdjCNFraTU+eyXLYO9T091mJGp3+jNjM3N2XHuBCNEdOwZ2cnTdvVSeKv+ZF1tpBlqexWT34Z77SKBO0Yrx997nIJ+pHWqXp7bfVUvGf1vi5CsWDUmnyaULKVkCphA+PNh7isniZil8ZKuO4Uk+V/kzqdOsM3IEIIIZnABxAhhJBM4AOIEEJIJnSvBtRsta14vHbnqyp17Hn+OnNhoKywiq07Y3JKKui5SfF9Im4M37esa5MRn4eP1ik+sHw3lhk2QHzcLaEcr4OgjqNj3I4tPKy3jPY0al1Juk6SHqAxmmLS32srsKNP2mYSrnUKlEmAfdXHGM/N1NQx09baIGqveM1r3QqvNRxDGMTPWcG5VW7Jel0mwV/SWs8rw7IOiM+CxtVx4q/parUvdj1L3w1i+/TvnIh73/lKRPjA+XY4fq1h5fOBd1nUdrQ2aEvHUAMihBDSxfABRAghJBP4ACKEEJIJXasBhWG4rL6TNLcnTVlhHzhHAjUHHTPG+DjiKyPslitOUSo7RSkBnMeht4NamG8ek4jVv3BZPD+6fHfiHJU08zgS5kH4SFPew24j3fd8c9J8ehJeAegJpq/NPph30ttn2z3gFVftja5jp/yCM5BoJM0GzOFqoMdZtGyraZedm7HzgmZmTpr2vJrnhNdpp+NbDq19YGlyLCOCHoxG+0jYjr4HUNfEttaHk3QSvE6tr6K9JlDn0Zdb0nbsnEDbV8jjfRXvbWdLTdALjhBCSBfDBxAhhJBM6NoQXLFcklJ56TWztyeyhUdr+oWFWdPWb9Jpq2bqkJZTARUsQGzKYdIreuclInz9SSnbviDUatLVnVd4bUOE5SMwRKrG6Fi7w/i19QiGF1sFrKaKIQdPaQ1Mi/ekuvvA8WNYxxfixZCbY2GkQkKYXtzbay1y1q2LyiaMnrne9A2M2GV7B+390j/c3/7c02/DzMVyfKoyhtXwvLeaymJmznrMzJwEK6HDJ2x74lT786kTR03fiROHTNukfzthTSx5ocoxFDA93W/lpCv5OrTirWwcCy+4Tvv6h+PXC+A9u7g41/6MNj269IdIfJmEpXZ8SC4IMBxv15sP7XHT9kFNsFjqBL4BEUIIyQQ+gAghhGQCH0CEEEIyoWs1oGpfpZ1u2qhFcevypNVm/Km7nesEIjYuj1oTlgbWdiMhWNf4SkB3alGx3HcRTNMslZSlBsR9m571uNpMCpLSobX1u5OijSUWovFj6jHqLWlLbXSKbz1pUr1FbNntPGgQWEZBawPDw2eYvrGttiTB1ovOiZYds2W1Hd0TUq11ie6eqj3+lSLE99WxaMG1hstq/cuxawG9aGbOWs5MHok0oJ8++jPb97+sJuQryY2lsvX1hiXQMT0aMVMNPDY9Im4Kt4+i+k0aGBzp+Hsi9rejVrPp6ljapGCsqVADin/vQBkz6ffK6J7GvohWPIQQQroYPoAIIYRkAh9AhBBCMqFrNaBcPie5l2wg0pTZtvb5CTF71CTKPct+Xg5tO5EUA9Z2OxgfX+mclKV1xZcjR81EwCLfHEdHt/Fv18x7cGx8Ot8f1NW0lT1qQLheXzlvxx7Icx3g8XfOjyeWnVQWXGuQSWXbc8cOtD+j/vjCC9Ze58nHork+OJ8Fy13jcTJln53xdl7WHMtj6H3FZfv6hk17ZGzUtPXcpYnnJkwfWvPoMabSeKEP5wXhPC29LrwmfOc5je1NEXS0oeENdmHnty6+dDYeJ61Ru/ZA8fO9vCVqliUah75nqQERQgjpavgAIoQQkgldG4JrLDYkFyy92i7O62qLfufpNODrZkmFFTDEgKGNhYXIXsStGuhPy06D71XWn6LtDwv6wpruuuIdrxPDXdaW13ThMdWv8OhGjtYjvlBs0PJbFul1BRCi8qXQI3hcMISlry8cf6Nh7Wq0pRQeFyfkk1s+9XU50qSr+0JauG+Y1uxjcMjaBY2dOMe0N206t/15ctKmXafBcRz3hJOSrJG03U4uZ68Rt8qpdrtfeaXSUqkas+TL64531Z+dsW1dXbXWtGnvuB39u5d0PfkqOtuQc2dTFvgGRAghJBP4ACKEEJIJfAARQgjJhK7VgBbnFqVVX4ovzs9HFRV1bHM5dNw3qdInpvrqkguYpqnTrrGdVJU1CDov3eCLY6epoOjoUNhOkW7pjsmT6p7CrsbVRSJ9r7d30PRhunEQYFw+2p8W6BW+ypLJadg+zQSnAMSnNSfpE75UXt8xTtLvvNebc53Gp/UjWE1VjzGHelFCGRQzBtDkHK3Jo0thurE3ZdsZI7YjXaTV8l9PWptJqrZrKxH7baywDIdO20YNy1eqBX8zUX/U5x1/E9PaT6WFb0CEEEIygQ8gQgghmcAHECGEkEzoWg2o1WhKXpbittpWwpTkXYY0VhIYF/bZ7+hyuCIirWZ8+Vl/3N2vMayqdLaK5XpLCotbQkKTRm9J1HxSlDfQsWos+Vws2XlZTTskyef1HAl7jH1xeexDXcqMP8HOJQyxredL+fUWTZKFlJ3DlXS9e+aRedaL63bnE+HcMF1SvHPNR0QkCNQcLlyvM7dKa0Co+cSXXHCsd7BsRR6OeV5vJ966RkSk1YrXXn16kc9GbGnMdrv69wn3Ha9NOzfJjgFLOWg9G39fizAGnC9ldE7xaMMx8A2IEEJIJvABRAghJBO6NwTXCiSfX3p11KmZrqswhrA8aZCwrOMSa1Jh7bI+CyAMGyRZ82hcW4+VWe+IpLP88Tl4lyDc1ahD+EiFERJDQJ4QnetwHYW/ZmZPmb4hsHNx3Zmjc5lsQxQfWnLGqI5pDg4vnitvCm5C6NVLitCYi+/8pKvO2ylJ6cWIvreSzp3GTZ0uxLbdkJs/TGhCfSX/sjpK1WrY2HCjYUO6+hJxCromjLFYVNVUB4a9302DtnZqQAiuDi76ebTPUjtfoBs2IYSQ0wU+gAghhGQCH0CEEEIyoWs1oHp9sR1HNOm5KWLEGIfE1EUsuZBTMdcGxD4xPVFrA1h10mdZjlUoxZMOnRZf6qNTIRXGrHGqXcKYayptE+PwvoqiOD5UQfR30T4EU0dLpYppryZ93YcZc0ptwy4cn07stFOsN0lv8R0XnSb+8wS1DZ+VjZuaXFz281I7Pg07l6J8BJIvoNYUb42EyyI6ZRtxzyWMo6jLHdj19PfbqrmFQlTiAjXdE8cPmvbs3GS0DSdF27adSqyB+p1Uv5md3o98AyKEEJIJfAARQgjJBD6ACCGEZELXakBrAWoOGAutVHpjv5tkvaNjnL7yC2lJE8N3NAhTusFv86HxWZiIiAwMjJj2rJqjg9oYzi/yWRbhvCytF2H5CF0CfWnM9m8no8PBet1S2fHlDJzj7+lz523Ez0tJ0jbSlEj3jx/31bsqL0a/W8V8r6Tj5FvWKbGgxuHO+4kvx4DrcY5xPn6MqOsUS/HrwvuuULRjbNb1nEb/PDLfNaLnvS39BzTL0Zwc1Evxd+/48Rfbn2emT5i+eThXrjYe3d96Hh81IEIIIV0NH0CEEEIygQ8gQgghmdC1GlAul2vHPPX8HNR1vPHyHGpAEAstV01bx7wbdRvrdEs3x9ude+cBrYIkryifNpBGS8J5P319do6B3veFhVm7LoHjFmqr+s61sRZoQDXQ5LB0sM/by3+c/Db9vj631He8TyFqWr51uzpOvD7hLLuKEunumNS68frxjMmn8YgsMx/MV/YcS5crLSfpGtCaUOIcmxQ6IaK786D5FMBHrlyNxlxftPpoq2mvEWe+kd5mwjEO1bXZO9Bn+kqlLXZM6nfwGPxG5iePmvbCvNViaxI3R5MaECGEkC6GDyBCCCGZ0LUhuCAI2iGO0FOVEtFhA7TmcNKuYV06pRhtyZcb38ukCblhJdKksJR9/cc03/i/H3ylDnC9TngiwapeW/VUKraKrC9cgWE1/3GC8cN3MU2+VIrCCEkhIE1SWDPNGN1zq/oE0+Ix5KNDWP7whSnHgCHphBIFK7XtTwoj+8KaSaUP9D3gnI8C2mcp+/+834rHV44hzXHI5f3fzRXiw4+Yhm3CdTDeRi1+qoczpoT9Mb8rENqr9Now20hhvP0Zq0Ljb+aJE9bGR2bUeVf7HoaBY+OzHHwDIoQQkgl8ABFCCMmEVA+gVqsld955p2zdulV6enrkvPPOkz/5kz9xXAHuuusu2bhxo/T09MiOHTtk3759az5wQgghpzepNKA/+7M/k49//OPymc98Ri688EJ5/PHH5d3vfrcMDQ3J+9//fhER+fM//3P52Mc+Jp/5zGdk69atcuedd8p1110nTz31lFSr1YQtRLRajXZcE2PrPnQMtlq16YdlSLvG1FKdUoyaiVMKPIi3kHd0HW0xs4ZlA3wp6YGn3AIum2SV4tOpMEbs279Cggakx4yaiVsi3aZ7a10E0+196bpo34LHIo29iK8chrMsxvDFo8l59KKkkgQ+expcr6+Mchq7KRxDmtRwLAWC952+h4tgrYX76t2kJ8XZ/a4/hVvrPAWw7cl5yjOUcFkYE5b3DlqqPDweUiwZIfo8+9PeK8ayyNpu4W8m/qYeO3ag/Xl6+ng01iCQabD1WY5UD6Dvfve78va3v12uv/56ERE555xz5B/+4R/kscceE5GlHbv33nvlj//4j+Xtb3+7iIj83d/9nYyNjcmXvvQlede73uWss1arSa0W/ZhMT0+nGRIhhJDTlFQhuKuvvlp2794tTz/9tIiI/PCHP5SHH35Y3vrWt4qIyPPPPy8TExOyY8eO9neGhobkqquukkceeWTZde7atUuGhoba/zZv3rzSfSGEEHIakeoN6MMf/rBMT0/Ltm3bpFAoSKvVknvuuUduuukmERGZmJgQEZGxsTHzvbGxsXYfcscdd8jtt9/ebk9PT/MhRAghrwFSPYC+8IUvyGc/+1n53Oc+JxdeeKE88cQTcuutt8qmTZvk5ptvXtEAKpWKVCoV5//NnJxG9NkXpxYRKRaieD/GKzG+jKUE6qrUtDvHA9pKo8A4tRtz9Q555eTitRrUUNDSBPtNnxPvj98/nIuBxzjNegOlZzSbqMFBPNzR3aJ19fevM339/cOxY8LS3zMzJ03b6oJYet1f8kLrLUGA1wTMy9JWNqFfkysYOxp7vF29K14DQnzXfNJ95yu9jqB+p0t24PibIGUW1P3tlr+IbyeXzkDLH48NjlOiOx/b57dR8o+pBetqNuJ1Z5xv5FuvM8euEa+x9xf7TbtUOdu0tUaktddWqykTE8/FrvdlUj2APvjBD8qHP/zhtpZz8cUXywsvvCC7du2Sm2++WcbHlyY0HTlyRDZu3Nj+3pEjR+QNb3hDmk0RQgh5lZNKA5qfn1/mr7FC+2m8detWGR8fl927d7f7p6en5dFHH5Xt27evwXAJIYS8Wkj1BvTrv/7rcs8998iWLVvkwgsvlH/8x3+Uv/zLv5T3vOc9IrL0SnnrrbfKn/7pn8r555/fTsPetGmT3HDDDakG1mq1JJdbev/WoTIMgyDlSvRKiLYSmHaqQ24i/tTlxFRrD2nCXb6UScc+BMIiaFej0aGLpe/GL4vHAUOVJoUb/iDBqrOmWiQcsyakuvusUxpQWDUn8ePHMM7g4GjssouLNs0Ury8d2ms2oPprCodrJAziw2oIhtlSheAwhdvYpcB16QkzJ5FmegE6KutQOe6Pr0JqzrGmwhT0VdjteEJl3vU444WUdLUdHF8B9sex8SlEN0HQxOqpdhzGvQyic61WvHVYER4J+DtXhvM8ekYU6dJTMhqNujz11HckiVQPoL/+67+WO++8U973vvfJ0aNHZdOmTfLv/t2/k7vuuqu9zIc+9CGZm5uT9773vTI5OSlvfvOb5cEHH0w1B4gQQsirn1QPoIGBAbn33nvl3nvvjV0ml8vJ3XffLXffffdqx0YIIeRVDL3gCCGEZEIXl2NoSfBSnFzH5THWjDHvaiU+nozpn1jNsxXEb8dNA27FLutrJ2lHmJqs8VmNpEVb5jtW7qAtYekDHbNPstrPq+204Bj67HacCpWQ2ov6kdb3cLyY0q3Hj5Hhnp4B0/al5kvLE3gHcgW/HYougeHa68S3i6DtoSaHuqcFrkXHHigeX9VW9/q328FSJ3p/cOoE3ndG90kqseCrKJpgr+MtV+L5Lmo+hWK8Boe6UxjY4+Zx8ZGg4NekzTnA9XrSyFt5+/vj2AHB/ukrc2gksvHBitJx8A2IEEJIJvABRAghJBP4ACKEEJIJXasB5fOFtr5gShBD/BWt90vKGgKXdbUBG4vWcdOWYwUTH3N15+7El93GeLg/Ru/fd6Sg9ZiE9eq4b1LZZm+JBYwJg16h9aRCAHqFU/pA62qdz7NCUOtbBK2vr28oGh9amsCx0PuHegtqM07JDo/2VyrGn0tfGWoR/zWRdC51O5+Lt016aeGO1oMk6ac4f6e3d7D9uX/IWr/UFsEuazE6t0k6js92yC1DHz8HB215fMfCN59oaV2e+xmGi8dRfzfAuTxwfbXUPCGvPgTg3CTcH6TVUvqRKv2d9wlYensdLUUIIYSsMXwAEUIIyYSuDcEN9I+0bV3m5qba/4+hGUzb1CEIDMWg8zGuS7+qYiqykx5tqpwKLLvy8BFiXu+dypLxqbCOs24r3tE6KeXZSQPOe6xgIEyl7WrQ5sYNwak0+MAfqsRQkz4/aD8zN2+LHA7W17c/Y0owts024Tg444ewrU47x1Bl4Alh4fHGfdX9rqtz0nd1+r0/hd53jfjA6x+rC+P5WbchSt/tGbT2WTMnrG2Pz7nZhxOqLGAbj6Pad6wym6LqqROS81UixlT9wJPWj1MnwG09r0KIGK7DY6jPV77kfyS4Y4qucVORucMq1nwDIoQQkgl8ABFCCMkEPoAIIYRkQtdqQIOjw1IuL6VYz85FMWKMffb2Dpm27p+H2D9qQm5qdVN9jo+TilgbmSQrHjs+f8VE/JvAVFBMsr1JkXZqbe0x9RJt7dFSPtoOaj5FKMdQ0KntoAGh1uG1XGphNdsGtCPtxi3lYMtuHD32ghqDHT+m6vvsgRDUiHzXSC4XX4YDjwumYWvdzde33LqcshwxY8B2mpIEeK/gdAdt2y8isnnbWdH4UIMAzWFxXlkjtVAnTFN+wV8RVes6qONgqrKd0uAfg9HVML0bxwCXmzkfcIzzaAulgbIOTay0WtfaK9j2JGhC+tjoNOxCyDRsQgghXQwfQIQQQjKBDyBCCCGZ0LUaUO9AT7u8dm/vQOxyGAPXJRaw3IKr68TrPM5cnjDeziJNOWLXOsX+DYDb9c3V8NmwODYeTjw5v+znpPWK2GOOukcBS0aodaFehOUYzFwe2GYDymGjrqDneCWdj3lVEhqtnHwl0t25F/75XkZbc2xi4nU2vKZLpSq0ozGXy7YP27h/et5cT79ddmDE3mflnui7Or4vIjI/ZbWyueno+C/M2fl2qL2uHx8z7ddd8c+i787Y7y7MLpj29Mno3NUW7Hp995ZvPo6IWzbB6jr+eT/GXifFfKnEchLO4lon7HxemWs7FF82HOcLBs2VzbsqBH69+mX4BkQIISQT+AAihBCSCXwAEUIIyYSu1YBCCdvx+Go1mjeAcfd63caIFxZmYvtwHopPEwqxL4UG5CslkFRW2xfHTqM1uduN1xx8ugeOYamtveCsXuGWEojfX7TlL6h19Q5YT7DZKavnoa+fKSvsnGf0Jov6k0qKh2o+g1u6Af9+g5Lj6ru+Mg8iVufBuTpOyQs11we9EAeG1pn2uRdvNe0Lr7mw/fn1Z282faMDthRCRZ2PetPu2+S8Pf77T5xof37mp/9k+vY9/rRpb75gi2n/4gXb2p9fOH7cbufolGmfmjjV/oweZ9jWWodTcjthvo7GN+8H+wvFzrQPEXFMJJPuwzT4vlssx5dFQa2vUbPz7ZwDGUPQogZECCGki+EDiBBCSCZ0bQiuvtAQeananraDwHAEplpr+x0MxbjlFzxp2J6QG7ZxvWlepZOW9X3XV/3SXU98qnVS2jWmVutzgOGiEljBlMpRKm8eUl3RZkWnAY+fu9H0nTh0wrQnj06att6/AENwHmt4PP4lsBLypYZj+AtTnnt7o5DW2NZx07fpPLt/o5uiEhFo0fLDb//ItA/s3R+t92y73mtuuNq0r33DJaY9NhRZV7khxM7ZDMft9Wee2f68eMEFpu/AL19p2pWSvWbOGomstsoQst1/ng3JTfzTRLSdOWuxhOEiEzpLuq+c0ggqtTqpuqf3vvN/daU49kCreJfQYyyWUoQQJf53hOUYCCGEdDV8ABFCCMkEPoAIIYRkQtdqQEEQtDUZHaev1639xuzsKdOu15Vdu6fk9nLtsMO45XLr9i/rs+VHWwxMkdSWOQJ9EG+W+Ji3ky7tST/GMWBJYm2pg9Yvjp6kdJ9Kj9VIimD1vuHsDe3P2960zfQdf9FqAc8+8SysKxpjsxFfflzE7p++XkRc2yedcl7psfuKKc5bLznX9m+NdJF/ttFqPuv6rH5UUdpHANfIxo1nmPaehyNNaMd1203fFefaMRQL8ec5n6bEQsJ1q7fTD9t8Hey7b7v9VXuMN43atPJ9Q9Fxmz5uy61gCrHRcZxrIKEcg8f2JsnWx0cujS6VgjRp5UigtFg8Lklp5bZ0uZoykqMGRAghpIvhA4gQQkgm8AFECCEkE7pWA8rlcu2YqC4Zi2W20ZJFPHoLzvtBux2f1Y3zXW/Z7RQ2HzmMRXdefsG73qSy2rn4+Dja5/jm9qC2hKWy9Vwf1HwqvVYT2nB2ZNN/zQWgAZ1lzzuO+ej+o+3POB/EKe/9T9E4ZidnTF9Pny0X3b8umsvjzBmq2uNS7rHtgZ7ITgjnvvjmexVBj7jyPKvrvOHss9uf9bweEZGSR/NZDavRi9KMqQLX3mDVWjL1DUfnI+8pi4A4Gg/qOJ6y2075gmK8JuSUQfGMKa3mY9adIEH7tuvToVst/4qdfdfzgFQph1bQ2aOFb0CEEEIygQ8gQgghmdC1IbhSpSill1xbtaXO3OykWQ4r+Okqmz6rHVxWZOVVThG0vbF9aPlhT4ETKsBSmr7tqjCbN0VbMM0UxwBt6K9UojBVUhQhr17ZMURVhhDWyHhkybJpeNj0nTFg06NPXWRDr2dsjlKV8RjWF2311J7+KKyDqbzFMoSA1g+2P2M67uHnD5v2HFQJPXbgWPvz0+fYKqBbN1kLnWGVlr2u14YBx+FYaAudNKGxnxerGVOSE7tO5cfrpwHnWd/COCScWoDbEU9asxuuiw9nu1/2WGBB2CwEqyoT6gs6/31Kmr4Bvd51uc7g0XHU4w8Ca4cVu76OliKEEELWGD6ACCGEZAIfQIQQQjKhazUgnYbdakVptWjzjanUWi9ydJwEa540JRZwrL62Jint2tFqTOVSf4qk3q6Tzu0pz+DalGBV0xK0o9h7Eypl4nZLlei7RUjhROv3/oFI+8BSAWgpc8769ab9C2ORxjLUY1N3W3DufjIe6UWHT1orp7lJq+MU1BgxbRyP26FnDpr2xHORRtSsQ3o6cM7GaPw9kLLtpPZ6ri9Mge5GjcgHnnds61IVeD5qC9amC6157Hriq5qK4L3U+f3tVl4FXSeFtuy1/FnFq0O68jDOl21TW/Go3HA8ZnHwDYgQQkgm8AFECCEkE/gAIoQQkgldqwHpcgz5nMo1z1ktwDeXxym/kKDr6HlDSbFaW4rW/xzX8wQKoKcUPNY7uJ2kvxf8Jbl9ulS8vcZSG63qo3azaePuWMqhrDSgPFq7o/bkKRGNfethXlBPuaQ+W20AGTr//PbnGmhYs4u2PMNzRyOLn5NzVh/acsFm016ct9+dOjbV/jxzypaOn4L5R3Mjw+3PPWU7v8VXUgE53TQfBMdf9VgYoYaIGqNeNu28PnMPwJiSSqp0uizqULgevx7jP89Wz7Z9eJt59a40Y/Qcszj4BkQIISQT+AAihBCSCV0bgqsvNERaS8/HpkrDboHFg067FhEJdBq2Jzwn4lr1pEGnG6NFi7tsdJgd25uC/xT4wmreZRMtNdTfHmgJkpAq3lKO12iFhDY+GsdNumzDK32VKHSWtK9ldOxWxzEpDKVDXBjuGoCKnEPKFudH+/ebvsWGtX4563VnxW6zvmCXnZ20IbkT05Er90IdLWUghnKah9nSgKHX2JCPuOGivAnH2/UmhZq063NSqMwLhtE8tj1pLHOSHX90+DFhjB2uJ6kd5qINYfp5HHwDIoQQkgl8ABFCCMkEPoAIIYRkQtdqQHNTs1IvLWkNi4tR+itW3PSlWrtWOwllBBVJsU+t+yTpLVr3cUodpLDqSIo9G60mMZW6c20Jx1SrL+hO01cuWw1Fd7caVi+q9tl06dH+fokjKY3Wl8KdBjwWWpd63bgtoTC9sGDassE2a/NRivrxg8dj+0REZlWa9vFZqw9tGR017bXa19OBQiobHJ+dDqy35L8PtUbklEjxjSHBgsbed/hdGGQK8caX7p1kp7MakUgfGz0Gp7xF3PdXvGVCCCFkFfABRAghJBP4ACKEEJIJXasBzcycaNv+z81Ntv+/0bCxc3duj9aA0s3z8Vl3OGUTtH0NaksJ1jyaJFsPvya08r8ffOXHcf6ULochIlKrRfuL85pwXc1GNC8rX4e5FhCH9x1/VO/mavY60JYtq9FIfHOIRkCj2nrGGab94wMHTHtgNLILwlIBOC9oYTbSk06BBtSE67gidv7U6Y4uIZFoJaT7k7QLreN4NFCRZcpso21Uh0NKmqBjrcLgNwauckdr8uy7V8ddxTENsKQF/j4V1NyfFVgf8Q2IEEJIJvABRAghJBP4ACKEEJIJXasBzc6ealv7z81Otv/f1YBsjNKUVEADqBQlCrDPyWv3zClKM28A5yolbtezrK/Mtve7qAGhjgNzr/L5eG0tAK8+rX3g+Qhatr3YiC9bjX0nZmdMW5fhRp+4tQK1pbGhIdP+2eHDpq33r2+oz64L/MVCtewC7GuwCi+v0406lMeYr9VjlhTXw9Bzrzj3JGo+hfjvJukZ+rtJ8weN9gonNnTuWY/OkzSXR88Dgn3F7cZ9b1lQI1LXbSGFbvYyfAMihBCSCXwAEUIIyYSuDcHNz8+0U3wXa8qKB0JwWA4gTeXDpMqfpg/sdnSqci6hoqj5HqaGO2OAiq/h8mmO2Cdi7UOKUHnVe1xgvRgWFLEhoTCIP0712mJsX6vRY9rz0/OmjdVINcdnbAXRAydOmvaW0fWx332l0DY9IiIjfTbMdvjFqJpquceWfUAatfjw42sJN9RqU9K1nROG1QpFuC6bnj7EE1pCMFxXSPF3fJrqqVj1NOcpReH8jqjfAgy5JdkF2dV2Xg02jb3Xy/ANiBBCSCbwAUQIISQTui4E9/Irnq50qjPb0N3AccPW7ZSVJH3dQQ5CfSYEl2IzOf+Y3CHHv9bmcvFhgjSvzm50DrPi7N8pgfq7Bb/abNqsJVsB0q6ntmjdpOdUuGV62obcZmZs1ts8hGbM8p5surUEM7bmYEwL81GIsdi0odX6gh1jU4XgvPsmImHdkxl2GqKdEDAMi8d0UR1TvH5qEP4NmtH9kW+l/FvbcxPnCxiyjn5G0dE6lXt3gvO3qeKa9Fu2RiE4DAO6d/zyruEvn5skSSQXphFNfg68+OKLsnnz5qyHQQghZJUcOHBAzjorvlR91z2AgiCQQ4cOSRiGsmXLFjlw4IAMDg5mPayuZXp6WjZv3szjlACPU2fwOHUGj5OfMAxlZmZGNm3a5J2f1XUhuHw+L2eddVY77DA4OMgT3AE8Tp3B49QZPE6dweMUzxBM1F4OJiEQQgjJBD6ACCGEZELXPoAqlYr8h//wH6QCk/2IhcepM3icOoPHqTN4nNaGrktCIIQQ8tqga9+ACCGEvLrhA4gQQkgm8AFECCEkE/gAIoQQkgl8ABFCCMmErn0A3XfffXLOOedItVqVq666Sh577LGsh5QZu3btkiuuuEIGBgZkw4YNcsMNN8jevXvNMouLi7Jz504ZHR2V/v5+ufHGG+XIkSMZjbg7+OhHPyq5XE5uvfXW9v/xOC1x8OBB+e3f/m0ZHR2Vnp4eufjii+Xxxx9v94dhKHfddZds3LhRenp6ZMeOHbJv374MR/zzp9VqyZ133ilbt26Vnp4eOe+88+RP/uRPbFltHqfVEXYhn//858NyuRz+l//yX8Kf/OQn4b/9t/82HB4eDo8cOZL10DLhuuuuC++///7wySefDJ944onwX/7Lfxlu2bIlnJ2dbS/ze7/3e+HmzZvD3bt3h48//nj4pje9Kbz66qszHHW2PPbYY+E555wTXnLJJeEHPvCB9v/zOIXhyZMnw7PPPjv8nd/5nfDRRx8Nn3vuufDrX/96+Mwzz7SX+ehHPxoODQ2FX/rSl8If/vCH4W/8xm+EW7duDRcWFjIc+c+Xe+65JxwdHQ2/+tWvhs8//3z4wAMPhP39/eF/+k//qb0Mj9Pq6MoH0JVXXhnu3Lmz3W61WuGmTZvCXbt2ZTiq7uHo0aOhiIQPPfRQGIZhODk5GZZKpfCBBx5oL/PTn/40FJHwkUceyWqYmTEzMxOef/754Te+8Y3wl3/5l9sPIB6nJf7wD/8wfPOb3xzbHwRBOD4+Hv7FX/xF+/8mJyfDSqUS/sM//MPPY4hdwfXXXx++5z3vMf/3jne8I7zpppvCMORxWgu6LgRXr9dlz549smPHjvb/5fN52bFjhzzyyCMZjqx7mJqaEhGRkZERERHZs2ePNBoNc8y2bdsmW7ZseU0es507d8r1119vjocIj9PLfOUrX5HLL79c3vnOd8qGDRvk0ksvlU996lPt/ueff14mJibMcRoaGpKrrrrqNXWcrr76atm9e7c8/fTTIiLywx/+UB5++GF561vfKiI8TmtB17lhHz9+XFqtloyNjZn/Hxsbk5/97GcZjap7CIJAbr31VrnmmmvkoosuEhGRiYkJKZfLMjw8bJYdGxuTiYmJDEaZHZ///OflBz/4gXz/+993+niclnjuuefk4x//uNx+++3yR3/0R/L9739f3v/+90u5XJabb765fSyWuwdfS8fpwx/+sExPT8u2bdukUChIq9WSe+65R2666SYRER6nNaDrHkDEz86dO+XJJ5+Uhx9+OOuhdB0HDhyQD3zgA/KNb3xDqtVq1sPpWoIgkMsvv1w+8pGPiIjIpZdeKk8++aR84hOfkJtvvjnj0XUPX/jCF+Szn/2sfO5zn5MLL7xQnnjiCbn11ltl06ZNPE5rRNeF4NavXy+FQsHJTDpy5IiMj49nNKru4JZbbpGvfvWr8q1vfctUGRwfH5d6vS6Tk5Nm+dfaMduzZ48cPXpU3vjGN0qxWJRisSgPPfSQfOxjH5NisShjY2M8TiKyceNGef3rX2/+74ILLpD9+/eLiLSPxWv9HvzgBz8oH/7wh+Vd73qXXHzxxfKv//W/lttuu0127dolIjxOa0HXPYDK5bJcdtllsnv37vb/BUEgu3fvlu3bt2c4suwIw1BuueUW+eIXvyjf/OY3ZevWrab/sssuk1KpZI7Z3r17Zf/+/a+pY3bttdfKj3/8Y3niiSfa/y6//HK56aab2p95nESuueYaJ43/6aeflrPPPltERLZu3Srj4+PmOE1PT8ujjz76mjpO8/PzTjXPQqEgQRCICI/TmpB1FsRyfP7znw8rlUr4t3/7t+FTTz0Vvve97w2Hh4fDiYmJrIeWCb//+78fDg0Nhd/+9rfDw4cPt//Nz8+3l/m93/u9cMuWLeE3v/nN8PHHHw+3b98ebt++PcNRdwc6Cy4MeZzCcClFvVgshvfcc0+4b9++8LOf/WzY29sb/v3f/317mY9+9KPh8PBw+OUvfzn80Y9+FL797W9/zaUX33zzzeGZZ57ZTsP+7//9v4fr168PP/ShD7WX4XFaHV35AArDMPzrv/7rcMuWLWG5XA6vvPLK8Hvf+17WQ8oMEVn23/33399eZmFhIXzf+94Xrlu3Luzt7Q3/1b/6V+Hhw4ezG3SXgA8gHqcl/sf/+B/hRRddFFYqlXDbtm3hJz/5SdMfBEF45513hmNjY2GlUgmvvfbacO/evRmNNhump6fDD3zgA+GWLVvCarUannvuueG///f/PqzVau1leJxWB+sBEUIIyYSu04AIIYS8NuADiBBCSCbwAUQIISQT+AAihBCSCXwAEUIIyQQ+gAghhGQCH0CEEEIygQ8gQgghmcAHECGEkEzgA4gQQkgm8AFECCEkE/5/v0wY0GRcCI4AAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img, defective = train_dataset[20]\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.imshow(img[2], cmap=\"bone\")\n",
    "\n",
    "defective"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-02T04:50:43.161333Z",
     "start_time": "2024-08-02T04:50:42.977714Z"
    }
   },
   "id": "84f74c8c0e01c49f"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "triplet_dataset = SiameseDataset(train_dataset)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-02T04:50:48.586865Z",
     "start_time": "2024-08-02T04:50:43.161026Z"
    }
   },
   "id": "3d896aa216943714"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "tensor(1.)"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img1, img2, label = triplet_dataset[90]\n",
    "\n",
    "_, (ax1, ax2) = plt.subplots(1, 2)\n",
    "ax1.imshow(img1[2])\n",
    "ax2.imshow(img2[2])\n",
    "label"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-02T04:51:39.069640Z",
     "start_time": "2024-08-02T04:51:38.938005Z"
    }
   },
   "id": "c5b4c22695966885"
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "ClassifierNetwork(\n  (resnet): 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  )\n  (criterion): MSELoss()\n)"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightning as pl\n",
    "\n",
    "\n",
    "class ClassifierNetwork(pl.LightningModule):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self.resnet = torchvision.models.resnet18(weights=torchvision.models.ResNet18_Weights.DEFAULT)\n",
    "        self.criterion = torch.nn.MSELoss()\n",
    "\n",
    "        self.resnet.fc = torch.nn.Linear(self.resnet.fc.in_features, 1)\n",
    "        torch.nn.init.xavier_uniform_(self.resnet.fc.weight)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.resnet(x)\n",
    "\n",
    "    def _step(self, batch, _, step: str):\n",
    "        x, y = batch\n",
    "        y_hat = self.forward(x).squeeze()\n",
    "\n",
    "        loss = self.criterion(y_hat, y)\n",
    "        acc = 100 * (torch.round(y_hat) == y).float().mean()\n",
    "        self.log(f\"{step}_loss\", loss)\n",
    "        self.log(f\"{step}_acc\", acc)\n",
    "        return loss\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        return self._step(batch, batch_idx, \"train\")\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        self._step(batch, batch_idx, \"valid\")\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        self._step(batch, batch_idx, \"test\")\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        lr = 1e-5\n",
    "        weight_decay = 5e-4\n",
    "        \n",
    "        params_1x = [param for name, param in self.resnet.named_parameters() if \"fc\" not in str(name)]\n",
    "        optimizer = torch.optim.Adam([{\"params\": params_1x}, {\"params\": self.resnet.fc.parameters(), \"lr\": lr*10}], lr=lr, weight_decay=weight_decay)\n",
    "        return optimizer\n",
    "        \n",
    "\n",
    "ClassifierNetwork()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-03T04:09:09.856577Z",
     "start_time": "2024-08-03T04:09:09.668922Z"
    }
   },
   "id": "246e2c95ae3a4f8c"
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "SiameseNetwork(\n  (resnet): 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=2, bias=True)\n  )\n  (criterion): BCELoss()\n)"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchvision.models.resnet import ResNet18_Weights\n",
    "import lightning as pl\n",
    "\n",
    "\n",
    "class SiameseNetwork(pl.LightningModule):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self.resnet = torchvision.models.resnet18(weights=ResNet18_Weights.DEFAULT)\n",
    "\n",
    "        # remove the last layer of resnet18 (linear layer which is before avgpool layer)\n",
    "        fc_in_features = self.resnet.fc.in_features\n",
    "        self.resnet = torch.nn.Sequential(*(list(self.resnet.children())[:-1]))\n",
    "\n",
    "        # add linear layers to compare between the features of the two images\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(fc_in_features * 2, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "        self.fc.apply(self.init_weights)\n",
    "        \n",
    "        self.criterion = nn.BCELoss()\n",
    "\n",
    "    @staticmethod\n",
    "    def init_weights(m):\n",
    "        if isinstance(m, nn.Linear):\n",
    "            torch.nn.init.xavier_uniform_(m.weight)\n",
    "            m.bias.data.fill_(0.01)\n",
    "\n",
    "    def forward_once(self, x):\n",
    "        output = self.resnet(x)\n",
    "        output = output.view(output.size()[0], -1)\n",
    "        return output\n",
    "\n",
    "    def forward(self, input1, input2):\n",
    "        output1 = self.forward_once(input1)\n",
    "        output2 = self.forward_once(input2)\n",
    "\n",
    "        output = torch.cat((output1, output2), 1)\n",
    "        output = self.fc(output)\n",
    "        return output\n",
    "\n",
    "    def _step(self, batch, _, step: str):\n",
    "        x1, x2, y = batch\n",
    "        y_hat = self.forward(x1, x2).squeeze()\n",
    "        \n",
    "        loss = self.criterion(y_hat, y)\n",
    "        acc = 100 * (torch.round(y_hat) == y).float().mean()\n",
    "        self.log(f\"{step}_loss\", loss)\n",
    "        self.log(f\"{step}_acc\", acc)\n",
    "        return loss\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        return self._step(batch, batch_idx, \"train\")\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        self._step(batch, batch_idx, \"valid\")\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        self._step(batch, batch_idx, \"test\")\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam([{\"params\": self.resnet.parameters(), \"lr\": 1e-7},\n",
    "                                      {'params': self.fc.parameters()}],\n",
    "                                     lr=1e-6)\n",
    "        return optimizer\n",
    "    \n",
    "\n",
    "SiameseNetwork()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-02T05:11:01.818008Z",
     "start_time": "2024-08-02T05:11:01.642810Z"
    }
   },
   "id": "2653ecb916830bcb"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001B[34m\u001B[1mwandb\u001B[0m: Currently logged in as: \u001B[33mbhavye-mathur\u001B[0m. Use \u001B[1m`wandb login --relogin`\u001B[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb\n",
    "from lightning.pytorch.loggers import WandbLogger\n",
    "\n",
    "wandb.login()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-02T04:54:35.156645Z",
     "start_time": "2024-08-02T04:54:32.954761Z"
    }
   },
   "id": "9c18b5d8d1d73f33"
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1670f9e6829a4376bb9fc77bbb597c40"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": " View run <strong style=\"color:#cdcd00\">frosty-dew-15</strong> at: <a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning/runs/tm703zdf' target=\"_blank\">https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning/runs/tm703zdf</a><br/> View project at: <a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning' target=\"_blank\">https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Find logs at: <code>./wandb/run-20240725_134309-tm703zdf/logs</code>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require(\"core\")`! See https://wandb.me/wandb-core for more information."
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wandb.finish()\n",
    "\n",
    "model = SiameseNetwork()\n",
    "wandb_logger = WandbLogger(project=\"soap-side-contrastive-learning\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-25T08:13:50.804272Z",
     "start_time": "2024-07-25T08:13:42.872496Z"
    }
   },
   "id": "e73df0aeb54c8d7e"
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n",
      "/Users/bhavyemathur/Desktop/Programming/Python/AIvolved/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/configuration_validator.py:70: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n"
     ]
    },
    {
     "data": {
      "text/plain": "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.011187065277610802, max=1.0…",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0d785792f9f14183ade40ccf2bd126ef"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Tracking run with wandb version 0.17.5"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Run data is saved locally in <code>./wandb/run-20240725_134350-3inxv7zr</code>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Syncing run <strong><a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning/runs/3inxv7zr' target=\"_blank\">polished-waterfall-16</a></strong> to <a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": " View project at <a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning' target=\"_blank\">https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning</a>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": " View run at <a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning/runs/3inxv7zr' target=\"_blank\">https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning/runs/3inxv7zr</a>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name      | Type       | Params | Mode \n",
      "-------------------------------------------------\n",
      "0 | resnet    | Sequential | 11.2 M | train\n",
      "1 | fc        | Sequential | 295 K  | train\n",
      "2 | criterion | BCELoss    | 0      | train\n",
      "-------------------------------------------------\n",
      "11.5 M    Trainable params\n",
      "0         Non-trainable params\n",
      "11.5 M    Total params\n",
      "45.888    Total estimated model params size (MB)\n",
      "/Users/bhavyemathur/Desktop/Programming/Python/AIvolved/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.\n"
     ]
    },
    {
     "data": {
      "text/plain": "Training: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "26ba839605a74f1c8a578ba455f279ed"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=10` reached.\n"
     ]
    }
   ],
   "source": [
    "train_dataloader = DataLoader(triplet_dataset, batch_size=256, num_workers=1, pin_memory=True, persistent_workers=True)\n",
    "\n",
    "trainer = pl.Trainer(max_epochs=10, log_every_n_steps=3, logger=wandb_logger)\n",
    "trainer.fit(model=model, train_dataloaders=train_dataloader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-25T08:15:53.376769Z",
     "start_time": "2024-07-25T08:13:50.805379Z"
    }
   },
   "id": "a90267263db801bd"
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Tracking run with wandb version 0.17.5"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Run data is saved locally in <code>./wandb/run-20240803_094651-axx62pql</code>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Syncing run <strong><a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning/runs/axx62pql' target=\"_blank\">clean-energy-19</a></strong> to <a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": " View project at <a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning' target=\"_blank\">https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning</a>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": " View run at <a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning/runs/axx62pql' target=\"_blank\">https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning/runs/axx62pql</a>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name      | Type    | Params | Mode \n",
      "----------------------------------------------\n",
      "0 | resnet    | ResNet  | 11.2 M | train\n",
      "1 | criterion | MSELoss | 0      | train\n",
      "----------------------------------------------\n",
      "11.2 M    Trainable params\n",
      "0         Non-trainable params\n",
      "11.2 M    Total params\n",
      "44.708    Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "text/plain": "Sanity Checking: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c882b839ec0a4b9ba0aeed11f5a0541e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bhavyemathur/Desktop/Programming/Python/AIvolved/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.\n",
      "/Users/bhavyemathur/Desktop/Programming/Python/AIvolved/venv/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.\n"
     ]
    },
    {
     "data": {
      "text/plain": "Training: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9833fee9bf2c4471b0300c31bb306175"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e15a2780b8f446b3898147cb6318bdb4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b1259470ab584483b012fbbe767fa2b5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "8434530cf20041d89eb0eb6e3e09e316"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c75d472104494d57aa023aa8b2c1b2a9"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "eccbfe0922e942d9a7cd1d41ff2ef303"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "37911833a695403fae11ead7cc277898"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "8850b0fc861540c19de911f39797343c"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a0a2e50c3e644a56bc0380a40aecdd39"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "65af67671feb41d59a4eb6aab1001cac"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "de443e15174844afab80335ff6bcc9fa"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "858ae89974ff49838a67ca7f050e0de4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "91fdd06b877847a9b42b50bea30a56d4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a681d735a30945cf961f4158ff3589d3"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "684f0c7b67c744be9ba1cee8163db8d1"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3a26cd612640445f94e02789b301654f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7d76ab0c4a304cc4a9025deb955e020b"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "870b7aa302ee44c4a560bf1d338a6e57"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a8e026a116c2420c95b2833b0305d6d7"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "905f7cc489a748189d8e6a3d283926cf"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c58d187e5acd48b199dfaad54e601290"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "67d3b62ee9354c9b950a1892f623c491"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "68a25e31147240adbe0693b072b7e0e4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ed464179662248faa61c177447d934f0"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c93d4d8ff6ce4c7890275e633f4ec2bf"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "4e8f98d794884dc4b780188da4f2bd4d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "4a7c6584f7ed42d1877d9f918b74e9f8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1cf92c4ef46b4d03be6c6127cf5b2946"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "58b2d021ccd042d19b594e3c8b102255"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "eb81d3e430144e949707ad2d6379824d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9cb9095f56904b37bc26d61c995e6e73"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "025e7d373c154e91ab4a9a754ce6fa47"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ec7958d7a50d444e8ba99650b3ab492b"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9eb2d24f74c54f62af8b34141102822e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c88f914ad79a4ef09c2e443d4079d138"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "88d20300db034c55a2e314e8b6580074"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "bc1fbc64d90c41c9830a71eca3b9cf34"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a209c9f59b374fee9ad617d99c98df2f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "5ca6a46daebd457395c23cfe2fbd0bc4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d1f6601ce5f54acba4e91148f8c7a850"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "59b86e481efb4981ae235fd551044980"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "223de026712f4d1892a13758e0d9854a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3aedbdf6279148d3a1f0b2b20ebea260"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f2c273c198b64f61b3fb362787cbb311"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e3991873cc844c59aa93d0dbffea7470"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "40876d48127a48d2adf4c98f0bece2f9"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "75bac4cff82e4a8dbc9701bf142d303d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0b49b4c1c8154095b9092a03d36de74a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7a77a4038b1842fb8a1fae9e0fc4dfe5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a2fcf55e31354946bde7d6322dade300"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d6705fadf41340f484b7f0f555f196eb"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b827ff0bdf9b4cd7801c8c4f06d20f9d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "22d124f09fa046038e8853d5b2a1f12e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "4ff3612ce83f424490f8762a3c64a6e2"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d40a82b0b31542a3b38bc2437f9d985d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9d66889834e04ac29efb32f144e2c1a5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "55b2721b03c64ed1b6ecfce7255e2e91"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b3842225a73f400089a9c4a91e70c587"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "940a3c875b2249429a40dee7918f8bee"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "95e582521ba7467f88e66ad83d716c90"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0a938dbafaa443f39c48dd5519fc6178"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "736e435a455d433b95e9fa26c3348752"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "70261822213b4faf9dc2b1352cc59612"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2bf733b5b2cd489688274965afa8c19e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "fb3dde80d74745769fc58c7e95c56997"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d34a3b10b4c748fdaec7a4e27cf4455d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "880bc9f9091f4228a69a652cfa0bedb5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "8ccc5cc81d634a55ac8885b0baf749b8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0bc9d47f06ea4db78781eafe378440cd"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "be488248eca54f94a05316e6f726a0bb"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a5af620a25bd4f579159c700a08f360a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "51941a58495c49bcb343a03385c2c75d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ae4ec1bc9ba64637aae9fef4ac108457"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0e3c178c1d1144a4902076131401473a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "20394d6e0acc41beacadfbc07c1147e4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "74618c322f06467583f5f899183f57db"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a36218ec6b2d43a78e115c6fb8dd5837"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "6de631c882964671b5050ccb0ddbef52"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "5df7c523e3fb48049c6acb6b29ac62c5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7dda480793d948e88a6714e86c38fa47"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "77e7c846c9d54ad488d4a0b4f1524585"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9764c2836f994ff7b65e25adf7c3ce74"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "316591f074aa44068414fc6f4699a82b"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "cd65cdecd8d043c9b5c0e8f787c2ab1a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e770b1bbfb634488b55c12f8d67696d7"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "81f5ae7deded42b7b50879ecf46fe0fa"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3b1798481d564f339293bf4196e05619"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "83923c82c1e24704bcdb0357ce4a1f46"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3542b56bddb04dc88133b17d531cd5b6"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c8b262a4a0444440a92ac705ced37ae4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b527180e12814b739002dbf20290c46e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a1834721bc4d40a2999f2e8d99466b05"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "fe7b1af91f474296b11407f18387db9f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ffe269a07ca249a0aa917f93bf4067b7"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c3e10c3cc8f445aba5bdd6766c1d9982"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "57b7b3177873477fae58a9021078049d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "60fe1341d30f4031b1f325a457b22bb6"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f1437ec3f5ca46238dde329bf45c3ae0"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a21df0d36dd743ca99711c2fcab718e6"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f3cfdd393629430da488e29a4e1dc80c"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Validation: |          | 0/? [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f0b662e166bf449e83aac7a0fa21e0e0"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=100` reached.\n"
     ]
    },
    {
     "data": {
      "text/plain": "VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2facc40262aa4867bf90cdb14bbe4afc"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<style>\n    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n    </style>\n<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>train_acc</td><td>▁▆▇█████████████████████████████████████</td></tr><tr><td>train_loss</td><td>█▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>trainer/global_step</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>valid_acc</td><td>▁▂▅▆█▇██████████████████████████████████</td></tr><tr><td>valid_loss</td><td>██▄▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>epoch</td><td>99</td></tr><tr><td>train_acc</td><td>100.0</td></tr><tr><td>train_loss</td><td>0.0</td></tr><tr><td>trainer/global_step</td><td>299</td></tr><tr><td>valid_acc</td><td>48.48485</td></tr><tr><td>valid_loss</td><td>0.51897</td></tr></table><br/></div></div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": " View run <strong style=\"color:#cdcd00\">clean-energy-19</strong> at: <a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning/runs/axx62pql' target=\"_blank\">https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning/runs/axx62pql</a><br/> View project at: <a href='https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning' target=\"_blank\">https://wandb.ai/bhavye-mathur/soap-side-contrastive-learning</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "Find logs at: <code>./wandb/run-20240803_094651-axx62pql/logs</code>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require(\"core\")`! See https://wandb.me/wandb-core for more information."
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ClassifierNetwork()\n",
    "wandb_logger = WandbLogger(project=\"soap-side-contrastive-learning\")\n",
    "\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=256, num_workers=1, pin_memory=True, persistent_workers=True)\n",
    "valid_dataloader = DataLoader(valid_dataset, batch_size=len(valid_dataset), num_workers=1, pin_memory=True, persistent_workers=True)\n",
    "\n",
    "trainer = pl.Trainer(max_epochs=100, log_every_n_steps=3, logger=wandb_logger)\n",
    "trainer.fit(model=model, train_dataloaders=train_dataloader, val_dataloaders=valid_dataloader)\n",
    "wandb.finish()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-03T04:28:18.196603Z",
     "start_time": "2024-08-03T04:16:51.699049Z"
    }
   },
   "id": "9af8203ebdcc2ab5"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}