main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision.transforms as transforms\n",
    "from utils import *\n",
    "from models import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device='cpu'\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "test_transform = transforms.Compose([transforms.ToTensor(),\n",
    "                transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                     std=[0.229, 0.224, 0.225])\n",
    "])\n",
    "invertTransform = transforms.Compose([\n",
    "                                transforms.Normalize(mean = [ 0., 0., 0. ],\n",
    "                                                     std = [ 1/0.229, 1/0.224, 1/0.225 ]),\n",
    "                                transforms.Normalize(mean = [ -0.485, -0.456, -0.406 ],\n",
    "                                                     std = [ 1., 1., 1. ]),\n",
    "                               ])\n",
    "# test_transform = transforms.Compose([transforms.ToTensor()\n",
    "# ])\n",
    "\n",
    "test_data = torchvision.datasets.CIFAR10('data/', download=True, train=False, transform=test_transform)\n",
    "test_dataloader = torch.utils.data.DataLoader(test_data,\n",
    "                                          batch_size=4096, # just for test accuracy\n",
    "                                          shuffle=True)\n",
    "\n",
    "cifar_labels = {0:'airplane',1:'automobile',2:'bird',3:'cat',4:'deer',5:'dog',6:'frog',7:'horse',8:'ship',9:'truck'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model = ResNet('18').to(device)\n",
    "model.load_state_dict(torch.load('models/resnet18_l2eps=70/110')) # 20 omg\n",
    "model.eval()\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_acc = accuracy(model, test_dataloader, device)\n",
    "# fgsm_acc = fgsm_accuracy(model, test_dataloader, device)\n",
    "# pgd_acc = pgd_accuracy(model, test_dataloader, device, eps=10, steps=3, alpha=0.1)\n",
    "# print('Normal Test Accuray:', test_acc)\n",
    "# print('FGSM Test Accuracy:', fgsm_acc)\n",
    "# print('PGD Test Accuracy:', pgd_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "it = iter(test_dataloader)\n",
    "i, l = it.next()\n",
    "images, labels = i[0:2].to(device), l[0:2].view(2).to(device)\n",
    "image, label = images[0], labels[0].view(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this cell exists because of the most annoying bug ever:\n",
    "# the loss for pgd updates needed to not be averaged so that each input\n",
    "# was worked on independently of the batch size\n",
    "\n",
    "# display_im(images[0].cpu())\n",
    "# print(cifar_labels[labels[0].item()])\n",
    "# print(cifar_labels[torch.argmax(model(images[0].view(-1,3,32,32))).item()])\n",
    "# print(torch.min(images[0]), torch.max(images[0]))\n",
    "# adv = pgd(images.view(1000,3,32,32), labels, model, stepsize=2.5 * 100 / 7, eps=100, steps=7, constraint='l_2')[0]\n",
    "# print(torch.min(adv), torch.max(adv))\n",
    "# # adv = fgsm(image.view(1, 3, 32, 32), label, model, eps=.2)\n",
    "# # print(adv.shape)\n",
    "# print(torch.min(image - adv), torch.max(image - adv))\n",
    "# print(cifar_labels[torch.argmax(model(adv.view(1, 3,32,32))).item()])\n",
    "# display_im(adv.view(3,32,32).cpu())\n",
    "# display_im((image - adv).view(3, 32, 32).cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "horse\n",
      "bird\n",
      "tensor(-2.1179) tensor(2.6400)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-2.1480) tensor(2.6894)\n",
      "tensor(-0.2854) tensor(0.2767)\n",
      "bird\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_im(image.cpu())\n",
    "print(cifar_labels[label.item()])\n",
    "print(cifar_labels[torch.argmax(model(image.view(-1,3,32,32))).item()])\n",
    "print(torch.min(image), torch.max(image))\n",
    "adv = pgd(image.view(1,3,32,32), label, model, stepsize=2.5 * 50 / 7, eps=50, steps=7, constraint='l_2')\n",
    "# adv = pgd(image.view(1,3,32,32), label, model, stepsize=.015, eps=.6, steps=40, constraint='l_inf')\n",
    "# adv = pgd(image.view(1,3,32,32), label, model, stepsize=0.1, eps=.5, steps=7, constraint='l_inf')\n",
    "print(torch.min(adv), torch.max(adv))\n",
    "# adv = fgsm(image.view(1, 3, 32, 32), label, model, eps=.2)\n",
    "# adv = pgd_attack(image.view(3,32,32), 5, model, stepsize=1, eps=60, steps=80, constraint='l_2')\n",
    "# print(torch.max(image - adv))\n",
    "# print(adv.shape)\n",
    "print(torch.min(image - adv), torch.max(image - adv))\n",
    "print(cifar_labels[torch.argmax(model(adv)).item()])\n",
    "display_im(adv.view(3,32,32).cpu())\n",
    "display_im((image - adv).view(3, 32, 32).cpu())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sketch Completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZUA0fo9MT8AzwXJN1AHfReTv4Hp13OlcCHwYeBX5W/T5lQnX8AHgW2EonbCsaqOMzdN6SbgW2VD9faHqf9Kij0X0CfJLOIK5b6fxj+cac1+yPgeeBfwCWHs7z+g06qRB+g04qhGGXCmHYpUIYdqkQhl0qhGGXCmHYpUIYdqkQ/wcw8xe0eQB6MAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    }
   ],
   "source": [
    "import imageio\n",
    "from torchvision.utils import save_image\n",
    "model = ResNet('18').to(device)\n",
    "model.load_state_dict(torch.load('models/resnet18_l2eps=70/65'))\n",
    "# model.load_state_dict(torch.load('model'))\n",
    "\n",
    "model.eval()\n",
    "image = torch.Tensor(imageio.imread('jupyter_testing/sketch3.png')).permute(2, 0, 1).to(device) / 255.\n",
    "# print(image.shape)\n",
    "plt.imshow(image.permute(1, 2, 0).cpu())\n",
    "plt.show()\n",
    "label = torch.Tensor([6]).to(device).long()\n",
    "# adv = pgd(image.view(1,3,32,32), label, model, stepsize=1, eps=60, steps=80, constraint='l_2')\n",
    "# adv = pgd(image.view(1,3,32,32), label, model, stepsize=.015, eps=.6, steps=40, constraint='l_inf')\n",
    "# adv = pgd(image.view(1,3,32,32), label, model, stepsize=.03, eps=.1, steps=5, constraint='l_inf')\n",
    "eps=5000\n",
    "steps=23\n",
    "# adv = fgsm(image.view(1, 3, 32, 32), label, model, eps=0.2)\n",
    "adv = pgd_attack(image.view(3,32,32), 1, model, stepsize=2.5 * eps / steps, eps=eps, steps=steps, constraint='l_2')\n",
    "plt.imshow(adv.cpu().view(3, 32, 32).permute(1, 2, 0))\n",
    "plt.show()\n",
    "print(cifar_labels[torch.argmax(model(adv.view(1, 3, 32, 32))).item()])\n",
    "upsample = transforms.Compose([transforms.ToPILImage(), transforms.Resize(480, interpolation=2), transforms.ToTensor()])\n",
    "\n",
    "torchvision.utils.save_image(upsample(adv.cpu()), 'saved.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ship\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "15\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "25\n"
     ]
    },
    {
     "data": {
      "image/png": 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/fqbr+DjA1xZFSWe4dNhTzJf0HDjIS7qj1biHlw67e8KLes6axqfflVSEz4el+PVbV3aRSCjZRSKhZBeJhJJdJBJKdpFI5PVufFNXM76y7tZgzPfz1anLisNrxpWU8vXi0M7XOnt5A5+UsPWtzfyY5eGFy57pfob3KeGh0dr8Ah9/T1H4LnjrQT5BoqOZ342vmHkajaWr+KSQ/lfCE5FqenlVIN3Nt5oC0qMK3ffyE8H2mx/+T9pn1uB8Gnt88fdobEbCzgkHe/jrsag4fM2dXMtW8gPmnxlek6/0Cf7a0JVdJBJKdpFIKNlFIqFkF4mEkl0kEkp2kUgMW3ozszkAfgBgBoAhAKvc/XYzuwXADQAO1VhucvdwnSMrnUpjYsWUYKx9/Q7ar3NruLYyYTFfK2xCa7gEBQDtG/iacUNFCWvXdZAJEuXdtEv9v3yKxm7/+I00dmzZSTS2Y/2rNDY4/Zhge8/sStqnvIuX5d5/ybU0VtbGS5+vtrwWbP/ySz+gfaom8MkuN5/9eRrb3tFIYyvu+LtwYAtfg66p9Vkau+OiK2nspiv5pKdB/hJB38FwWa4vYd294y//ZLB9wjfupH1GUmcfAPBFd19nZhMBrDWzp7Ox29z9P0ZwDBEpsJHs9dYEoCn7uNPMNgPgl1QROSod0Xt2M5sP4FQAz2WbbjSzDWZ2j5lNHuexicg4GnGym1klgIcBfMHdOwB8G8AxAJYic+X/Fum3wswazKxhKOG9oYjk1oiS3cyKkUn0H7n7TwHA3ZvdfdDdhwDcBWBZqK+7r3L3enevT5XzRe9FJLeGTXYzMwB3A9js7rce1l532KddBWDT+A9PRMbLSO7GLwdwLYCNZrY+23YTgGvMbCkAB7AdwGeHO9CCqXNx62duD8Y+eXAl7dextjnYXtXPV2qbWcOnIE2fzr/s/jSflfVGd7hckyrls8bWrvk1jX26l5cbX//yszR2/oxaGnurfW+wfWY7317rvA/x8mDdAhrCdzb/ksZ+3vBQsL28hK8zN7tmJo09sPMXNLaxaQuN1ZWEj5k6djrts3siL7/esfE+GvvaFbz0NqV0Eo21tLQG29v38RKxGSt7Ou0zkrvxvwUQKjAn1tRF5Oiiv6ATiYSSXSQSSnaRSCjZRSKhZBeJRF4XnJxUWoYPL1gSjN3+1XBJDgCeeDRcvup4k08lat2yi8aKLbwlEABYMf8rvyVLzgi2T5/LT2NTW7hsCAAnnDmPxpCw29Hn/zH4x4oAgJ/cEV5IcVH9xbSPnX46ja18ls9S27WbzxzbvC38dZeneGnz4EEeKyOLMgJAYzcvffb0hL+fqYMJi5UO8P2k+jr5wqj/9dRqGlt64cdorHltuEy57PIP0D6TyPZP6bS2fxKJnpJdJBJKdpFIKNlFIqFkF4mEkl0kEnktvQF8Ts62pt20z6mLg1Pl0T+Zl2o2d75IY+XlvTTWXcLLeeniCcH2shL+MzPdxxdDPK2Dz/LCAT6OZQ/dQGMvNG0IB37DZ40du46XAI+p5LEd23jpbX9bePxF4KWrxm5e9qyayBcQLZ3CZ5QtmvO+YHu68wDts2diG42lq2to7Mctj9DY7//+DhpD1V8Hmwd6+Gvg568+HWzf383Pr67sIpFQsotEQskuEgklu0gklOwikVCyi0TC3PkCdeOtYv50X/xPfxkODvFlphdMOS7YPqOMb0zT/kYTjfUP8NJK80E+g6q7OzxTqi/VTvs07uSLIZZaD42VVw3R2KstfCbdYG9JsH3CwmraZ/IgX4yyyvneH69tfpnGvCO8fxl6EpYTN/41Y0IZDVXP4yXM6pLwHnd1FXzvO0uYYVdzangvPQCYmbCI5b+f8yUaYyP579+FZ8MBwA1rbg629965DUO7uoObEurKLhIJJbtIJJTsIpFQsotEQskuEolh78ab2QQAawCUIjNx5iF3v9nMFgC4H8AUAOsAXOvuCQt7AUXzJnv1youCsYoifre1dIistzXA15KrTKf5QFJ8jbG2Tn6nfnAw3G8gxc9hbx+5Kw2gB5005gmTIKZW80kh8PA5qeSnF6UJlZDO/fxctezh21dZafCGMLyPX19Ky8ITjQDAjX8/U+UJd9YHwnf4y8ldegAoKUs4WRN4xaBiEh//1JoqGtu9JzxZ6uWdvNpRRCoGA7dthe8c/d34XgAXufsSZLZnvszMzgLwDQC3ufsiAG0Arh/BsUSkQIZNds84NB+wOPvPAVwE4FAh8F4AfFc7ESm4ke7Pns7u4NoC4GkAbwBod/dDE8p3AeB/4SIiBTeiZHf3QXdfCmA2gGUATgh9Wqivma0wswYza/ADfNEIEcmtI7ob7+7tAJ4FcBaAajM7tNLNbADBuwzuvsrd69293ir5DTURya1hk93Mas2sOvu4DMAlADYD+BWAQ9tcXAfg0VwNUkTGbiRr0NUBuNfM0sj8cHjQ3X9mZq8AuN/MvgbgRQB3D3egVMowoTL886UkoVJW5OFyx1BPQllriJdIBp2vXTdQzI/pReFjDpDxAUBfPz9ecRn/WdufUBFtP8gn3qQQngiTTvPyWu20GTQ2o45PhDn7ghNpbOqMqcH2mim1tM/0yXU0tgjzaeyZA+tprGVXuKz1+pZttE9PPy837mrhE6z6DvDvdZ/xWAdZN65iIp+gVFYeLvO1F/GUHjbZ3X0DgFMD7duQef8uIu8B+gs6kUgo2UUioWQXiYSSXSQSSnaRSOR1DToz2wPg0J5BNQD25u3JOY3jnTSOd3qvjWOeuwfrm3lN9nc8sVmDu9cX5Mk1Do0jwnHo13iRSCjZRSJRyGRfVcDnPpzG8U4axzv9yYyjYO/ZRSS/9Gu8SCQKkuxmdpmZbTGzrWa2shBjyI5ju5ltNLP1ZtaQx+e9x8xazGzTYW1TzOxpM3s9+z+fbpbbcdxiZruz52S9mV2Rh3HMMbNfmdlmM3vZzD6fbc/rOUkYR17PiZlNMLPnzeyl7Di+km1fYGbPZc/HA2YWnuLIuHte/wFII7Os1UIAJQBeAnBivseRHct2ADUFeN7zAJwGYNNhbd8EsDL7eCWAbxRoHLcA+FKez0cdgNOyjycCeA3Aifk+JwnjyOs5AWAAKrOPiwE8h8yCMQ8CuDrb/h0Af3skxy3ElX0ZgK3uvs0zS0/fD+DKAoyjYNx9DYB972q+EpmFO4E8LeBJxpF37t7k7uuyjzuRWRxlFvJ8ThLGkVeeMe6LvBYi2WcB2HnYx4VcrNIBPGVma81sRYHGcMh0d28CMi86ANMKOJYbzWxD9tf8nL+dOJyZzUdm/YTnUMBz8q5xAHk+J7lY5LUQyR5awL5QJYHl7n4agMsBfM7MzivQOI4m3wZwDDJ7BDQB+Fa+ntjMKgE8DOAL7h5evqUw48j7OfExLPLKFCLZdwGYc9jHdLHKXHP3xuz/LQAeQWFX3mk2szoAyP7PN4rPIXdvzr7QhgDchTydEzMrRibBfuTuP8025/2chMZRqHOSfe4jXuSVKUSyvwBgUfbOYgmAqwE8lu9BmFmFmU089BjABwBsSu6VU48hs3AnUMAFPA8lV9ZVyMM5MTNDZg3Dze5+62GhvJ4TNo58n5OcLfKarzuM77rbeAUydzrfAPDlAo1hITKVgJcAvJzPcQC4D5lfB/uR+U3negBTAawG8Hr2/ykFGscPAWwEsAGZZKvLwzjOQeZX0g0A1mf/XZHvc5IwjryeEwCnILOI6wZkfrD882Gv2ecBbAXwEwClR3Jc/QWdSCT0F3QikVCyi0RCyS4SCSW7SCSU7CKRULKLRELJLhIJJbtIJP4PwF+nlfWbipgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "30\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "35\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "40\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "45\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "50\n"
     ]
    },
    {
     "data": {
      "image/png": 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hZBeJREEXnCwpK0Ud2canv4iXePb3hLcFKn2Hb7tUur2Lxu5o5tsFJWnrCc9ge+vAHtqnf4SXQs6/mJeuqk7zct6zj4cX7QSAU73hGWDF/QnvXuxNiJ3g57F1+xs0Nu+jVwXbr/rkatqnjI8CbT3hmY8AkPRWrZqKcC21soqXZndt3Edj5TPDC2kCQONMPrPtR2vX0VjtjPAWYYsXXcCPtSRcti0r42PQlV0kEkp2kUgo2UUioWQXiYSSXSQSSnaRSBS09DZSWoTj88J7fT1Y8TLvWLc12PzqrvW0y0M//ysa+7fmH9DY/7rkdhr7zNN/Gmz/3ZWfpH0+Nf8yGisb4IseXlBRRWPPlS+msfXPhGcPLl30CdpnzpzwfmgA0DSHl5r2bj1AYxtawmXR1/fyGXurrmumsdG+cPkVAIYTLlmzyqcG23f/63ba57kfPEFjlbPvorGqmXzPvM3v8J/10qHwTLpljbw8WH1eOI+Ki/nJ0JVdJBJKdpFIKNlFIqFkF4mEkl0kEgW9G9+dOoZHup8Oxj5+3YW03wVfuCHYfnqEb9Pzbz95kcb2/PwZGrtw/SM01tEfXj9ve00d7fMnC/jeGYcO8PXYplbX0xj6T9HQ1jePBNs3vson61y8lI+/sp5vBzClbjmNpfaHKyjVVXzyD8KnFwBgPfxu9oxpvHLxr72vB9v/4jVerTlYxO+Cf702PGllLKVTwlUBADhGJiIdP9FO+8z7yH8KH6eKr4SnK7tIJJTsIpFQsotEQskuEgklu0gklOwikRiz9GZmCwA8BmAOgFEAa9z9QTO7H8AdAM4sUnavu7+Q9FipoRQ6DnUEY69v2kz77RkJbyX0mU9+iva5pp6Xk/b8+5s01rqDb7tUsThcdtn6T7+kfZZ08E1zvvGp36exPzg/vIYbADQt4ZNr9neFy3J7dvOJH61b+2isYRVfM66TbMsFALOXh9d++8pjt9A+eHSEhlacXkhjdbN5mXLDnufDgfDTMI1XRPHETc/R2BfKf4fGTh4je4cB2N++P9jeOLeY9pl/ySXB9rLK8AQZYHx19hSAu919s5lNA/C6mW3IxL7t7v99HI8hIpNsPHu9tQNoz3zeZ2a7ASS8M0JEzkXv6292M2sAcCmAjZmmu8xsm5k9bGYzcjw2EcmhcSe7mU0F8CyAr7r7CQDfA7AUwAqkr/zfIv1Wm1mLmbUM9/GtcEUkv8aV7GZWinSiP+nuzwGAu3e4+4i7jwL4PoCVob7uvsbdm929uXRa+EabiOTfmMluZgbgIQC73f2Bs9rPvgV6E4AduR+eiOTKeO7GXwHgSwC2m9mWTNu9AG41sxUAHMABAF8Z64FmTanFf2kOf9kXX7iV9ut4tjvY/pMe/mfBjU18Ft1XVv8xjX3O+bZLu955J9ju0/lpPNL6Fo1981W+Ft6XG79MY9jNtycqOhberqlvSj/ts+Dym2ns8MBhGntqxkYa++jSxmD7jav506Q0YYun/ds30dimfbxs2/CxS4PtFc6P9eauAzT22Pq/obHbL/gtGps7wLcqG0G49Gknw9uNAQAr5PGV+sZ3N/5VAKE5f4k1dRE5t+gddCKRULKLRELJLhIJJbtIJJTsIpEo6IKTNRXn4QvLrwvG/ulLf0T7PdERLnd0vByeLQQA/3stjz26/g0aK6njWyvNbVgSbP/Epz9K+5w/dxGNDfa10VhpwrqGf/znD9DYQ3/7tWD7rf/5D2mfk1fymXn37PoujaWKeDlv/bqXgu19h/nMtoaq8Ew5ADjVy2eNlczgs95ODoWP1z/KZ4fhPP7O7+IKPv5X3l5HY5d99Foam9cSnpn3X/+Cl/nYmeLz5HRlF4mGkl0kEkp2kUgo2UUioWQXiYSSXSQSBS29Jbmw+SIa+84TTwbbu7cdoH3WPriGxjpH+Nygo90naaz15N5g+0+7+CKVlyzjCyV+bdWdNJbkR7/Bx//y/dcH233VNNpnVhUvAd7RzEt2/cfDM+wA4I3XfhFsn1fbRPucP4WPsaKS7782NKWUxmZNDe99NjjMZ0y2HeAzFY+dDs98BICBmUM09t0rH6Kxo/3hUmrVQl7me/aN8MKXPaf4hnm6sotEQskuEgklu0gklOwikVCyi0RCyS4SCXP3gh1s9sXz/YtPh2e3VVZNpf2uaQgvGngdLs9qHOHd0NL2IUVj2wfDJbafbvhH2qctYV+5kVF+rN9Y9R9obP1r22is83hnsH3ZxXwTH+/nFdji4nDpCgBKUrwEWEqeVzbI52UVdfAS4PTpZTQ2YxGf9XZeabhkV1pRQfsMnzxGY/0pvnDk9IW1NHZb9U001nB6drB97b6naJ/f+9l9wfbB77Zi9PBg8JvWlV0kEkp2kUgo2UUioWQXiYSSXSQSY06EMbMKAK8AKM98/TPufp+ZLQawFkANgM0AvuTufCYAgFPDg3ijfU8wNnN6He03Wh2e6LClopX2mVLK77YmTYLoHeTViYFU+Hdj0Vx+GrfsaKex47u209i/vLObxjB7AY+Vh++QD7Xx9eLqyvhd5O5DR2isryfhMWvCi+gN9vLzUdTLjwXwp9aUo/Np7ER3eGJIdW34DjgALKifRWPlZfxn3d/Nt8r6WekuGus4Gp5Q1HGKn4/RSjJpqCih2kEj/98ggGvd/cNIb898g5ldDuCbAL7t7o0AegDcPo7HEpFJMmaye9qZeZ+lmX8O4FoAz2TaHwXw2byMUERyYrz7sxdndnDtBLABwD4Ax939zLtCWgHwd22IyKQbV7K7+4i7rwAwH8BKAB8KfVmor5mtNrMWM2sZPs63yRWR/Hpfd+Pd/TiAnwG4HMB0Mztzt2I+gOB7Hd19jbs3u3tz6XR+00xE8mvMZDezOjObnvm8EsB1AHYDeBnA5zJfdhuAH+drkCIyceNZg64ewKNmVoz0L4en3f0nZrYLwFoz+2sAbwDgi2xlOIBB8vvlRMKkigOkfNJdzP8smFpaTmOjxtf2Onq8j8ZSxeEtg4oq+RpoH75iJY3tmDudj+MQLysCfJ2xKZXhMdZU8kk3V1y6jMaGLuLbYdVOn0lj58+6MNjeDF42XIrzaGw6+KvCHeATVzpwNNi+F7ysNZQwVeqnG/gWT0VlvOw1NMKfcyMkC6dM58+PsqqqYHt3Cd/2bMxkd/dtAH5l2pm770f673cR+QDQO+hEIqFkF4mEkl0kEkp2kUgo2UUiUdA16MysC8CZ/XNqAXQX7OCcxvFuGse7fdDGscjdg1NIC5rs7zqwWYu7N0/KwTUOjSPCcehlvEgklOwikZjMZOd7KheWxvFuGse7/dqMY9L+ZheRwtLLeJFITEqym9kNZvaWme01s3smYwyZcRwws+1mtsXMWgp43IfNrNPMdpzVVmNmG8zs7czHGZM0jvvN7HDmnGwxsxsLMI4FZvayme02s51m9keZ9oKek4RxFPScmFmFmf3SzLZmxvFXmfbFZrYxcz6eMjO+J1aIuxf0H4BipJe1WgKgDMBWAMsLPY7MWA4AqJ2E414F4DIAO85q+zsA92Q+vwfANydpHPcD+FqBz0c9gMsyn08DsAfA8kKfk4RxFPScADAAUzOflwLYiPSCMU8DuCXT/vcA/uD9PO5kXNlXAtjr7vs9vfT0WgCrJmEck8bdXwF+ZRL2KqQX7gQKtIAnGUfBuXu7u2/OfN6H9OIo81Dgc5IwjoLytJwv8joZyT4PwKGz/j+Zi1U6gBfN7HUzWz1JYzhjtru3A+knHQC+eHn+3WVm2zIv8/P+58TZzKwB6fUTNmISz8l7xgEU+JzkY5HXyUj20Hayk1USuMLdLwPwaQB3mtlVkzSOc8n3ACxFeo+AdgDfKtSBzWwqgGcBfNXdTxTquOMYR8HPiU9gkVdmMpK9FXjX2kR0scp8c/e2zMdOAM9jclfe6TCzegDIfAxvtJ5n7t6ReaKNAvg+CnROzKwU6QR70t2fyzQX/JyExjFZ5yRz7Pe9yCszGcm+CUBj5s5iGYBbAPCFvfLEzKaY2bQznwO4HsCO5F55tQ7phTuBSVzA80xyZdyEApwTMzOk1zDc7e4PnBUq6Dlh4yj0OcnbIq+FusP4nruNNyJ9p3MfgD+bpDEsQboSsBXAzkKOA8APkX45OIz0K53bAcwE8BKAtzMfayZpHI8D2A5gG9LJVl+AcVyJ9EvSbQC2ZP7dWOhzkjCOgp4TAE1IL+K6DelfLH951nP2lwD2AvgRgPL387h6B51IJPQOOpFIKNlFIqFkF4mEkl0kEkp2kUgo2UUioWQXiYSSXSQS/w+wuhB4HUARswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "55\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "60\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "65\n"
     ]
    },
    {
     "data": {
      "image/png": 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ymXnF7wiXDveHJw4CAIYmrPU5KGHBxul8nUeUHAu3D0+Y9ZbPJ0wiYS1KNHceprEhZ5fyjoXhBUs7WN0QwPmzw7XDIUMT9tLjIxCRPyZKdpFIKNlFIqFkF4mEkl0kElm9Gt8Oxy6EL482gH/of6SH19W6cjZfe2zaVH5pd2/LThq7sf7vaazz9fBEmE+X/g3tc9tfvU5jRUi4/NzNZ3489cN7aGzatHOD7RMn8ArEk0+spLEtTXwcW4xP8pnREL7sfjhhwtPw8Xy9vjFjwltvAcBVM8JruAHA/SufCbY//PLSYDsAXDOyisbuu/oLNHZsCL+MX1fLKx5jyOSapiMJs38WLAy3f/W7tIvO7CKRULKLRELJLhIJJbtIJJTsIpFQsotEosfSm5lNAvAQgLEAugEscfe7zewuALcCOD5j40vu/nTSfXWhC80I762z/ege2m/dis3B9mvnL6B9LpzCt0h64UU+gaarhJeGcD5Z+205L13Nu+hzNPbilXfT2CVlF9DYM8P4pIqOg+GF3FqNH99Xd/JJSEM/dCuNNW9YRWObJoQn8jzy2ztoH9SPo6Frp32Gxn6/ne8W/qN/vCUcWMOHcf/ER2hseidfS+6Om26gscb1/P/W2BY+VoPKx9I+GFoSbs/j5+/e1Nk7AXzB3VeZWQmAlWb2XDr2HXf/Zi/uQ0RyrDd7vdUBqEvfbjGzjQAmDPTARCSzTuk9u5lVApgDYEW66XYzW2NmD5rZqAyPTUQyqNfJbmbFAB4D8Hl3PwTgXgBTAcxG6sz/LdJvsZlVm1n1wYb9GRiyiPRFr5LdzAYjleg/dvfHAcDd6929y927AdwHYG6or7svcfcqd68aVc43FRCRgdVjspuZAXgAwEZ3//YJ7SdeXlwIYF3mhycimdKbq/GXA7gZwFozW51u+xKAm8xsNgAHsAPAp3q6oyIMwgUIz5S6Z2u4JAcAW34fnqX24Q/wrZpG5lfSWEUx32do8rQZNNY2N7zm2i/38a2VNq7bRGNfvJLPXvsm/pLGyvL5HkSlReG9iwoTFnGb8u5301htFV/L75kyPqPvwsvC2xBdfM11tM908Jlth7CNxp5/7DEaA6tSTuZdEF4SDgDwt/d/lMbu+AwvvZWdxcuDm1aH1/krPW86H0gf9OZq/MsAQr/xxJq6iJxe9Ak6kUgo2UUioWQXiYSSXSQSSnaRSJw22z9dPPNKGruv7cvB9hWvvUn77Fm7hcb27eef5DvWzbfPOdoWLr2NmsjrOJ1tR2hs+Su/pTG8k5fePvXVB2msbeWyYHvJ/E/QPjv4KPB9vEFjt43lm2VtR3hBx511tbTP4XF8xmF7E98q61grLw9iSrgUicl86yps6uax5Ty0u20vjU26ls/QLN8XPiajb/w4f7A+0JldJBJKdpFIKNlFIqFkF4mEkl0kEkp2kUicNqW3chTS2LxPhPe1KkEH7fPcU8/S2KBB/G9cVwcv8extDC98iTY+Y2/sFL5/2aKzrqGxJJ88/CSNbRodXjyya2VwbREAwPYOPjNv6FReDjvSXENjB978r3CAV96wuYLvwYdmUkIDgLV8FiBaK8Lt3Yd5n8a+LbLy2We/RmPLFn6Pxkbf/Ben/FjsMPKM0JldJBpKdpFIKNlFIqFkF4mEkl0kEkp2kUhktfS2z/fj++0PBWM7a3kZ59LLwrPKKsfyBScnTxlPY0eOttNY0XCyhxaAsgPhw7V6M5/Z1tTEZ0L9fCVfKPHFC3n55/FnXqCx7vqmcKAjoTy1+2UaOtjIj1XiqeJc0p5Q8ULDPh5r578XrKnjsYrw3mw2nm9qVPTeKTR2eDrfF2/mpefxcfTBBvDn1cd+/dfB9i2HdtE+OrOLRELJLhIJJbtIJJTsIpFQsotEwtzD2/T87w+YDUFq5a1CpK7e/9zdv2JmZwF4BKkNdlYBuNndEy7dAgVTirz8zqnB2PixY2m/d0yZGGwvLApfaQWALh9OYx3t/P9cs7uRxoYOC1+NP9jMryK/sXYVH0d30tVnvhYeDiesn3awNdw+OmFH7Tw+kQctzTzWmTCOUjJ+IxNTAGBQEQ2dP/dqGvvDZvJ/BnC45ViwvXICv7pfMZ5P/lmx6iUaO+eCc2js7Gn8Cv8fdm0Ptrc636asZstb4cDd2+C7jwQX5evNmf0YgKvcfRZS2zMvMLPLAHwDwHfcfTqAgwBu6cV9iUiO9JjsnnL8T+fg9D8HcBWAn6fblwL44ICMUEQyorf7s+end3DdB+A5ANsANLn78ddxNQD4pxREJOd6lezu3uXuswFMBDAXQGhf4+AbYTNbbGbVZlbd3coXhhCRgXVKV+PdvQnAiwAuAzDSzI5fsZoIsniGuy9x9yp3r8orzu/PWEWkH3pMdjMrN7OR6dtFAN4DYCOAFwB8OP1jiwDwtZJEJOd6MxFmHIClZpaP1B+HR939KTPbAOARM/sqgDcAPNDTHXW5obUzfHbf2xwukQDAkW17gu2FBXyiQFExL+Xl22AaazxUT2NDOsLr5A0bwUtGsy69mMYaWvjkn51bwuWY1EAS3g61Hw23N/EJHOCVJgweP4zGOrr5uoFgOyi1tdAuNoyvM7foimtp7KlR62lsx65wKXVMKX+soQmvQPOL+HOnoYmXbQft5f26LFwKLisbTfu0NZcF2w8N4uv49Zjs7r4GwJxA+3ak3r+LyBlAn6ATiYSSXSQSSnaRSCjZRSKhZBeJRI+z3jL6YGYNAI7XBsoA8FpF9mgcb6dxvN2ZNo4p7l4eCmQ12d/2wGbV7l6VkwfXODSOCMehl/EikVCyi0Qil8m+JIePfSKN4+00jrf7oxlHzt6zi0h26WW8SCRykuxmtsDMNpvZVjO7MxdjSI9jh5mtNbPVZladxcd90Mz2mdm6E9pKzew5M9uS/pqwQuSAjuMuM9uTPiarzey6LIxjkpm9YGYbzWy9mX0u3Z7VY5IwjqweEzMbYmavmdmb6XH8Q7r9LDNbkT4ePzUzPnUvxN2z+g9APlLLWp0NoADAmwDOy/Y40mPZAaAsB497BYCLAKw7oe1fANyZvn0ngG/kaBx3Afhilo/HOAAXpW+XAHgLwHnZPiYJ48jqMQFgAIrTtwcDWIHUgjGPArgx3f4DAJ8+lfvNxZl9LoCt7r7dU0tPPwLg+hyMI2fcfTmAAyc1X4/Uwp1AlhbwJOPIOnevc/dV6dstSC2OMgFZPiYJ48gqT8n4Iq+5SPYJAHaf8H0uF6t0AM+a2UozW5yjMRxX4e51QOpJB2BMDsdyu5mtSb/MH/C3Eycys0qk1k9YgRwek5PGAWT5mAzEIq+5SPbQAva5Kglc7u4XAXgvgM+a2RU5Gsfp5F4AU5HaI6AOwLey9cBmVgzgMQCfd/eEnSuyPo6sHxPvxyKvTC6SvQbAiVu50MUqB5q716a/7gPwBHK78k69mY0DgPTXhO1iBo6716efaN0A7kOWjomZDUYqwX7s7o+nm7N+TELjyNUxST/2KS/yyuQi2V8HMD19ZbEAwI0AlmV7EGY2zMxKjt8GMB/AuuReA2oZUgt3AjlcwPN4cqUtRBaOiZkZUmsYbnT3b58QyuoxYePI9jEZsEVes3WF8aSrjdchdaVzG4C/y9EYzkaqEvAmgPXZHAeAh5F6OdiB1CudWwCMBvA8gC3pr6U5Gsd/AlgLYA1SyTYuC+N4F1IvSdcAWJ3+d122j0nCOLJ6TABciNQirmuQ+sPy5ROes68B2ArgZwAKT+V+9Qk6kUjoE3QikVCyi0RCyS4SCSW7SCSU7CKRULKLRELJLhIJJbtIJP4HsUZ21wj+eFEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "70\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "75\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "80\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "85\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "90\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "95\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "100\n"
     ]
    },
    {
     "data": {
      "image/png": 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kF4mEkl0kEkOm9HZT2300dt/zd2f2YOH1/ZKu/TqPvR1ebPCrP74p2A4AP/3av6Q5GD/9J4zli1jOWMAX06wbFV6Ec8IEPmvsYIKX5WrTXA5eXBPeVw4AmpvDpa13tj5B+/QcCpeTAODAXr7QY90Yvsda9459wfZX3l1D+yDN/nytbdtprL72RBp77vHlvN9JJwfbe3t5Ga16ZHg2YmEh/4bpyi4SCSW7SCSU7CKRULKLRELJLhKJIXM3ftioKh5kcxb4jVHgPL6V0IfPuZDGXvjFr9I8adjPNtxOYz8Fvxvf1he+UwwAtSP42m89betobPSs84Pt5bXhbZAAoPMdvgbdjvf4OmgjRk+isRMPhp+zvI0/n5fxr7mri1+XaoaNorFbd4bv/t/yzDLap66Yb4f1zbM/TWMn1PJ1D3s7+Hp9zRs3BtuLS/jXPH/xZ8J9yvk51JVdJBJKdpFIKNlFIqFkF4mEkl0kEkp2kUgctfRmZhMB/BbAOAAJAEvd/SdmdgOALwI4vFDW9e7+6EAHsvKJ53iQLMVVeg6fEHL6nJk0VuV8kklRwwU01jsjvHntP551Du1z5V1forH6Wj7+r3/8KzRmfOk3bFkXXpdvWAVfp+31x1+gsbEnn0tjPev55I49c8OTU27Zw7d4GjmOrw34bzOvpLGD02po7Jaf/zEceDm87hsANONZGvtmayeNffn6JTRWcrCMH297+Bs6sjq8dRUAjDvlpGB7cTmfxNOfOnsvgGvdfbWZVQJ42cyeTMVudvcf9uM5RCTP+rPXWzOA5tTjdjPbAP42FxEZoo7pb3YzqwdwGoDDaztfbWbrzOw2M+NvYxKRvOt3sptZBYD7AVzj7u8BuAXAVADzkLzy/4j0W2JmjWbW2NLC18EWkezqV7KbWTGSiX6nuz8AAO6+y9373D0B4FYAC0N93X2puze4e0NtLV98X0Sy66jJbmYG4DcANrj7TUe0H7nVyWUA1md+eCKSKf25G38GgM8BeNXM1qbargfwaTObB8ABNAHgNab+DGQX3/4Jh8LN0wvqaZcPVYTX9QKAyRMX0NiCKXNorH1PeCbXSOczuX75yB9o7N2WpTR2cW14VhMAlPfw8kr39vA2Q4mdvNRUX8NLgKjk66CtXMBny1nDCcH2z5b8E+3TsZ+fxzdH8CmOh/bwctjE4nCJqnUun6HWV8RLXm1701zT+MRCzC7j5d7n31kbbO8s5GsDpskWqj9341cAsEBowDV1Eck9vYNOJBJKdpFIKNlFIqFkF4mEkl0kEkNmwckbv/YdGlv5wIvB9jHVfHG99Wv59j6jRobLQgCw23k56cPzwyW7BfOm0z4Yxbc0enTPJhpbMI+/+/jZ5U009vqf7gq21//NFbRPySn8fDyNHTR2BT5PYxt3hWff7d3cTvtsev1tGuvo5As2/nop3x7s3WdWBdvnnPkRfqzegzS2pYmX+Ta/xWPj54UXAgWA0pUrgu3zP8Fn+hXSCKcru0gklOwikVCyi0RCyS4SCSW7SCSU7CKRGDKltxfS7F82Yc7UYHtPbznt89LKN2isZUW4HAMAxePS7NdV9m6wvbs0vLgiABzo5rFzE7z8s+xmPs/oG1t+SmM7LLwoZt/mh2mf0nb+NX9kFl8EsnVH+HwAQFtveF5W1xsdtE/77vDYAeDEcSNpbOe74TIfAKA7XLLbueY12qW1i48Dlbzo9Q93Xk1jf7nwNhr7+xvDpcOy0/ix7lizMti+pzM86xHQlV0kGkp2kUgo2UUioWQXiYSSXSQSSnaRSOS09LajswU3rP1VMPbnp56h/ba1bQ22147mG9OMmzuBxnbu54sXduzgM6827grPUlu1PjxrCQAOOl/ocWMrX0f/UONLNIYZY2iouH5KsL2nYz/tc7B5H42t6OOzzfbv4pvOjZkZXuixbHwV7VNRWUJjTa27aexAAV8Uc8zC+cH2mRP5z0drOz8fvVP4qpILPhI+FgDsOJGGMK4tXGL72XPLaJ9/X/W9cGDfO7SPruwikVCyi0RCyS4SCSW7SCSU7CKRME+zdREAmFkZgOcBlCJ59/4+d/+2mU0GcA+A0QBWA/icu5ONmpIKJlR6ydXhddz8EL+jOqoivCFkJ59TgeIifmc30cWHeWAfv8NcQG5MjyjgvzM7uvmd3S7wNctKwSf5FJaU0RjbPPOEE/ndZ0Oac8W/LSgdwb/u4jHhLZRqi2ton4pSvt3Rvt28ctG1fy+Nde4Pj/FAB9/iqf1QL43t6NpFY9Pn8S3HhlXxKsRbTeEtpTqL+Pp/vYnwGA9872X0vdMe2sGpX1f2gwDOcfe5SG7PfL6ZLQLwfQA3u/s0AG0ArurHc4lInhw12T3p8DW0OPXPAZwD4L5U+x0ALs3KCEUkI/q7P3thagfX3QCeBLAZwD53P/xaYhsA/g4XEcm7fiW7u/e5+zwAEwAsBDAz9Gmhvma2xMwazazRD/QMfKQiMijHdDfe3fcBeBbAIgAjzezw220nAOHdBNx9qbs3uHuDDec3RUQku46a7GZWa2YjU4/LAXwMwAYAzwD4ZOrTrgTwULYGKSKD15+JMHUA7jCzQiR/OSxz90fM7HUA95jZdwGsAfCboz1RQWEBhleFyyu9B3mNp3xEuE9FmtJPXzcvayWK+ZZMRQk+8aOwPFymrCji46gs4Kd4X18pjZUmeDmssHQYjZWUBasuaOvmE2Gqyvixhg/jr8YKwc9jYXd4Dbr2/bycZCP4llezqybS2B+38a2+WvaGx3Ggm5c2Dzr/nnV08VLqyjV8bcOSKr5VWXFpuIxWNY5/X/oS4ckzXWnKwEdNdndfB+C0QPvbSP79LiLHAb2DTiQSSnaRSCjZRSKhZBeJhJJdJBJHnfWW0YOZtQA4vEhWDQC+QFvuaBzvp3G83/E2jhPdPTj1MafJ/r4DmzW6e0NeDq5xaBwRjkMv40UioWQXiUQ+k31pHo99JI3j/TSO9/urGUfe/mYXkdzSy3iRSOQl2c3sfDN708w2mdl1+RhDahxNZvaqma01s8YcHvc2M9ttZuuPaBttZk+a2cbURz4FLLvjuMHMtqfOyVozuzAH45hoZs+Y2QYze83MvpZqz+k5STOOnJ4TMyszs1Vm9kpqHP+Rap9sZitT5+NeM+PT4kLcPaf/ABQiuazVFAAlAF4BMCvX40iNpQlATR6OexaA+QDWH9H2AwDXpR5fB+D7eRrHDQD+Ncfnow7A/NTjSgBvAZiV63OSZhw5PScADEBF6nExgJVILhizDMAVqfZfAvjnY3nefFzZFwLY5O5ve3Lp6XsAXJKHceSNuz8P4IPrH1+C5MKdQI4W8CTjyDl3b3b31anH7UgujjIeOT4nacaRU56U8UVe85Hs4wG8e8T/87lYpQN4wsxeNrMleRrDYWPdvRlI/tAB4Fu1Zt/VZrYu9TI/639OHMnM6pFcP2El8nhOPjAOIMfnJBuLvOYj2UNLqeSrJHCGu88HcAGAr5jZWXkax1ByC4CpSO4R0AzgR7k6sJlVALgfwDXu/l6ujtuPceT8nPggFnll8pHs2wAcucYQXawy29x9R+rjbgAPIr8r7+wyszoASH3kG5JnkbvvSv2gJQDcihydEzMrRjLB7nT3B1LNOT8noXHk65ykjn3Mi7wy+Uj2lwBMS91ZLAFwBYCHcz0IMxtuZpWHHwM4F0B4H57ceBjJhTuBPC7geTi5Ui5DDs6JmRmSaxhucPebjgjl9JywceT6nGRtkddc3WH8wN3GC5G807kZwDfyNIYpSFYCXgHwWi7HAeBuJF8O9iD5SucqANUAngKwMfVxdJ7G8TsArwJYh2Sy1eVgHGci+ZJ0HYC1qX8X5vqcpBlHTs8JgFORXMR1HZK/WL51xM/sKgCbAPw3gNJjeV69g04kEnoHnUgklOwikVCyi0RCyS4SCSW7SCSU7CKRULKLRELJLhKJ/wVJ67PvG98bfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "105\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "110\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "115\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "120\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "125\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "130\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "135\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "140\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "145\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "150\n"
     ]
    },
    {
     "data": {
      "image/png": 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7+Rox0yaThaGMnydd2UUioWQXiYSSXSQSSnaRSCjZRSIxZO7Gt3byiQ4VPeF1y86cPoE/YQHf7mhDPZ9JsnYEH0fZuPBd64YqPnEilfBmUgmpFt3exuefoOhgeNuo5t38GQ/YYR7r4iurTZz9YR4rOBRsH5fPtxcoreDr9U0eFp7QAgAYy0N3P/j7YPtvd/8b7TOv4kwa+1Ah3w7Lc8Pr/wHA1vX8dYVh4bv/E0ucdjnp09cE2wt+8HN+GD4CEXkvUbKLRELJLhIJJbtIJJTsIpFQsotE4qilNzOrAvAAgAkAegAscve7zex2ANfj/6tEt7r7U0d7vnBhCFj7Bi9NbFoZnjwx4xy+rtre5nwa+8kPl9LYofwxNDaxnJVk+GSXi5/4Zxq79cqv09j5KX4OT0tRcdxTF25vWc0nYpRPqaKxcy75Io2t2Poaj+16Pti+4BvX0z7oGE9Dn5l8LY2dW8Enwtz1tS/z4xFr8TMa+9W8i2jsq5/g6+ttWbOVxrq6wmXKyrOm0j4lp80LtucU8m3P+lJn7wLwJXdfbmbFAF4zs2eSsbvc/bt9eA4RGWR92ettB4AdycdtZrYOAP/0g4gMScf0O7uZVQOYC2BZsulmM1tpZveZGX//KyKDrs/JbmZFAB4D8AV3bwVwD4ATAcxB4sr/PdJvoZnVmlltU1OqD4GKSCb1KdnNLA+JRH/Q3R8HAHdvdPdud+8B8GMAwTsG7r7I3Wvcvaa8PMVOECKSUUdNdjMzAPcCWOfu3+/V3ntGwJUAVqd/eCKSLn25G38WgGsArDKzI3sB3QrgajObA8AB1AG4oS8HzCHt+w/ztbNatoTLFqOKr6R9CqfyWwiTxk+nsY5uvh7b9obwOnkN9XydtpXdfF2yH9bcS2PnV/ES1fvLeanv1e3hmVdvPc7Ljad98TYaW7+P/wy/6cVv0piNPhhsL8kbRfs0b+XT+R5r/BaNHZzxARrrlxSXwN0Nv6Oxc6ffT2Ob2/jMwsbd4XXt9ndM4gPph77cjX8JQCgTj1pTF5GhQ5+gE4mEkl0kEkp2kUgo2UUioWQXicSQWXDy8g/yMtrisd8Jtu9t3Uv77NnBP61XPO4kGmvPYcVBwMpKg+0TRvIZdoX17TTW3cEXKExl/oKP09jbK34UbC+rOpX2yT0/PIMKAO6tf4jGxlbzxSNzC8Mvra7y8AwvAGgZxv8/h/fwLZk6WtlcSgAFpD2P/z9jDJ85hmF8S6Yn3vw1jY0/nc+Wa3n5F8H2GZfyLbv6Q1d2kUgo2UUioWQXiYSSXSQSSnaRSCjZRSIxZEpv9S2v0ti0yvBMqZ1b+IysF5byxRD3H+Tln5PnnUdjXQiXhjrb+ay3os5GGvub8VfRWCqL38/LP0vIuoyvTOULev7V7kdpbHwJL0Pl5PLVyfYjXHKc97HLaJ+8eRfT2LgRfEO3ynxeViwdMyvY3tHCy55WTENY/MfFNPY/bb+kseX/8iSN7eq+MzyOOaxuCPzvvuZge0s3L0Pqyi4SCSW7SCSU7CKRULKLRELJLhIJJbtIJLJaetuDVjyA3wRjzz/937Tf7LnhJahnz51P+4w9YTKNdXYPp7HuUftprHF7ePHIPfvCC1ECwOY94cUEAeC7S39CY0tmPENja1t5qa94SnWwfVwFn6HWfvAAjTW3hEs8ANCcooRZOim84OfhDn5+2w7zWW9vv8XP8cGdq2hsV2u49Flg4QUxAWD0cD6LcfaZfMbkzNn8Ndd+Mp/9WNETrvUtWvUn2ufzy8O7rh3aW0f76MouEgklu0gklOwikVCyi0RCyS4SCXP31N9gVgDgBQDDkbh7/wt3v83MpgJ4GEApgOUArnH3zlTPVTB1lFffdmYwNrG0kPY7bUr4bnyHj6B9mjv5HfeG1i4a29PJJxIUFIS3qMrP48/X1rqTxg4d2kVj3e18u6DCPP7v7uwI/xcUF4bXzwOA6hnTaKyolG811XqA38XPKw1P4ji8k9/dH9baQWPNu/bR2MY1e/g4yGukuICvQTe2rITGdu3hlZCcAl7cqqjkm5ruOxSuUDTm8tfAwc5wrPlrL6Frc3PwhdqXK/shABe4+2wktmdeYGbzAXwLwF3uPh3APgDpXR1PRNLqqMnuCUeKhHnJPw7gAgBHlsW8H8AVGRmhiKRFX/dnz0nu4LoLwDMANgFodvcj71+3AeCTm0Vk0PUp2d29293nAJgEYB6AU0LfFuprZgvNrNbMarvbUv5KLyIZdEx34929GcDvAcwHUGJmR+5ITALQQPoscvcad6/JKeYfQxSRzDpqsptZuZmVJB8XArgIwDoAzwH4RPLbrgXA190RkUHXl4kwFQDuN7McJH44POLuvzKztQAeNrN/BfA6gHuP9kTdPT1oIZMuyvP4wl/7DpMywwFexmnv5qW8dudluUO5vCTTScqUI/L4sYonVtGY82oS2jt5cNhwfrw8C48/fwT/N+eN5qWmMZOraWzCMF7Oy0d47bqp48MTZABgFHpo7GycTmO//vjLNNbQES7n7dvDy5779/PJOluf+x2NdXXxUmTOQf6cnT3hX28th//aO7oo/P/ZNixcHgb6kOzuvhLA3ED7ZiR+fxeR44A+QScSCSW7SCSU7CKRULKLRELJLhKJo856S+vBzJoAbEn+tQwA34MnezSOd9I43ul4G8cUdw9Osctqsr/jwGa17l4zKAfXODSOCMeht/EikVCyi0RiMJN90SAeuzeN4500jnd6z4xj0H5nF5Hs0tt4kUgMSrKb2QIzW29mG83slsEYQ3IcdWa2ysxWmFltFo97n5ntMrPVvdpKzewZM9uQ/Mqnh2V2HLeb2fbkOVlhZpdmYRxVZvacma0zszVm9vlke1bPSYpxZPWcmFmBmb1iZm8kx/H1ZPtUM1uWPB+LzezYFohw96z+AZCDxLJWJwDIB/AGgFOzPY7kWOoAlA3Ccc8BcAaA1b3avg3gluTjWwB8a5DGcTuAf8jy+agAcEbycTGAtwCcmu1zkmIcWT0nAAxAUfJxHoBlSCwY8wiATyXbfwjgxmN53sG4ss8DsNHdN3ti6emHAVw+COMYNO7+AoB372J4ORILdwJZWsCTjCPr3H2Huy9PPm5DYnGUSmT5nKQYR1Z5QtoXeR2MZK8E0Htr08FcrNIBPG1mr5nZwkEawxHj3X0HkHjRAeDbrmbezWa2Mvk2P+O/TvRmZtVIrJ+wDIN4Tt41DiDL5yQTi7wORrKHltIYrJLAWe5+BoCPALjJzM4ZpHEMJfcAOBGJPQJ2APhetg5sZkUAHgPwBXdvzdZx+zCOrJ8TH8Air8xgJPs2AL3XaqKLVWaauzckv+4C8AQGd+WdRjOrAIDkV75uUga5e2PyhdYD4MfI0jkxszwkEuxBd3882Zz1cxIax2Cdk+Sxj3mRV2Ywkv1VANOTdxbzAXwKwJJsD8LMRppZ8ZHHAC4BsDp1r4xagsTCncAgLuB5JLmSrkQWzomZGRJrGK5z9+/3CmX1nLBxZPucZGyR12zdYXzX3cZLkbjTuQnAVwZpDCcgUQl4A8CabI4DwENIvB08jMQ7nesAjAWwFMCG5NfSQRrHzwCsArASiWSryMI4zkbiLelKACuSfy7N9jlJMY6snhMAs5BYxHUlEj9YvtbrNfsKgI0AHgUw/FieV5+gE4mEPkEnEgklu0gklOwikVCyi0RCyS4SCSW7SCSU7CKRULKLROL/AEHjabHHM8BWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "155\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "160\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "165\n"
     ]
    },
    {
     "data": {
      "image/png": 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yJpODE3j5J392eNbZSceEm08A4Jgu3mRyYO8uqq19J1y6AoDf/eynVDvzy9cG7X92/fXUZz/4VlOf+uIXqLZq41au1e4O2hc21FOfPfX8fFx08blUq0goHb768MNBuzXvoD5lWXxOXu3Wd6iWVF4bCo0t3VQr2h4uKfZ1dlCfwVzZewB8291PALAEwDfNbAGAZQBWuPs8ACtS/xdCjFMGTHZ3r3P3t1O/7wOwEcB0AJcBuDt1s7sB8EuAEGLMOaK/2c2sCsBiACsBVLh7HdD/hgCAf41JCDHmDDrZzawQwAMAbnR33vn/x35LzazGzGoaG8NN+kKI0WdQyW5m2ehP9Hvc/cGUud7MKlN6JYDgZt3uvtzdq929uqysbCRiFkIMgQGT3cwM/fuxb3T3Hx0mPQLg0NLvtQDCy55CiHHBYLrezgbwZQBrzWx1yvYdAN8HcL+ZXQfgDwCuHJ0QgbyS6UH7cVV8C5+8mfOpdvqlCZ8wZvDuquo3w1sh9fTykstbhflU27iOb620aRefoffvb9RSLW9JuEtt4abw/DwAyJ3VR7VTJ8+g2qUX8y6vl595KWhv3LmJ+rz59GNUO3PxyVQ75vRLqdZVHI7/M9fyUl7n3jqq1W8Pdz4ORFcvLznW1e8P2nOzcqjP3rpwabOnm5frBkx2d38ZAJvAd+FA/kKI8YG+QSdEJCjZhYgEJbsQkaBkFyISlOxCRMJRMXBy0pRw6W3LZj4M8dabf0i1//Y3S/nBZmRSqba2J2hv+IB3UBVP5iXAK/+cD4H8IJd/+/hf//N+qr36argr64TTaqhPWQP/ZuPsc06nWtHeP1CtuDDcPVh88iXU58Q+3n1Xv4OXrvZm8W3A5raFn5v5mby7sS2TH6u1fSPVknj0Z7+iWkHp3KC9LI9382X1tAftvT3hzkxAV3YhokHJLkQkKNmFiAQluxCRoGQXIhKU7EJEwrgpvd3Tzcsnf/XWLWFhO+8Mwzu8k+uHW/+Dan972d9T7YabvxG0f/0rfPze7bf8b6qhiktTWOsRgPOqr6DaxGlFQfv7Dfx9PbOQH6yzge+L196SR7XJFeEhnNtfWEV9qkp4ubG6ipcpyxacQrWz28OdkZMqeDdfdgWfzbJjw06q8VccAPButC5SRjvYw+9xcm74ObOEy7eu7EJEgpJdiEhQsgsRCUp2ISJByS5EJIyb1fjVb/EtmdAWXsk86ZxPUJe1+16mWuMOvqLKVtyTuOe9R6l2+9SE1fhVfNX3F4/x+FfU/JZqF5/5Z0H72y+tDtoBoPbZ8PZaALDA/pJqU8v4CvnBdeH4H7o3vG0RABSeewHVZizhDUXo5NK/vfx40P5s0xrqc+XxZ1KtqI2nzMvP88fWvY+vxu/ZE24oai/mxzr7W38XtOcW/y/qoyu7EJGgZBciEpTsQkSCkl2ISFCyCxEJSnYhImHA0puZzQTwSwBT0f9d/+XufpuZ3QLgqwAODTD7jruH6xz/hcMRbqzY8uob3G17S9C8+NPh2V0AUHEabyLYsZnPXHt351YeR2Z20NzedJC6XHLjV6g2o4fPGMtrnEm1E8G3vZpVEp7jVt/A5/V9/tNnU+2Uyz9HtZkbm6lWs/WRoH3FprAdAFbkvUa1b/z4Bqq1H+yl2h17wlts5eWGG4YA4Jc7X6daxqrg/qUAgO+eyLfD6mhootqO3eGZd7PnTKE+Q2EwdfYeAN9297fNrAjAW2b2TEq71d3/74hGJIQYFQaz11sdgLrU7/vMbCOA8LhXIcS45Yj+ZjezKgCLARz6bHS9ma0xs7vMjM8BFkKMOYNOdjMrBPAAgBvdvQ3A7QDmAliE/it/cFC7mS01sxozq2ls3DMCIQshhsKgkt3MstGf6Pe4+4MA4O717t7r7n0A7gBwRsjX3Ze7e7W7V5eVjeyCgxBi8AyY7GZmAO4EsNHdf3SYvfKwm10OYN3IhyeEGCkGsxp/NoAvA1hrZodap74D4BozWwTAAdQC+NrAd2Uw5ASVfd0Jf/I3hedtTSzhXW8zPvlJqv3VVxdT7X/edjvVZs8qCdq3N2ygPk89xufdoZ2XjB77SXgbJwA49yTu17Y/3F21csVm6vO5ZeEOqoE45sfnUe2UT4Q/xR27jD8vmX0TqPazrXdSbdMq3tHX9W54u6aq2YuoTyc5hwCweRXvEGw8/3qqbXmJl/P2dYVLtwvm8+67oTCY1fiXAYSybYCauhBiPKFv0AkRCUp2ISJByS5EJCjZhYgEJbsQkTBuBk5e9InzqfZc68+D9p9861v8Dg/ybYvO+fxVVNvw2ANU23JMuDw4bS7vXkMuL5Mt/iYvAX72SydT7d5/4gMun7r7X4L2BedeTn16+C5OKL/5VKrt/ykf2uiXXBO0l87l5bV1r/MS2qtvvkm19qZWHkd2uFNx5nw+wHJXPR9ImrTH091rHqbaaVP5c13QFC6LnnXF1/nBhoCu7EJEgpJdiEhQsgsRCUp2ISJByS5EJCjZhYiEcVN6a23dRrXik8O1obZdB/gd8hmQePGJ+6mWcSyfuLVkYUHQ/pkrz6U+nZlnUe3mq/+aagAvJ32749+ptvus9rAw/Qnqs+HNm6jWvD+8DxkAgFdLMfuUY4P2pu38OZvVGfYBgOmnzKPalPypVKtaFO5uO/XUP6E+a1e9QrV3nqUS3il/kWpP31xDtfJZpfxOCQ9/sD5ob+nm51dXdiEiQckuRCQo2YWIBCW7EJGgZBciEpTsQkRCWktvDQfr8eNNwfHyWL2R7/V28/8Id7d99TI+4/I3D/2WaiVlxVTLnM73WPvk7LKgfQYWUp994LPyXwPv5HrjXV4qO+PC06lW9PlzgvYt7/O9xhpa+J5tWRW8LNQzhWtWmB+0e3EH9WlxHser6/g+cLndvJOusjzckdh6cAb1mXdcuMQKAH96wxd5HKWFVCucdeTX1Xu38FLeNb8jQ0Kb3qM+urILEQlKdiEiQckuRCQo2YWIBCW7EJFg7p58A7NcAC8CmID+1fvfuvv3zGw2gHsBlAB4G8CX3Z0PfgOQM2uily87O6jNnzWH+l1+0XlBe0E2n/3WcoCv7Lb37Kfa3j7eQdPZSlbW+3jzQV1DI9V+/ypfYW7fvZdqeZP4466aGW7kySyYRH1ae3qo1tHDH9v+Tv50T5sbXu2eUsI399yzi5+rPR98QLW293ZTrbIy3CQzi5wnAJgzjcdYmsdX6jdv5VtseQYvfO3cE256qu/kr8Wm5pawcNtm+Psdwf3SBnNl7wRwgbufgv7tmS8xsyUAfgDgVnefB6AZwHWDuC8hxBgxYLJ7P4f6JrNT/xzABQAOFbPvBvCFUYlQCDEiDHZ/9szUDq4NAJ4B8C6AFnc/9PlvJwD+uUgIMeYMKtndvdfdFwGYAeAMACeEbhbyNbOlZlZjZjV97Yl/0gshRpEjWo139xYAzwNYAmCSmR1adZgBILhK4u7L3b3a3aszCsN7swshRp8Bk93MysxsUur3PACfArARwHMArkjd7FoAfDsMIcSYM5hGmEoAd5tZJvrfHO5399+Z2QYA95rZPwNYBeDOge6ozx3tfeEyT3PC7KzVO8Lz6VrbuU9nJ9e6kx51AW+q6Nofnu+Wge6EOPihsnOLqJZfxoOsKK+gWk+w6AJ0HuTno7y8hGp5hbzZZe7suVSrqpgVtJ8Evq3VrBOrqDYHvHnpVvyGavVt4RLmxvXhGW4A0JOdS7W+HK4VlPDz2N7Oy70FxeFy3uw8/pgrK8Kl1Hdza6nPgMnu7msA/NFGVe6+Hf1/vwshjgL0DTohIkHJLkQkKNmFiAQluxCRoGQXIhIG7Hob0YOZNQI4NCRrCpAwoC19KI4Pozg+zNEWxyx3Dw5LTGuyf+jAZjXuXj0mB1cciiPCOPQxXohIULILEQljmezLx/DYh6M4Pozi+DAfmzjG7G92IUR60cd4ISJhTJLdzC4xs81mts3Mlo1FDKk4as1srZmtNrOaNB73LjNrMLN1h9lKzOwZM9ua+jl5jOK4xcx2pc7JajP7bBrimGlmz5nZRjNbb2Y3pOxpPScJcaT1nJhZrpm9YWbvpOL4p5R9tpmtTJ2P+8zsyAZEuHta/wHIRP9YqzkAcgC8A2BBuuNIxVILYMoYHPccAKcCWHeY7f8AWJb6fRmAH4xRHLcAuDnN56MSwKmp34sAbAGwIN3nJCGOtJ4TAAagMPV7NoCV6B8Ycz+Aq1P2nwL4xpHc71hc2c8AsM3dt3v/6Ol7AVw2BnGMGe7+IoCP7rR4GfoHdwJpGuBJ4kg77l7n7m+nft+H/uEo05Hmc5IQR1rxfkZ8yOtYJPt0AO8f9v+xHFbpAJ42s7fMbOkYxXCICnevA/pfdADKxzCW681sTepj/qj/OXE4ZlaF/vkJKzGG5+QjcQBpPiejMeR1LJI9NEtlrEoCZ7v7qQA+A+CbZhbe7zgubgcwF/17BNQBCO+xPQqYWSGABwDc6O5t6TruIOJI+znxYQx5ZYxFsu8EcPgm6HRY5Wjj7rtTPxsAPISxnbxTb2aVAJD62TAWQbh7feqF1gfgDqTpnJhZNvoT7B53fzBlTvs5CcUxVuckdewjHvLKGItkfxPAvNTKYg6AqwE8ku4gzKzAzIoO/Q7g0wDWJXuNKo+gf3AnMIYDPA8lV4rLkYZzYmaG/hmGG939R4dJaT0nLI50n5NRG/KarhXGj6w2fhb9K53vAvj7MYphDvorAe8AWJ/OOAD8Gv0fB7vR/0nnOgClAFYA2Jr6WTJGcfwKwFoAa9CfbJVpiOOT6P9IugbA6tS/z6b7nCTEkdZzAuBk9A9xXYP+N5Z/POw1+waAbQB+A2DCkdyvvkEnRCToG3RCRIKSXYhIULILEQlKdiEiQckuRCQo2YWIBCW7EJGgZBciEv4/OCCfVnbVCO0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "170\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "175\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "180\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bird\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,185,5):\n",
    "    model = ResNet('18').to(device)\n",
    "    model.load_state_dict(torch.load('models/resnet18_l2eps=70/' + str(i))) # 20 omg\n",
    "    model.eval()\n",
    "    eps=5000\n",
    "    c = 1\n",
    "    steps = 28\n",
    "    adv = pgd_attack(image.view(3,32,32), c, model, stepsize=2.5 * eps / steps, eps=eps, steps=steps, constraint='l_2')\n",
    "    print(i)\n",
    "    plt.imshow(adv.cpu().view(3, 32, 32).permute(1, 2, 0))\n",
    "    plt.show()\n",
    "    print(cifar_labels[torch.argmax(model(adv.view(1, 3, 32, 32))).item()])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "15\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "25\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "30\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "35\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "40\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "45\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n",
      "50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-79-e38911cc433b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0meps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0madv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpgd_attack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstepsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2.5\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0meps\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0meps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconstraint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'l_2'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\desktop\\nmep-proj\\adversarial.py\u001b[0m in \u001b[0;36mpgd_attack\u001b[1;34m(input, label, model, stepsize, eps, steps, constraint)\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequires_grad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m         \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mconstraint\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'l_inf'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    530\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 532\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    533\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    534\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\desktop\\nmep-proj\\models.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    530\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 532\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    533\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    534\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torchvision\\models\\resnet.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torchvision\\models\\resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    204\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayer1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 205\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayer2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    206\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayer3\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayer4\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    530\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 532\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    533\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    534\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\container.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m     98\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     99\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 100\u001b[1;33m             \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    101\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    102\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    530\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 532\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    533\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    534\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torchvision\\models\\resnet.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     65\u001b[0m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbn2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    530\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    531\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 532\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    533\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    534\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2d_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    346\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mconv2d_forward\u001b[1;34m(self, input, weight)\u001b[0m\n\u001b[0;32m    340\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m    341\u001b[0m         return F.conv2d(input, weight, self.bias, self.stride,\n\u001b[1;32m--> 342\u001b[1;33m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for i in range(5,100,5):\n",
    "    model = ResNet('18').to(device)\n",
    "    model.load_state_dict(torch.load('models/resnet18_l2eps=50/80')) # 20 omg\n",
    "    model.eval()\n",
    "    eps=5000\n",
    "    c = 0\n",
    "    adv = pgd_attack(image.view(3,32,32), c, model, stepsize=2.5 * eps / i, eps=eps, steps=i, constraint='l_2')\n",
    "    print(i)\n",
    "    plt.imshow(adv.cpu().view(3, 32, 32).permute(1, 2, 0))\n",
    "    plt.show()\n",
    "    print(cifar_labels[torch.argmax(model(adv.view(1, 3, 32, 32))).item()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "def remove_dataloader(dataloader):\n",
    "    base_images, base_labels = [], []\n",
    "    for batch in dataloader:\n",
    "        images, labels = batch[0], batch[1]\n",
    "        base_images.append(images)\n",
    "        base_labels.append(labels)\n",
    "        \n",
    "    return torch.cat(base_images), torch.cat(base_labels)\n",
    "\n",
    "train_data = torchvision.datasets.CIFAR10('data/', download=True, train=True, transform=test_transform)\n",
    "train_dataloader = torch.utils.data.DataLoader(train_data,\n",
    "                                          batch_size=4096, # just for test accuracy\n",
    "                                          shuffle=False)\n",
    "images, labels = remove_dataloader(train_dataloader)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def include(labels, c):\n",
    "    included = []\n",
    "    for i in range(len(labels)):\n",
    "        if labels[i] == c:\n",
    "            included.append(i)\n",
    "    return included\n",
    "\n",
    "def class_gaussians(images, labels, c):\n",
    "    \n",
    "#     for c in range(classes):\n",
    "    included = include(labels, c)\n",
    "    class_images = images[included].view(len(included), -1)\n",
    "        \n",
    "    mean = torch.mean(class_images, dim=0)\n",
    "    covariance = ((class_images - mean).T @ (class_images - mean) + 0.001 * torch.eye(len(mean))) / len(mean)\n",
    "\n",
    "    return mean, covariance\n",
    "\n",
    "def generate_conditionals(n, c, images, labels):\n",
    "    mean, covariance = class_gaussians(images, labels, c)\n",
    "    m = torch.distributions.multivariate_normal.MultivariateNormal(mean, covariance)\n",
    "    return m.sample((n, )).view(-1, 3, 32, 32)\n",
    "invertTransform = transforms.Compose([\n",
    "                                transforms.Normalize(mean = [ 0., 0., 0. ],\n",
    "                                                     std = [ 1/0.229, 1/0.224, 1/0.225 ]),\n",
    "                                transforms.Normalize(mean = [ -0.485, -0.456, -0.406 ],\n",
    "                                                     std = [ 1., 1., 1. ]),\n",
    "                               ])\n",
    "\n",
    "im = generate_conditionals(10, 9, images, labels)\n",
    "# im = generate_from_prior(train_dataloader, 1)\n",
    "# print(im.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(im)\n",
    "# for i in range(len(im)):\n",
    "#     display_im(im[i].view(3,32,32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fdHlwvOM4P9eLL3M7KbuD21rLw6cCAOQcC48fTMh6+/NWbq8l1KLEZ6u41dRUzD2ZjO5wgciafdwerMnittyytVdRbTRMLWNVO4HFqxZTbUYZb2u0970GfsLp4VRFb+3gU4p4kc3KuTwzr+UcsWYBdB3k/Z+uWBu25cL5oaNHd3YhIkHBLkQkKNiFiAQFuxCRoGAXIhIU7EJEQlqtt+7OLhzcFc42qtnB+2Tl14WrNi5eygsDHq7hGVS8UxpQvo1rLBs/qezi7eEktMTjAcBVHfVUeyZBO0rGwx3ghngZvIBlxe5wMcTRMrhrO9VO1BBvE0DziU9SbZFzW+7qBeGeaAVTuf3qZ3iRlTfe4n3l3j9ymGol+Xze+jvC13/NddycrT8V/j83NfC1684uRCQo2IWIBAW7EJGgYBciEhTsQkRCWnfjuzo7sXtneDd25Xz+urPy9tuD4+/s5r0rtiTsua+hSnJdNbajzRsaDRXpY+QkaLjjVir1v/wy1Tq6ws7F0mV8Z7fgIG+jtXz9OqqNhk8svplqM/L5bvZfzF7OD1rEtTvmHgwLg3znf7CR+ySnqrm9crSFP4e37d9HtcZZVwTHuy4P16YDgAHiQCQ933RnFyISFOxCRIKCXYhIULALEQkKdiEiQcEuRCSMpP3TAgCPAJgDYBDAZnf/gZnNAPAYgHIMtYD6krvzgmsAPDMT/UXhGmQFC+fQeRvu/lfB8f4lvK5X0ZuvUy2nhFf3as3l5kV9Z7iO26E8vvaBAl5zbdaeXVw700+133Vx0+51dAfHs8/yllFZRbyN1mW3hW3P0TJ3BrfJcjq5lYqE+m5IqBl32V3XhoWulfx4N3yaSpfn8vZPNz//HNW+9tM3qfZy65ng+Om3f0/nFC8Lp19lOl/fSO7s/QD+1t2XA7gWwF+Z2QoA9wN4xd0rALyS+l4IcYkybLC7e62770g9bgdwAEOFUe8E8HDqxx4GcNdELVIIMXYu6m92MysHsBbAVgCz3b0WGHpBQPIHyYQQk8yIPy5rZoUAngDwDXdvM+N/A35o3iYAmwAgP1P7gUJMFiOKPjPLxlCg/8Ldf5MarjOzspReBiBYPsXdN7t7pbtX5maO7AVCCDH+DBvsNnQL/wmAA+7+vQukZwDck3p8D4Cnx395QojxYiRv468H8FUAe8zsvFf0AIBvA/iVmd0L4CSALw53oI7eAbx1IuzOzboiXCsMALD+c8HhT1zHa499YU/YzgCAvTvfp1r2Fby9z+mz4eywvQ3hTDMAaFlQTrWGPdw6rGriLZmeBLf6mhG2B3/YVE3nzJnN67ut/NOvUm00FBXw31nZtQnV/OZwey2R1ZeFx0uLRnc88PXjpiu5doa70le+dSg4/sKut+ic6cRi7ermba2GDXZ3fxMAe//N8xWFEJcU2jETIhIU7EJEgoJdiEhQsAsRCQp2ISIhrQUnLb8IOcuvCWpbc6bReW1ZYbuD55MBBYW5/HjN3IbqyV1AteyZYcvriit5Fp0t5NqRA+FWWABQ18YLG3Ys4tYbFlWEx994iU45036Eaq0z+amSaCRJe3sPkgKQACoG36Pa3JKEMqFn+7iWR6y3JJJyN/cf4Nr1K7h2Dc/am51TGhyfnnuWztl+OryOzj61fxIiehTsQkSCgl2ISFCwCxEJCnYhIkHBLkQkpNV6y86dgjkVq4JaS0MNnff4o1uC4xXzuV137CDv5XXsGM82W/0pbvFkNYetkNXrwnYiAKA4k0pv791KtQ4kWChTeFGgWQVlwfEzSUZlNl9jJk/oS7xVPPKd7wfHn/i/v6NzSs8dpdq1v+GW6NyZ3F5rLwpnMWbPDV8nAOjtLKFaQQ3PpvzLgTaq7Uiw7A43NwTHWzJ4Ac52og0k9DjUnV2ISFCwCxEJCnYhIkHBLkQkKNiFiIS07sZPKyzEbZ8Mt+PJ7eeJH1tfDbfO+eEfeIunnfteoBpv8ARs3fIU1Y4eDNeg239yP50zbV5COf08nnFRmDuDavl54d1bAFieH04aKqrgNdcsoaVR3bObqfZqHq+T99hPww2CKhaG3RgAmNGfx8/1whtUK+jn2jFSUO3aL36Gzqnp4jv/zzz+CNW+t4UkIQG4Zh1ve+WD4V38K6/k62gjv86dOTykdWcXIhIU7EJEgoJdiEhQsAsRCQp2ISJBwS5EJAxrvZnZAgCPAJgDYBDAZnf/gZl9C8DXAJzPDnnA3Z9LOpZjEP0eblvzpxtvovOe7gsnwmzfwmuPJdlrSRx9L2yvAQCmTAkOr6zgtceqa3iCz5LFvF3QihVL+TKyeMJFT224vl5eTSOd059NCsYB2Lr1NaotKOG24mXLw7bRxi9spHNm5YavLwBctXon1V5+9Emq9TSGnwnTM/hTv2GAW5FJ7D0R7G0KALjl5s9SLbMzPC+zL5/OKSsN163Lzsqhc0bis/cD+Ft332FmRQC2m9n56oXfd/f/NYJjCCEmmZH0eqsFUJt63G5mBwDMm+iFCSHGl4v6m93MygGsBXA+Efs+M6sys4fMbPo4r00IMY6MONjNrBDAEwC+4e5tAH4EYCmANRi683+XzNtkZtvMbFtnR/s4LFkIMRpGFOxmlo2hQP+Fu/8GANy9zt0H3H0QwIMANoTmuvtmd69098r8wtH2xBZCjJVhg93MDMBPABxw9+9dMH5hXZ+7Aewd/+UJIcaLkezGXw/gqwD2mNmu1NgDAL5iZmsw5HIdB/D14Q7U1dWKqqpwDbLFRdwaKisJZ3L9m3tvo3PW7+cZSD/+p99Srb+bW2XoCteF2/nsH+iUwcEOqnV38T9rphdw2+XOz4UzBwGgJW92cNz6eb2+lw/x1+l/+IdfUm00/uar1TxrbP06bk9dVXY11Uo+W0C10kPhFlu9ueV0zpYt/PeZSHsxlU528Ou/qjz8O2vu5TXo2ixcN3AQJM0PI9uNfxMIHiHRUxdCXFroE3RCRIKCXYhIULALEQkKdiEiQcEuRCSkteBkQX4urlsTtsRu+EQlnbd4waKw0MXbFmE6//j+d/7+v1Dt/zzBC07+jwe+ERzvy+CtmrIKeRFFdLVSafs23hoqSVu68rrg+IoreIbduXcS7EZPyAIcBTW7wll5APD0rp9yLX8J1b5wC8+YnDJ7WXB85/4jdE5dAy8gmsTSVYupVpzTQ7Wu5pbgeFYG7721cuXK4HheLn++6c4uRCQo2IWIBAW7EJGgYBciEhTsQkSCgl2ISEir9eb9g+htCWeBnTx0jM5bXBG2TzDILa9w7s4QUxfNp9p//Jv7Llo7VPUqnfPyazyDamk5Lyr5wnP/TLUtbx+kWlvPQHD81ju5PXXVuvVU2/fWu1R7/flfU61sbrh45L5TvChjIp1HqbSr9jI+rT3cF6/5vR2jW0cC3W1nqZabE87cBICcnLCF3FjNY6KzKWzXDfbz4qG6swsRCQp2ISJBwS5EJCjYhYgEBbsQkaBgFyIS0mq9TZmSjytXhbPbcrMSssN6iZYfLtSXbi5f/ZlRaUl87k++Mqp57+4NF1jMLOHXd8mcy6lWbP+OatXvhbMAAWBOaUlw/O0qbuX9+PE3qfbIUy9RraGpjmqFheHCnTmLiZ0LoPfUCaplziqj2o0J/Qqvuobbmz1dYXuwtYMXYW3rD2fRDTivAqo7uxCRoGAXIhIU7EJEgoJdiEhQsAsRCcPuxptZHoA3AOSmfv5xd/+mmS0G8CiAGQB2APiqu/cmHSsjIwv5ueHOzjbYR+f9/pnfB8dXruCJJLOXkbp1AJDJW/F81Fm/anVwvAbNdE7hKM81P6GuHXAuOFp5w1o6YzCXJ4vMXzSDaltef51qRWQ3vm+Adxivmx1O4gGAnBzelqur7jDVju/o5PN6wlpBQvJM/enwufr7eK27kdzZewDc5O5XYag980YzuxbAdwB8390rADQDuHcExxJCTBLDBrsPcT4vNTv1zwHcBODx1PjDAO6akBUKIcaFkfZnz0x1cK0H8BKAIwBa3P188mw1AF67WQgx6Ywo2N19wN3XAJgPYAOA5aEfC801s01mts3MtrW28jrpQoiJ5aJ24929BcBrAK4FUGxm5zf45gMIdhpw983uXunuldOmfXw3xoS41Bk22M1sppkVpx5PAXALgAMAXgXwZ6kfuwfA0xO1SCHE2BlJIkwZgIfNLBNDLw6/cvdnzWw/gEfN7L8D2AngJ8MdqOtcB/ZtfzuoFWbypTQ0hWvNnT1zks5Z/D5PWFhQxm2csrU8KQTZpURISOJJeD3t6uT12AazuCFmfdySyS8IawMNbXROm4fr1gHAjBkJ94PMqVRyktyRN4VdQ6BsLv+drVmZYF018PZVzS1hy7Gzl/+/Zlx+BdVskLvLZ4/zllLH0E61xRXhlmjzZoaTiQCg07uD49mZ/P81bLC7exWAPzJH3f0ohv5+F0J8BNAn6ISIBAW7EJGgYBciEhTsQkSCgl2ISDBPqFk17iczOwvgfIGvUgBhfya9aB0fROv4IB+1dSxy95khIa3B/oETm21z93D1Sa1D69A6xn0dehsvRCQo2IWIhMkM9s2TeO4L0To+iNbxQT4265i0v9mFEOlFb+OFiIRJCXYz22hmB83ssJndPxlrSK3juJntMbNdZrYtjed9yMzqzWzvBWMzzOwlM3s/9ZVXRJzYdXzLzE6nrskuM/t8GtaxwMxeNbMDZrbPzP46NZ7Wa5KwjrReEzPLM7N3zGx3ah3/LTW+2My2pq7HY2aWc1EHdve0/gOQiaGyVksA5ADYDWBFuteRWstxAKWTcN5PA1gHYO8FY/8I4P7U4/sBfGeS1vEtAH+X5utRBmBd6nERgEMAVqT7miSsI63XBIABKEw9zgawFUMFY34F4Mup8f8N4N9fzHEn486+AcBhdz/qQ6WnHwVw5ySsY9Jw9zcANH1o+E4MFe4E0lTAk6wj7bh7rbvvSD1ux1BxlHlI8zVJWEda8SHGvcjrZAT7PACnLvh+MotVOoAXzWy7mW2apDWcZ7a71wJDTzoAsyZxLfeZWVXqbf6E/zlxIWZWjqH6CVsxidfkQ+sA0nxNJqLI62QEuwXGJssSuN7d1wG4DcBfmdmnJ2kdlxI/ArAUQz0CagF8N10nNrNCAE8A+Ia789I66V9H2q+Jj6HIK2Mygr0awIILvqfFKicad69Jfa0H8CQmt/JOnZmVAUDqK69ZNYG4e13qiTYI4EGk6ZqYWTaGAuwX7v6b1HDar0loHZN1TVLnvugir4zJCPZ3AVSkdhZzAHwZwDPpXoSZFZhZ0fnHAG4FsDd51oTyDIYKdwKTWMDzfHCluBtpuCZmZhiqYXjA3b93gZTWa8LWke5rMmFFXtO1w/ih3cbPY2in8wiA/zxJa1iCISdgN4B96VwHgF9i6O1gH4be6dwLoATAKwDeT32dMUnr+DmAPQCqMBRsZWlYxycx9Ja0CsCu1L/Pp/uaJKwjrdcEwGoMFXGtwtALy99f8Jx9B8BhAL8GkHsxx9Un6ISIBH2CTohIULALEQkKdiEiQcEuRCQo2IWIBAW7EJGgYBciEhTsQkTC/weKP0IPpp3ZswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B/n0AqwBcDaAZwLfYL5rZo2ZWZ2Z1o85rWgshZpcpBbu7t7j7qLuPAXgSwA0Jv7vN3WvdvTbT+AaGEGJ2mVKwm9nFdXs+C4DXzxFCXBZMxnr7GYA7ACwwsyYAXwdwh5ldDcABnADwh5O5s3klJXjo7nuC2n33hMcBoLs3nE2U38Vb+BS0NVKt4wjPXGo6fYxquSvCddCO7OcWyc5z3OY7ldCq58ZP8VZCa5fyLZL2g+FzMpxbQ+dYC3/NX5RQkq89XDptynSf502qso/zjLLyBMtr/WDYts2v4Ivffa6BamvB7d55CfXfbgDPVOxHeI2HE67FHQkWG2PCYHf3LwSGf3jJ9ySEmFP0DTohIkHBLkQkKNiFiAQFuxCRoGAXIhLSWnAyvzAPm68Pt+NZt5wXPWw4ER7PSfhC3qYabjUVLecFIjHEW/hgZdiuecgX0iljJTxtbMeZDqotu5pnlO1+912qDfWELZ7RPN7SaE1ChiA3jICyJHEKZOTzTLTT4Blxi7K4HfZOUzhbrrqIF4Dcfo5nHCatoxa8OGd2gs2aQYpHZibYa6Xl4dKSnZ0fJtyPECIKFOxCRIKCXYhIULALEQkKdiEiQcEuRCSk1XpzH8HYSDh7qYi7P7hic9iWm2fcrsujGfbpJenV9EZMoWAjgPY3uCWz6Zra4PjwGM/yOs8T83CylWtrlnBtKiy65Uqq7ekO90MDgJY+bpd25YYrql1759V0zt6hG/k6dh2i2tL7b6Va21FeMHPgaDjT8nOri+mchtPvBMc/3HuUztGVXYhIULALEQkKdiEiQcEuRCQo2IWIhLTuxvd3n8cHr/8oqN17/Qo6b9G9fJf2ciChZBn6eW4EwDdb4TzfAitrN1OtdCx80GPhDkMAgOwEJ+Q07wyVWAVtNRlvTpjTAp4IY0U862a4IJtqrZvXBccPb9xA5+xuHKFa+8ZuqhXdyf5qIG8trxuYszP8mN28hSfkrL5QGhw/feq3dI6u7EJEgoJdiEhQsAsRCQp2ISJBwS5EJCjYhYiEybR/qgbwYwCLAIwB2Obu3zWzcgA/B7Ac4y2gPufuvA8SgAwfRcFIODFh35636LwFC3KC4wP94TpcAHC2mdd3q1jNs2TWruanpKQoPF6whk5BeOXjJJ38EwNcq1rCj9rZHG6hlFHBX9e33lNANd6QCeApIQA7YlPCnKIi7jdWloRbJAFA/xDXzp/fFRwvHFtK52wt5j2v2pbxhJzblvGEnPcv8NpwTWUtwfGWIV4nLyOf2I3GW1BN5so+AuBP3X09gC0A/sjMNgB4HMBr7r4GwGup/wshLlMmDHZ3b3b3Xanb3QDqASwB8DCAp1O/9jSAz8zWIoUQ0+eSPrOb2XIA1wB4D0CluzcD4y8IAHg9ZSHEnDPpYDezIgC/BPCYu3ddwrxHzazOzOr6hvlnKyHE7DKpYDezbIwH+k/d/bnUcIuZVaX0KgDBmibuvs3da929tiCbf4dZCDG7TBjsZmYY78de7+7fvkh6EcAjqduPAHhh5pcnhJgpJpP1dguALwLYb2Z7UmNfBfBNAM+a2ZcBNAL4vYkONDA4iA+Phetttf7q/9B5VxaHraZ9u+rpnO/94MWElXBLo3zj71DtP//xvwuO3/dguKUVACxKqNOWyZO8sHCQZ15VLMulWl9JWBtr4e2HeGOoZH6xk2tZ14XH88POKwBgxzN/T7UDTR9QbVUFzwJsPBeu1da1upzO2bSYZ69lLOXtvPp7zlKtrJzbpVWrwmspnc/TEbsawsanj/En1YTB7u5vA2Dm3d0TzRdCXB7oG3RCRIKCXYhIULALEQkKdiEiQcEuRCSkteDkqBs6hsNfrFlewVs5rb0yXHDyN+/uSLg33gYnibYD+6j29vbwN4JrVvIsqcbz3EIbPh22IQHgwVu3Ug2ZvG1UQ1fYjny/nrcfWrbgAar95Bn+9YmvPZaQDlFzV3D483fdQqck2WtJDPbyYpQ5BeGKnyf38cf5rqpKqq2qXku1n33vr6iWkcGz0YrKw2vcObqbznl717vB8Y4OnniqK7sQkaBgFyISFOxCRIKCXYhIULALEQkKdiEiIa3WW05+IZZt3BLU1l3Pc2peez2c4fPjnzw5I+uaLIPHwvZVw3v76ZyGPm6vlTqvKnnTdTdRbXFCb7a/+MvwOdm5lxc8HD7L7Zo/eez3+Z0lMXwiOLyq5h46pSahPGcjhqhmQ3z9K0rCHenKMnmRzY4z56jWMP8g1Z556RWqjZ3jGXFTgvXg48mNurILEQsKdiEiQcEuRCQo2IWIBAW7EJGQ1t344pJ83H7P+qCWkc93W7/7378xW0u6JD7Y935wfMfRw3TOEM9Zwb3Xb6La4sqKSa/rYv7phXAdt7IlvNLcxmW8rtpUuW55dXC8OqG+W14u6a8FAIO8eF3VsvB9AcDKos7g+IJcXsdvxz/wWnhvPnuGamO849iUKFnNnzyb1ocTx/Zu5+6PruxCRIKCXYhIULALEQkKdiEiQcEuRCQo2IWIhAmtNzOrBvBjAIsAjAHY5u7fNbMnAPwBgI+yBr7q7i8nHWt0bASd/ReC2ju7uH3V3XUkOH791t+lc7bcdjPVelobqXay4TzVjnSH7cEFZdz6ueMuntDyX//4c1SbKu0d4SSOsUJu47y/l9djmyrLysI22o+e/CmdczjBXkvi/aOvU41V3ltbuYzO2dvC7bXeyS7qn8Gbmt54U7iu3U03rqFzNm4MW2/f2MeTeCbjs48A+FN332VmxQB2mtmrKe077v6XkziGEGKOmUyvt2YAzanb3WZWDyChXaEQ4nLkkj6zm9lyANcAeC819BUz22dmT5lZ2QyvTQgxg0w62M2sCMAvATzm7l0Avg9gFYCrMX7l/xaZ96iZ1ZlZXU9f/wwsWQgxFSYV7GaWjfFA/6m7PwcA7t7i7qPuPgbgSQA3hOa6+zZ3r3X32qICXsxfCDG7TBjsZmYAfgig3t2/fdH4xZkVnwUwtXYeQoi0MJnd+FsAfBHAfjPbkxr7KoAvmNnVABzACQB/ONGBunv68ebb4fZEz23nrW4Yh+veoVpNZSHVRga6qbZ792+pVrzl9uD4tfddS+fcfAVvJVRROLWkw//9vb+gWm9fT3C87yxvh1V/5kTCvfHWVjffdyfVBodOBcff2znzNl9RBn/HeMMN64LjJYu5rbWwldtyg5m8nded9/GWXb97T7gdFgDk5oUfs731e+mcFWvDe+SFhS/ROZPZjX8bQKhRVaKnLoS4vNA36ISIBAW7EJGgYBciEhTsQkSCgl2ISEhrwUnLyEZOycKgNn/+IjqvtTWcidbZx7OkGj7kVt7+A+F2UsC4j8ho//VzwfGn3wyPA8CBDSv4ARseotJvDoQtSgB4/ZVLt6+Kcvi3F1ubeWuovCJ+RnrO8eyw/Yf4MafCdRt5Ac4v/Vue/ZibF24p1T3Is9DmZ4UzygCgdCm35VbX8L5co6O8EOQIaXtVkWAfzystDo5nZvLrt67sQkSCgl2ISFCwCxEJCnYhIkHBLkQkKNiFiIS0Wm8ZWVnILw1bKJs21NJ5rSXh4oVDfby43r4Ee23GGeRS3W5W8hD4wu6/noXFhCkaKaBaeR4vMrRxGbeaMvp4BlhVcfhxLgB/zP7sr/4L1W67KdwjEAB6zx2g2tu7wtr8cl6A84oKbr0NFvMswIGBdqo1XmiiWmlZOIOtu5tby6ND4XM/NMB7JurKLkQkKNiFiAQFuxCRoGAXIhIU7EJEgoJdiEhIq/WWk5WFZRVhS6Y4bwGdl70+bP9UVoYz6ADg4MmDVOtKsEEyRvkpaekM20YNp/jxTp0M96mbLZYuvCo4/sYrf0vnrN6wgWov/OIZqnV3cc+xpyvcFa1q0QCd8+kvPUC10dEWqh3s5FmAt99+a3B8Xi63G7OHub124MwY1boTrp09A+EsNQA43xi2y9qauE25cH54jUMDo3SOruxCRIKCXYhIULALEQkKdiEiQcEuRCRMuBtvZnkAtgPITf3+37n7181sBYBnAJQD2AXgi+7Ov4UPIDMDKCkKf4H/wFFes+zw8XAyybCFGtWMM+R8p7i9he+ejyQktRjCO7FtpOXSRGSDJ6dU1VRTraeTJ0jc++lwK6rD5/iO9d6XuPbBQa71DvBEmJVrw+sor+yjc95/+y2qDfaepVrOaAfVzvSGa++db+Y14ZYtqqHa7vpwPUQAwAiv89ef0HKspz9cg877+fGyM8JtxUb4ZvykruyDAO5y96sw3p75fjPbAuDPAXzH3dcAaAfw5UkcSwgxR0wY7D7OR5eu7NQ/B3AXgL9LjT8N4DOzskIhxIww2f7smakOrq0AXgVwFECHu3/0Pq4JQDgpVwhxWTCpYHf3UXe/GsBSADcACFUSCBYYN7NHzazOzOp6+sLfqhJCzD6XtBvv7h0A3gSwBcA8M/tog28pgGDHAHff5u617l5bVMCL3gshZpcJg93MKsxsXup2PoB7ANQDeAPAv0n92iMAXpitRQohps9kEmGqADxtZpkYf3F41t1fMrMPATxjZt8AsBvADyc6kGMEw2Nhm2Tdel73KzsjbFu09ybYGYM8YaEokycl+Aifl5URTj4o7+KJE3293MtbWbmUahs2r6La0AD/OJQ9GLaoXvn5T+ic+Qt5i6qKhXwrZl7CuVpdFX4Xt2UTb/P1wcH9VOto4UkhPtZFtZ6zYS8qb4zbngW9PFnnyvnheogAMK+QJ9cUlvDnXP9wfnB8cIS33hoZI39XDm9rNWGwu/s+ANcExo9h/PO7EOJfAPoGnRCRoGAXIhIU7EJEgoJdiEhQsAsRCebOt/dn/M7MzgE4mfrvAgAJKURpQ+v4OFrHx/mXto5l7h4s9JjWYP/YHZvVuTtv8KZ1aB1ax4yuQ2/jhYgEBbsQkTCXwb5tDu/7YrSOj6N1fJx/NeuYs8/sQoj0orfxQkTCnAS7md1vZofM7IiZPT4Xa0it44SZ7TezPWZWl8b7fcrMWs3sg4vGys3sVTNrSP3kKVSzu44nzOx06pzsMTPek2nm1lFtZm+YWb2ZHTCzP0mNp/WcJKwjrefEzPLM7H0z25tax5+lxleY2Xup8/FzMwtXqmS4e1r/AcjEeFmrlQByAOwFsCHd60it5QSABXNwv1sBXAvgg4vG/geAx1O3Hwfw53O0jicA/Kc0n48qANembhcDOAxgQ7rPScI60npOABiAotTtbADvYbxgzLMAPp8a/18A/v2lHHcuruw3ADji7sd8vPT0MwAenoN1zBnuvh3AJ+tBP4zxwp1Amgp4knWkHXdvdvddqdvdGC+OsgRpPicJ60grPs6MF3mdi2BfAuDURf+fy2KVDuAVM9tpZo/O0Ro+otLdm4HxJx0A3qJ29vmKme1Lvc2f9Y8TF2NmyzFeP+E9zOE5+cQ6gDSfk9ko8joXwR7q7DBXlsAt7n4tgE8B+CMz2zpH67ic+D6AVRjvEdAM4FvpumMzKwLwSwCPuTsvP5P+daT9nPg0irwy5iLYmwBc3O6EFqucbdz9TOpnK4DnMbeVd1rMrAoAUj9b52IR7t6SeqKNAXgSaTonZpaN8QD7qbs/lxpO+zkJrWOuzknqvi+5yCtjLoJ9B4A1qZ3FHACfB/BiuhdhZoVmVvzRbQD3Avggedas8iLGC3cCc1jA86PgSvFZpOGcmJlhvIZhvbt/+yIpreeErSPd52TWiryma4fxE7uND2B8p/MogK/N0RpWYtwJ2AvgQDrXAeBnGH87OIzxdzpfBjAfwGsAGlI/y+doHT8BsB/APowHW1Ua1nErxt+S7gOwJ/XvgXSfk4R1pPWcALgS40Vc92H8heW/XfScfR/AEQC/AJB7KcfVN+iEiAR9g06ISFCwCxEJCnYhIkHBLkQkKNiFiAQFuxCRoGAXIhIU7EJEwv8D9jzAGHlcqZ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vyhb7a9eVlfDEmvIF/uKAnzz/EbUZzZ39nwD4UrAedc4tS/wb2dGFEJPKiM7unHsfAO9OKIT4g2A8n9nvM7MtZvaEmUXUUhZCnA6M1dkfAzAfwDIAjQBoP2Azu8fMqs2suvUwqbsuhJhwxuTszrlm59yQc24YwC8A0F0t59wa51yVc65qemHEjo4QYkIZk7Ob2YktLG4BsPXUTEcIMVGMJvT2NIBLARSZWT3iZcQuNbNliOdE1QH43mhO1tfbhT21/uylJedfS+3W/Gf/4bt28o8FeeXnUq1pPW+rs2/BSqp1HWzyjnfysmTo3r2dagfsGNU2vM4DUXkxnnnVleevXde7hGeGgZdHA6LaP0VR4A+lpkXE+dqwjWrPga9VcT8PUR1M9efSpR/lT1p5uT9cBwAp+3l7pcLBHKr1HeJ26Vn+UKqbxtMRu1P99+lh8LUY0dmdc77qjI+PZCeEOL3QN+iECAQ5uxCBIGcXIhDk7EIEgpxdiEBIasHJzIx0LJnPwhq91G727EXe8SuvvJjaFKy8nmq7D/JKicXTeKHK7n7/ci0q4S2ePqrlFRundU6nGlp5VtbsXp4RdyjFXzGzaXMmtenwd+QCAEwda+iNJHk5/Adqko8XqNaE/VTbWNdKtR0D/rDWnMW89OVwRDupzgZe7HPP9nlUqyVFJQFgmBS4PGflAmpTNDXbO97ZwUOUurMLEQhydiECQc4uRCDI2YUIBDm7EIEgZxciEJIaekNaFjCdZF818tSrldfc4R1fsGo1tRlo5r3N2mNDVDvyOc/Wze/2vzZWHubhjuYuHnqr6ubdzfKqeXjw/OYpVCs94q8ZcHVrBbWZOhFXgT8yhP7zeThp8zoebmyMSL/bt3ML1Q5l+kO9w1fwsO3GHZ9TrW4fL+i4pIsXK21pOUi1w52d3vGiKbz+Q1qR/7oa6uXXve7sQgSCnF2IQJCzCxEIcnYhAkHOLkQgJHc3PoLPt/J6cgca/DuP03fzHc6te3lfi73d/t1PAEgdGqBaz6ED3vFcx+dRUeRvdwUAWRl8Fz9jXz3VvkkSJwCg8Ez/Tv28G3lLI5zBpTFT7h++/kaegFK9jbe16u3kdQPfwz4+j+y53uEWx2vCIdZFpTOX8l38y1bx+oV7Gvg10tXgbyl151XfoDabSW3DGKm5B+jOLkQwyNmFCAQ5uxCBIGcXIhDk7EIEgpxdiEAYTfunUgD/DGAWgGEAa5xzPzOzAgDPIh5kqQPwLedcRCOkaBZdydsCRWmMVWPUavdybd4M//iFPG8CnY08oaUgh4cAF8wt5wfl3YmAWhLOi4i8JZPOwzyRpL2X13dz4KHUSIb8tQ0/2cVr2rWvf58fL43XpxtI5wlWDfW8DVhmfZ13vGvz76hN9br13vGeDh5SHM2dfRDAD51ziwGcD+BeM1sC4AEA7zjnKgG8k/hdCHGaMqKzO+canXObEo87AdQifm+5CcCTiT97EsDNEzVJIcT4OanP7GZWDuBcAOsBzHTONQLxFwQA5E2uEOJ0YNTObmY5AJ4HcL9zruMk7O4xs2ozq25t5fW9hRATy6ic3cxiiDv6U86545X8m82sOKEXA2jx2Trn1jjnqpxzVdOnRzRFEEJMKCM6u5kZ4v3Ya51zj5wgvQzgeL2oOwC8dOqnJ4Q4VYwm620VgNsB1JjZ8R42DwL4KYDnzOwuAPsB3DoxUwTAIhqpYzvclo95na7XavZQLTY1wzvecpB/PIml83PNncKzqxbkRMTXjGc24e0P/eOvkHEAWFTMtZmVXLvycirtfetF7/iFD99PbSrAMw5LZ/EadCmtPBw2XObfSpqRl0dt2iPquAH8OVv79ttUe+X5f4w4pp9n3/uXk7aJYkRnd859CICtJn+2hRCnFfoGnRCBIGcXIhDk7EIEgpxdiECQswsRCEktODkMoJtoa99YR+3+/AcPescfvP+PqU1jGzsTsK6Gtwva7fiStDT5s9RqN+ykNohFhMm6eZHNF+Yvp9pSnlyFTbvf9I4PgRe+/F/zf0K1utr3qPbJ716n2s83+OfRGRFeW/31q6h2w1kLqfbKY7+iWk9bnXe8I4eH6xBVwJIGpgDXxbMY//jGG6lWnO/PzFv7yrvUpmHYHwau6+qhNrqzCxEIcnYhAkHOLkQgyNmFCAQ5uxCBIGcXIhCSGnpLAZBFtMIcfygBAKqWLfCO3/D9/zimedywn/fd2t/Ks5o2bveHyl5dsInafLptK9WaPvWWAAAATDvH/38GgPZOXjtkw27/63cOrqM2y574IdX2/5IXPXz/f/wt1W7+jv98v/wHXp3zQBavill+2WqqXVa7jWrVDf61WnoGP1fzx1QC4O87CADfve1Sqt14571RB/Wy7eUnqNZw1H+d3vvjv6E2urMLEQhydiECQc4uRCDI2YUIBDm7EIGQ1N14gL+6NB6I2LXuavSOtx9toDYDvaQNEoDH//HvqXagZiPVmjv8CQs93Ty547ysTKqV3Xwu1a6+hFfidQd5l63mD/1ZMgtvOvkWWgDwq1/zHeGPa3nNtbvv9ie1/JIWFAQ+eO4Zqn20rJRqt15/AdVmbvHv1D/9m+epzVg51s2Tr6Jo3u6P5jS383txwez53vG0GI9q6c4uRCDI2YUIBDm7EIEgZxciEOTsQgSCnF2IQBgx9GZmpQD+GcAsxMvIrXHO/czMHgLwXQDHex896JzjRckA9A8cQ11DjVd74dVnqd3rb77mHb/m5luozefbeJ25o4f8IbSRyCe5E5UzCqnN4jPPptp37/sW1Y519lNt18EdVCtc+TXveFnVedTmuddqqfbsK/+HagBfx76hQe/4tV/nTYTeeu89qp1/5b+j2kVVV1Ot7KPfeMfXfchDrNsbeDLU3LN4bcCGY31Uaz3Mk54OpfrTw6yYtwAbjPld1xm/f48mzj4I4IfOuU1mlgtgo5kdD7A+6pzjaTZCiNOG0fR6awTQmHjcaWa1ACK6DgohTkdO6jO7mZUDOBfA+sTQfWa2xcyeMLNpp3huQohTyKid3cxyADwP4H7nXAeAxwDMB7AM8Tv/w8TuHjOrNrPqw4f51zyFEBPLqJzdzGKIO/pTzrkXAMA51+ycG3LODQP4BYAVPlvn3BrnXJVzrqqwUDd/ISaLEZ3dzAzA4wBqnXOPnDBefMKf3QKAZ7IIISad0ezGrwJwO4AaM/ssMfYggG+b2TLEi3LVAfjeSAfq7R3CFzv8b+V7M0uo3QVX3eodb+7h9cCOHtodNROqpCKHH7PNf74NbYepzYbPeQuf1qkrqTZ1bhnV3lvnzwIEgL2D/my5uUt56G3vEd4aas4lN1Pt6pW8JdOSq671jl98753U5rFH/5pq02fMotrOFv5cP/jTn3vHt9ceoDapZRVUy8jOo9qLb66lWn4Ff66vv+VK7/j0BbwO4c5P/SFsl5pKbUazG/8h/A2uImPqQojTC32DTohAkLMLEQhydiECQc4uRCDI2YUIhKQWnBwcAg63+bW83HnU7li3v4jewYadEWfjLZKiGALPeDrVPP9ERNHDs3ioDDVRgRB/iGpXTTW1WHJGOdXuvus2qs2dlk+17h7/feTo9h5q87//7g2q/fzPfkK1U83Q/j1UK1h0JtUuu/oyflBedxR76/znW1BWTm2+VuXPvsueMoXa6M4uRCDI2YUIBDm7EIEgZxciEOTsQgSCnF2IQEhq6C01lo5ps+d6te/8Ec/wSSPhsJ0bP6A2az/IpdpHv/2QajNn+OcHAG/WsMKMvK8cwIsQAhGhwx1RocMILd1fMaxyXrF3HAB+dMMyqtX28B5xP/9Lb70SAMCuTf61qj/M12O4kYcHTzV3/8ntVFu13F+0EwAuvfgbVCufz/vz7WnkWXaFs/wFS/MiMtgYvoy14+jOLkQgyNmFCAQ5uxCBIGcXIhDk7EIEgpxdiEBIaugtMz0dC+b5Q1uVM0/+eCtWnEG173z/Lqp17+J94LKn8PDJrqZD3vHWiL5sRzp4ccj0XF7ccstmXqz36Wf8/csAoC/bX7gzM8aLc0ZxcB+f/xc7eThpqNvf662tjaQ9Aigq8VYjBwBceF4l1R559C+oNn/efKolk4oSXsQyWejOLkQgyNmFCAQ5uxCBIGcXIhDk7EIEwoi78WaWCeB9ABmJv/+Nc+7HZjYPwDMACgBsAnC7c45vSwNITQPySW/HqFSS/v5u73heenqEFd99ziDJOAAA4+19SrP8yTX9B5v4ufJiVMvK5K+1ZXN4PbM//QGPNDSR3e6cAt4+KYr01maqrVzId5iHCv1tvmaW8rp1yy+sotof3b6aaiX5Ec+n+P+M5s7eB2C1c+4cxNszX2Nm5wP4KwCPOucqARwBwK9AIcSkM6KzuzjHc0xjiX8OwGoAxwO+TwLgHQCFEJPOaPuzpyY6uLYAeBvAbgBHnXPHvzlRD8CfSC2EOC0YlbM754acc8sAlABYAWCx7898tmZ2j5lVm1n14UOtY5+pEGJcnNRuvHPuKID3AJwPIN/Mjm/wlQBoIDZrnHNVzrmqwiL+VVQhxMQyorOb2XQzy088zgJwBYBaAO8C+Gbiz+4A8NJETVIIMX5GkwhTDOBJM0tF/MXhOefcq2a2HcAzZvYTAJ8CeHykA6UYkEmiZX293G7Prnb/+Bc11CYtg4e8nOO1vaZk8tDQlEz/Mdu7eMuozm7/3AEgfypf/sWVUVsgWVSZVXBqt04WL1lItaOd/pAoAOyt2eEdnz/rbGozvZSHBwdtiGrRsKAuX8Nkw/5nR9p5+LgoL6ranJ8Rnd05twXAuZ7xPYh/fhdC/AGgb9AJEQhydiECQc4uRCDI2YUIBDm7EIFgzo2tNtmYTmbWCmBf4tciAP6ibslF8/gymseX+UObx1znnPfba0l19i+d2KzaOcdzGjUPzUPzOKXz0Nt4IQJBzi5EIEyms6+ZxHOfiObxZTSPL/NvZh6T9pldCJFc9DZeiECYFGc3s2vM7Asz22VmD0zGHBLzqDOzGjP7zMyqk3jeJ8ysxcy2njBWYGZvm9nOxE9SmnPC5/GQmR1MrMlnZnZdEuZRambvmlmtmW0zsx8kxpO6JhHzSOqamFmmmX1iZpsT8/iLxPg8M1ufWI9nzSyq4urv45xL6j8AqYiXtaoAkA5gM4AlyZ5HYi51AIom4byXAFgOYOsJY38N4IHE4wcA/NUkzeMhAP81yetRDGB54nEugB0AliR7TSLmkdQ1AWAAchKPYwDWI14w5jkAtyXG/x7A90/muJNxZ18BYJdzbo+Ll55+BsBNkzCPScM59z6Ar9Z8vgnxwp1Akgp4knkkHedco3NuU+JxJ+LFUeYgyWsSMY+k4uKc8iKvk+HscwCc2P5zMotVOgBvmdlGM7tnkuZwnJnOuUYgftEBmDGJc7nPzLYk3uZP+MeJEzGzcsTrJ6zHJK7JV+YBJHlNJqLI62Q4u6/ExmSFBFY555YDuBbAvWZ2ySTN43TiMQDzEe8R0Ajg4WSd2MxyADwP4H7nXEeyzjuKeSR9Tdw4irwyJsPZ6wGUnvA7LVY50TjnGhI/WwC8iMmtvNNsZsUAkPjZMhmTcM41Jy60YQC/QJLWxMxiiDvYU865FxLDSV8T3zwma00S5z7pIq+MyXD2DQAqEzuL6QBuA/BysidhZtlmlnv8MYCrAGyNtppQXka8cCcwiQU8jztXgluQhDUxM0O8hmGtc+6RE6SkrgmbR7LXZMKKvCZrh/Eru43XIb7TuRvAjyZpDhWIRwI2A9iWzHkAeBrxt4MDiL/TuQtAIYB3AOxM/CyYpHn8EkANgC2IO1txEuZxEeJvSbcA+Czx77pkr0nEPJK6JgDORryI6xbEX1j+/IRr9hMAuwD8GkDGyRxX36ATIhD0DTohAkHOLkQgyNmFCAQ5uxCBIGcXIhDk7EIEgpxdiECQswsRCP8PcdOMuQtT878AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ResNet('18').to(device)\n",
    "model.load_state_dict(torch.load('models/resnet18_l2eps=70/50'))\n",
    "# model.load_state_dict(torch.load('model'))\n",
    "model.eval()\n",
    "c = 9\n",
    "# image = torch.Tensor(imageio.imread('output.png')).permute(2, 0, 1).to(device) / 255.\n",
    "for i in range(10):\n",
    "    ima = invertTransform(im[i].view(3,32,32))\n",
    "    eps = 10000\n",
    "    steps = 75\n",
    "    adv = pgd_attack(ima.to(device), c, model, stepsize=2.5 * eps / steps, eps=eps, steps=steps, constraint='l_2').cpu()\n",
    "    plt.imshow(adv.cpu().view(3, 32, 32).permute(1, 2, 0))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\Oliver/.cache\\torch\\hub\\pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 32, 32]) torch.Size([1])\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-201-3611e2d0c2a4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0meps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m5000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0msteps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0madv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpgd_attack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mima\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstepsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2.5\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0meps\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0meps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconstraint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'l_2'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\desktop\\nmep-proj\\adversarial.py\u001b[0m in \u001b[0;36mpgd_attack\u001b[1;34m(input, label, model, stepsize, eps, steps, constraint)\u001b[0m\n\u001b[0;32m     54\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m         \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m         \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mconstraint\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'l_inf'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m             \u001b[0mstep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstepsize\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msign\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[0;32m    193\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[1;33m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m         \"\"\"\n\u001b[1;32m--> 195\u001b[1;33m         \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    196\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\autograd\\__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[0;32m     97\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[0;32m     98\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 99\u001b[1;33m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[0;32m    100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = ResNet('18').to(device)\n",
    "model.load_state_dict(torch.load('models/resnet18_l2eps=50/75'))\n",
    "model.eval()\n",
    "# image = torch.Tensor(imageio.imread('output.png')).permute(2, 0, 1).to(device) / 255.\n",
    "ima = invertTransform(im[6].view(3,32,32))\n",
    "eps = 5000\n",
    "steps = 100\n",
    "adv = pgd_attack(ima.to(device), c, model, stepsize=2.5 * eps / steps, eps=eps, steps=steps, constraint='l_2').cpu()\n",
    "plt.imshow(adv.cpu().view(3, 32, 32).permute(1, 2, 0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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