Certified Explanation-Cifar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b413da50",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bhui/miniconda3/envs/xy_py38/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from models import *\n",
    "import torchvision\n",
    "from torchvision import transforms, datasets\n",
    "from torch.distributions import normal\n",
    "import matplotlib.pyplot as plt\n",
    "import captum\n",
    "from captum.attr import DeepLift, IntegratedGradients, Saliency, NoiseTunnel, GradientShap\n",
    "from captum.attr import visualization as viz\n",
    "import argparse\n",
    "from utils import get_args\n",
    "import matplotlib\n",
    "from models.model import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f4867ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def attribute_image_features(algorithm, input, **kwargs):\n",
    "    global_model.zero_grad()\n",
    "    tensor_attributions = algorithm.attribute(input,\n",
    "                                              target=labels[ind],\n",
    "                                              **kwargs\n",
    "                                              )\n",
    "    return tensor_attributions\n",
    "\n",
    "def add_gaussian_noise(noise_scale, input):\n",
    "    noise = torch.randn_like(input) * noise_scale\n",
    "    return input + noise\n",
    "\n",
    "def predict_with_randomized_smoothing(noise_scale, model, image, num_samples):\n",
    "    # Expand the single image to a batch\n",
    "    image_batch = image.repeat(num_samples, 1, 1, 1)\n",
    "    # Add Gaussian noise\n",
    "    noisy_images = add_gaussian_noise(noise_scale, image_batch)\n",
    "\n",
    "    # Predict using the model\n",
    "    predictions = model(noisy_images)\n",
    "\n",
    "    # Get the predicted classes for each noisy image\n",
    "    _, predicted_classes = predictions.max(1)\n",
    "    #Result\n",
    "    avg_noisy_image = noisy_images.mean(0, keepdim=True)\n",
    "    prediction = torch.bincount(predicted_classes).argmax().item()\n",
    "    # Return the most frequently predicted class\n",
    "    return avg_noisy_image, predictions, predicted_classes, prediction\n",
    "\n",
    "def show_image(input, noise_scale):\n",
    "    fig, axes = plt.subplots(1, len(noise_scale), figsize=(20, 10))\n",
    "    if not isinstance(axes, (list, np.ndarray)):\n",
    "        axes = [axes]\n",
    "    for idx, noise in enumerate(noise_scale):\n",
    "        avg_noisy_image, predictions, predicted_classes, prediction= predict_with_randomized_smoothing(noise, global_model, input, 100)\n",
    "        print(f'Noise: {noise}, Ground Truth: {ground_truth_prediction}, Noised Result: {prediction}')\n",
    "        img = avg_noisy_image.squeeze().cpu().detach().numpy()\n",
    "        img = (img - img.min()) / (img.max() - img.min())\n",
    "        ax = axes[idx]\n",
    "        ax.imshow(img, cmap='gray')\n",
    "        ax.axis('off')\n",
    "        ax.set_title(f'noise_scale:{noise}')\n",
    "    plt.tight_layout()\n",
    "    # plt.savefig('picture.jpg')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "35c33f6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "args = get_args.get_arg()\n",
    "data_dir = './data/cifar'\n",
    "sigma = 0.01\n",
    "\n",
    "apply_transform = transforms.Compose([transforms.Grayscale(num_output_channels=1),\n",
    "             transforms.ToTensor(),\n",
    "             transforms.Normalize((0.5,), (0.5,))])\n",
    "train_dataset = datasets.CIFAR10(data_dir, train=True, download=True, transform=apply_transform)\n",
    "test_dataset = datasets.CIFAR10(data_dir, train=False, download=True, transform=apply_transform)\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128,\n",
    "                                         shuffle=False, num_workers=2)\n",
    "device = 'cuda'\n",
    "global_model = CNNCifar10()\n",
    "global_model.load_state_dict(torch.load(f'./checkpoints/cifar/{sigma}/best_model_cifar.pth'))\n",
    "global_model.to(device)\n",
    "dataiter = iter(test_loader)\n",
    "images, labels = next(dataiter)\n",
    "images, labels = images.to(device), labels.to(device)\n",
    "classes = (\n",
    "        \"Plane\",\n",
    "        \"Car\",\n",
    "        \"Bird\",\n",
    "        \"Cat\",\n",
    "        \"Deer\",\n",
    "        \"Dog\",\n",
    "        \"Frog\",\n",
    "        \"Horse\",\n",
    "        \"Ship\",\n",
    "        \"Truck\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "7139a3ef-fd5f-4b9d-bbfc-17fc4214329d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noise: 0, Ground Truth: 1, Noised Result: 1\n",
      "Noise: 0.5, Ground Truth: 1, Noised Result: 1\n",
      "Noise: 1, Ground Truth: 1, Noised Result: 1\n",
      "Noise: 1.5, Ground Truth: 1, Noised Result: 1\n",
      "Noise: 2.0, Ground Truth: 1, Noised Result: 1\n",
      "Noise: 2.5, Ground Truth: 1, Noised Result: 1\n",
      "Noise: 3.0, Ground Truth: 1, Noised Result: 1\n",
      "Noise: 3.5, Ground Truth: 1, Noised Result: 1\n",
      "Noise: 4.0, Ground Truth: 1, Noised Result: 9\n",
      "Noise: 4.5, Ground Truth: 1, Noised Result: 1\n",
      "Noise: 5.0, Ground Truth: 1, Noised Result: 1\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = 1\n",
    "input = images[ind].unsqueeze(0)\n",
    "input.requires_grad = True\n",
    "global_model.eval()\n",
    "ground_truth_predictions = global_model(input)\n",
    "_, predicted_classes = ground_truth_predictions.max(1)\n",
    "ground_truth_prediction = torch.bincount(predicted_classes).argmax().item()\n",
    "noise_scale = [0, 0.5, 1, 1.5, 2.0, 2.5, 3.0 ,3.5, 4.0, 4.5, 5.0]\n",
    "show_image(input, noise_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "635f8e46-89e6-4a48-87ad-20ae5274a73d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_image = input.squeeze().cpu().detach()\n",
    "plt.imshow(input_image, cmap='gray' if input_image.ndim == 2 else None)\n",
    "plt.title(f'Ground Truth Prediction: {ground_truth_prediction}')\n",
    "plt.axis('off')  # Hide axis\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "1e1833dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Original Image\n",
      "Predicted: Car  Probability: 0.836014449596405\n"
     ]
    }
   ],
   "source": [
    "saliency = Saliency(global_model)\n",
    "ig = IntegratedGradients(global_model)\n",
    "dl = DeepLift(global_model)\n",
    "nt = NoiseTunnel(ig)\n",
    "print('-------------------')\n",
    "print('Original Image')\n",
    "print('Predicted:', classes[predicted_classes],\n",
    "      ' Probability:', torch.max(F.softmax(ground_truth_predictions, 1)).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "465ebf91-0a0b-40e5-acc3-da7e9da11cdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualization(args, sigma, model, input, noise_scale, method=\"masked_image\", sign=\"positive\"):\n",
    "    num_escape = int(args.num_escape)\n",
    "    avg_noisy_image, predictions, predicted_classes, prediction= predict_with_randomized_smoothing(noise_scale, model, input, 100)\n",
    "    attr_ig, delta = attribute_image_features(ig, avg_noisy_image, baselines=avg_noisy_image * 0, n_steps=1000, return_convergence_delta=True)\n",
    "    grad = attr_ig.reshape(attr_ig.shape[0], -1)\n",
    "    w0 = grad.mean(dim=0)\n",
    "    des, _ = torch.sort(grad, dim=0, descending=True)\n",
    "    e11 = des[num_escape:]\n",
    "    e1 = e11.mean(dim=0)\n",
    "    e21 = des[:-num_escape]\n",
    "    e2 = e21.mean(dim=0)\n",
    "    e = torch.stack((e1, e2), dim=0)\n",
    "    e = torch.abs(torch.sub(w0, e))\n",
    "    e, _ = torch.max(e, dim=0)\n",
    "    milestone = sigma * sigma / 2\n",
    "    m = normal.Normal(0, sigma * sigma)\n",
    "    g_x = torch.zeros(w0.shape[0], 3).to(device)\n",
    "    g_x[:, 0] = 1 - m.cdf(torch.sub(milestone, w0))\n",
    "    g_x[:, 1] = m.cdf(torch.sub(milestone, w0)) - m.cdf(torch.sub(-milestone, w0))\n",
    "    g_x[:, 2] = m.cdf(torch.sub(-milestone, w0))\n",
    "    g_x_p, _ = torch.sort(g_x, dim=1, descending=True)\n",
    "    g_x_p = g_x_p[:, :2]\n",
    "    radius = (sigma / 2 * (m.icdf(g_x_p[:, 0]) - m.icdf(g_x_p[:, 1])))\n",
    "    failed = torch.gt(e, radius)\n",
    "    g_x[failed == True, 0] = 0.0\n",
    "    g_x[failed == True, 1] = 1.0\n",
    "    g_x[failed == True, 2] = 0.0\n",
    "    v_x = torch.argmax(g_x, dim=1)\n",
    "    v_x = F.one_hot(v_x, num_classes=3)\n",
    "    v_x = v_x.float()\n",
    "    v_x[:, 0] = torch.mul(v_x[:, 0], args.gamma)\n",
    "    v_x[:, 1] = torch.mul(v_x[:, 1], 0.0)\n",
    "    v_x[:, 2] = torch.mul(v_x[:, 2], -args.gamma)\n",
    "    v_x = torch.sum(v_x, dim=1).float()\n",
    "    v_x = v_x.reshape(grad.shape).to(device)\n",
    "    grad = v_x\n",
    "    tensor_32x32 = grad.squeeze().cpu().detach().reshape(32, 32)\n",
    "    attr_ig = np.transpose(tensor_32x32.detach().numpy(), (1, 0))\n",
    "    count_ig2 = attr_ig\n",
    "    print('-------------------')\n",
    "    print('Smoothing Image')\n",
    "    print('Predicted:', classes[prediction],\n",
    "          ' Probability:', torch.max(F.softmax(predictions, 1)).item())\n",
    "    attr_ig = attr_ig[:, :, np.newaxis]\n",
    "    avg_noisy_np = avg_noisy_image.squeeze().unsqueeze(-1).cpu().detach().numpy()\n",
    "    _ = viz.visualize_image_attr(attr_ig, avg_noisy_np, method=method, sign=sign,\n",
    "                          show_colorbar=True, title=\"Overlayed Integrated Gradients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "08a329ce-ba93-4b61-a773-a58716310db8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Car  Probability: 0.8378058075904846\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Car  Probability: 0.8534078001976013\n"
     ]
    },
    {
     "data": {
      "image/png": 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rVaumyMjIgPf3JZdcotq1a2vhwoWaOHHiH/4ozptvvqmjR4/qjTfe8DkKK3zqudCJ95MTj6z27Nlz2iP8ku4bf53qLDw1atTQoEGDNGjQIO3evVuNGzfW448/TgjLCa8RlpMHHnhAkZGRGjhwoHJycnwu27dvn+68805VqFDB+/b9Qh06dFDlypU1d+5czZ07V82bN/d5Sik+Pl5t27bV1KlTtXPnziLbLe5jDSVR+Aj4xEe8v/zyi7Kysopdv2PHjqpatarGjh2r5cuX+xwNSlJGRoZiY2M1evRoHTt27KTzrFGjhho1aqRZs2b5PP26ePFiffXVVwH9LP4o/EP++w/Ud+vWTcHBwRoxYkSRowDnnPc2bdq0qapUqaJp06bp+PHj3nXmzJlToqdgCwUHBxfZzquvvlrk9d6TzbdJkyZKS0vTU089VeRpS+m3/R0cHKyMjAwtXLhQP/zwg/fyr7/+WosWLSrRXIcPH669e/dqwIABxd62JTlqKlTc/e7AgQOaMWOGz3odOnRQaGioJk2a5LPuxIkTT7uNku4bf0VFRRW5HfLz833ux9Kvv7MJCQl/+GgcgeOIsJzUrl1bs2bN0i233KL69evrtttuU0pKirKzszV9+nTt3btXL7/8ss+bOaRfj/S6deumV155RYcPH9ZTTz1V5LqfffZZtWrVSvXr19eAAQOUmpqqXbt2adWqVdq2bZvPZ89KqmXLlqpUqZL69Omje++9Vx6PR7Nnzz7pH7XQ0FD16NFDkydPVnBwsHr27OlzeWxsrKZMmaJevXqpcePG6tGjh6pVq6YffvhB//73v3XllVdq8uTJkqQxY8aoc+fOatWqlfr37699+/Zp0qRJqlu3brF/uEpTkyZNJEkPP/ywevToodDQUF133XVKS0vTY489pr/97W/Kzs5W165dFRMToy1btmjBggW64447NGTIEIWFhWn48OG65557dNVVVykzM1PZ2dmaOXOm0tLSSnzuzi5dumjkyJHq16+fWrZsqS+++EJz5swp8rpSWlqaKlasqOeee04xMTGKiopSixYtlJKSohdeeEEdO3ZU3bp11a9fPyUmJmr79u1aunSpYmNj9eabb0qSRowYoXfeeUfp6ekaNGiQjh8/7t3fp3s6X5JuvvlmbdiwQWPGjNGaNWvUo0cPpaSk6PDhw9qwYYNefvllxcTEnPa1O0m65pprFBYWpuuuu04DBw7UoUOHNG3aNMXHx/s80KtWrZqGDBmiMWPGqEuXLurUqZM+++wzvf322z5HzMUJCgoq8b7xR5MmTfTee+9p/PjxSkhIUEpKii655BLVrFlTN954oxo2bKjo6Gi99957Wrt2rZ5++mm/t4FSUh5vVcVv1q9f73r27Olq1KjhQkNDXfXq1V3Pnj3dF198cdIxixcvdpKcx+NxW7duLXadzZs3u969e7vq1au70NBQl5iY6Lp06eLmzZvnXafwIxJr164tMr64j0+sWLHC/elPf3KRkZEuISHBPfjgg27RokVFPpNVaM2aNU6Su+aaa076syxdutRlZGS4uLg4FxER4dLS0lzfvn3dxx9/7LPea6+95i677DIXHh7u6tSp4+bPn+/69Onzhz4+Udxb2JOSkoq85X3UqFEuMTHRBQUFFdknr732mmvVqpWLiopyUVFR7tJLL3V33XWX+/bbb32u45lnnnFJSUkuPDzcNW/e3K1YscI1adLEXXvttT77QpJ79dVXi8wrLy/P/fWvf3U1atRwkZGR7sorr3SrVq1ybdq08fkIhnO/foShTp06LiQkpMhHKT777DPXrVs3V6VKFRceHu6SkpJcZmamW7Jkic91LF++3DVp0sSFhYW51NRU99xzz3k/OlBSy5YtczfeeKP3vh0bG+uaNm3qhg0b5nbu3OmzblJSkuvcuXOx1/PGG2+4Bg0auIiICJecnOzGjh3r/vnPfxa5LfLz892IESO8+6ht27Zuw4YNRW7T4j5HWNJ9U7gP9uzZ4zO2uN+Xb775xrVu3dpFRkZ6P8Jx9OhR98ADD7iGDRu6mJgYFxUV5Ro2bOiysrJKvF9R+jzO+fE8BeCHdevWqVGjRnrxxRfVq1ev8p7OWaWgoEDVqlVTt27dNG3atPKeDmAarxGizEybNk3R0dHq1q1beU+lXOXl5RV5CvnFF1/Uvn37fE6xBqB88BohSt2bb76pr776Ss8//7zuvvtu8yfx/s9//qP77rtPN910k6pUqaJPP/1U06dPV7169XTTTTeV9/QA83hqFKUuOTlZu3btUkZGhmbPnn3aM+ic77Kzs3XvvfdqzZo12rdvnypXrqxOnTrpiSeeUHx8fHlPDzCPEAIATOM1QgCAaYQQAGBaid4sU1BQoB07digmJqbEHwAGAKC8OOeUm5urhIQE7ze3nEyJQrhjxw7VqlWrVCYHAMCZsnXrVtWsWfOU65QohIXv+mulTgpR6B+fGQAAZei4jukjvVWid62XKISFT4eGKFQhHkIIADjL/d/nIUrych5vlgEAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGBaSHlPAPDHtB8+8nvMgAtblcFMYNXsrSsCGter1pWlPBOUFo4IAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCm8e0TwDnqf7et8nvMvw7XDGhbL15SK6BxgTj4dprfY2I7bg5oW6O2rPV7TLgnsOOHmQF8c0qggj0ev8dY/nYMjggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYxkm3z1PTAzjB720XtiqDmZSuAefAHM+Uq9f19nvMTweiAtpWqj4PaFwgdufE+j2mwZrIgLY1NKVZQONwfuGIEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGt8+cZabs3VFQONuqXV+fkvD3zav93vMLy44oG09fVHdgMadKZU6b/R7zKGhLctgJqUr4bUwv8esvaBRQNuKfXen32PCr8kOaFv4zYJtawIad0PN5qU8k19xRAgAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjRACAEwjhAAA0wghAMA0Trp9lqvgCS3vKZzWa9v+4/eY6KCIgLaVkdDI7zF//ionoG2dj1yD3PKewmlVWLDa/zEBbiu7XQO/x6zZtiqgbWXWvCKgcWdKIL/HkvT/av7J7zFldfLsQHFECAAwjRACAEwjhAAA0wghAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjW+fOMudbWdpL05QAI+n8l1BGcykeG/UqXLGtnUmXfaJ/7++d8W+FtC2ntXFAY07281tPq28p3DWCORbJM4XHBECAEwjhAAA0wghAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjZNuw2v21hUBjTsWwOOpGxLP/pOJnymjt6wJaFy+PH6PGZrSLKBtna96r+vr95hDW2MD2lZtrQ5oXCC2vNzQ7zEpPdeVwUzODRwRAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjRACAEwjhAAA0wghAMA0QggAMI0QAgBM49sn4JXnXEDjetW6opRncu6a9sNHfo8JDXBboR7/v30Cvmp0/bq8p1Amqr4eeca29db2T/0e0ymxcRnMJHAcEQIATCOEAADTCCEAwDRCCAAwjRACAEwjhAAA0wghAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADDN49zpz7R88OBBxcXFqa2uV4gn0FME42z3569yAhpXJfiQ32NmXJIU0LbORxOzVwY0bnByy1Keydlh44v+n5A5JDQ/oG2l9FwX0LgzJb9dYCenDl7q/4mwzzfH3TEt0+s6cOCAYmNjT7kuR4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjRACAEwjhAAA0wghAMA0QggAMI0QAgBMI4QAANNCynsCOHus2p8W0LjVH1zm95hUrQpoW+eje/reHdC4YJ2f3zDwavpzfo/5e0rzMphJ6Upfn+f3mA8bnP238d82r/d7TPNw//eFJN1Qs2xuZ44IAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmMZJt+G1p+X+gMZVGOwp3YkYE7wssBMr776rpd9j4p9dGdC2zqRz4QTagTiSH1beUziljZNaBDRuwOpGfo9JvfnzgLZVVjgiBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYxrdPwCt9fV5A4+a8VsoTQYncd8//+j1mzrM1y2Am566Qmol+jzm+bXtA2/rk8jN33BF8cZrfYy59bEtA28rftTugcWcTjggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYxkm34TX941YBjbt4xMpSnglKYs6lnED7jwr0BNpnu2MXxPo9Jui/m8tgJucGjggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYxkm34XVx/4/Lewrww/Grmvg9JuT9T8pgJjjbBH34WXlP4ZzCESEAwDRCCAAwjRACAEwjhAAA0wghAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDS+feIsd8d/vwto3PMXp5byTHC22X7nL36Paf54bEDb2nXFwYDGAecCjggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYxkm3z3LTtqUHNO74e8F+jzlyLDSgbcV23BzQuED8d3pTv8dcfNvHZTCT8he+MsbvMbsmcvJs4Pc4IgQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmMa3T5zl3FXbAxoXyA0b+6cGAW1LAYwrCPX/2zEkqfb0YwGNOx8lzvzS7zH5ZTAP4FzHESEAwDRCCAAwjRACAEwjhAAA0wghAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTOOk2fvOf9WdsU4E+AgupfoHfY44HuK2zXf7+A+U9BeC8wBEhAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjRACAEwjhAAA0wghAMA0vn0C55TjP+4q7ykAOM9wRAgAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjRACAEwjhAAA0wghAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjRACAEwjhAAA0wghAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjRACAEwjhAAA0wghAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjRACAEwjhAAA0wghAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADAtpLwnAOD85AkPD2icO3q0lGcCnBpHhAAA0wghAMA0QggAMI0QAgBMI4QAANMIIQDANEIIADCNEAIATCOEAADTCCEAwDRCCAAwjRACAEzjpNsAygQnz8a5giNCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmBZSkpWcc5Kk4zomuTKdDwAAf9hxHZP0W79OpUQhzM3NlSR9pLf+wLQAADizcnNzFRcXd8p1PK4EuSwoKNCOHTsUExMjj8dTahMEAKAsOOeUm5urhIQEBQWd+lXAEoUQAIDzFW+WAQCYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaYQQAGAaIQQAmEYIAQCmEUIAgGmEEABgGiEEAJhGCAEAphFCAIBphBAAYBohBACYRggBAKYRQgCAaf8fMjMaEEDv2PsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Car  Probability: 0.9529780149459839\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Car  Probability: 0.9971339702606201\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Car  Probability: 0.9998134970664978\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAAH4CAYAAADZ+e2OAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAebUlEQVR4nO3deXRU9f3/8dckZCMbW1JIoNmkKrssUpEQEDSyWClHA6isinhwKVq09utCAAURBSoYRKSAkaMcEVBbFREBFS1iVRC3A0jcQCAgGMGgkPfvD38ZGZNAUDRt38/HOf6RO5+595M7wzznzp0bA2ZmAgDAqbCangAAADWJEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QolLz589XIBBQUVFRTU+lSkOHDlV6enpNT8ON/Px8BQKBmp7GCUtPT9fQoUODP69evVqBQECrV6+usTnhPwshrGHvvvuuLrvsMqWmpioqKkopKSm69NJL9e6779b01P5ndO3aVS1atPhJ93311VeVn5+vffv2ndxJ/UK2b9+u/Px8vf322zU9Fb388svKy8tTamqqIiMjlZiYqI4dO2r8+PHauXNnTU/vFzdx4kQtW7aspqeBaiCENWjJkiVq27atVq5cqWHDhqmgoECXX365Vq1apbZt22rp0qU1PUX3Xn31VY0bN+6/KoTjxo2r8RDefvvt6tKli/79739r6NChmjVrliZOnKjmzZvr3nvvVadOnWpsbl26dNE333yjLl26/KLbIYT/PWrV9AS82rp1qwYNGqTMzEy99NJLSkpKCt72pz/9SdnZ2Ro0aJA2btyozMzMX21eBw4cUGxs7K+2PRxbaWmpIiMjFRb23/OeddGiRZowYYLy8vJUWFioyMjIkNunTZumadOmHXMdZqbS0lLFxMSc9PmFhYUpOjr6pK8X/73+e/51/Y+ZMmWKDh48qAcffDAkgpLUoEEDzZ49WwcOHNDdd98tSVq8eLECgYDWrFlTYV2zZ89WIBDQpk2bgss++OADXXTRRapXr56io6PVvn17PfXUUyH3Kz8PuGbNGo0aNUrJyclq3LhxlXN+8skn1bt3b6WkpCgqKkpZWVmaMGGCjhw5EhwzduxYRUREaPfu3RXuf+WVV6pOnToqLS0NLnv22WeVnZ2t2NhYxcfHq3fv3pV+LLxs2TK1aNFC0dHRatGixc8+Wg4EArrmmmuC642KilLz5s313HPPBcfk5+frxhtvlCRlZGQoEAhUOG/6yCOPqF27doqJiVG9evU0YMAAffrppxW2d//99yszM1MxMTE688wz9fLLL6tr167q2rVrcEz5uavHHntMt956q1JTU1W7dm199dVX2rt3r8aMGaOWLVsqLi5OCQkJ6tmzpzZs2BBy/w4dOkiShg0bFpzv/Pnzg2PWrVun888/X4mJiapdu7ZycnK0du3aCvN95ZVX1KFDB0VHRysrK0uzZ8+u9r69/fbb1aBBA82dO7dCBCUpMTFR+fn5IcvS09PVp08fLV++XO3bt1dMTExwm/PmzdM555yj5ORkRUVFqVmzZpo1a1aF9ZqZ7rjjDjVu3Fi1a9dWt27dKn0uVXWOsDr7pvw86ZYtWzR06FDVqVNHiYmJGjZsmA4ePBgcFwgEdODAAS1YsCD4OJSfpywpKdHo0aOVnp6uqKgoJScn69xzz9Wbb75Znd2LX4KhRqSkpFh6evoxx6Snp1vjxo3NzOzgwYMWFxdno0aNqjCuW7du1rx58+DPmzZtssTERGvWrJlNnjzZZs6caV26dLFAIGBLliwJjps3b55JsmbNmllOTo7NmDHD7rrrrpDbtm3bFhzft29fy8vLsylTptisWbPs4osvNkk2ZsyY4JjNmzebJJsxY0bIHA8dOmR169a14cOHB5c9/PDDFggE7Pzzz7cZM2bY5MmTLT093erUqROy3eXLl1tYWJi1aNHCpk6darfccoslJiZa8+bNLS0t7Zj70MwsJycnZP+YmUmy1q1bW6NGjWzChAk2ffp0y8zMtNq1a1txcbGZmW3YsMEGDhxokmzatGlWWFhohYWF9vXXX5uZ2R133GGBQMD69+9vBQUFNm7cOGvQoIGlp6fbl19+GdxWQUGBSbLs7Gy777777IYbbrB69epZVlaW5eTkBMetWrUq+Hi0adPGpk6dapMmTbIDBw7Y+vXrLSsry26++WabPXu2jR8/3lJTUy0xMdE+//xzMzP74osvbPz48SbJrrzyyuB8t27damZmK1eutMjISDvrrLPs3nvvtWnTplmrVq0sMjLS1q1bF5zHxo0bLSYmxn7729/apEmTbMKECfab3/zGWrVqZcd7yfjwww9Nkl1xxRXHfVyOlpaWZqeccorVrVvXbr75ZnvggQds1apVZmbWoUMHGzp0qE2bNs1mzJhh5513nkmymTNnhqzj1ltvNUnWq1cvmzlzpg0fPtxSUlKsQYMGNmTIkAr7uXz9J7Jvxo4da5LsjDPOsH79+llBQYFdccUVJsluuumm4LjCwkKLioqy7Ozs4OPw6quvmpnZJZdcYpGRkXbDDTfYQw89ZJMnT7YLLrjAHnnkkRPaZzh5CGEN2Ldvn0myCy+88Jjj/vCHP5gk++qrr8zMbODAgZacnGyHDx8OjtmxY4eFhYXZ+PHjg8u6d+9uLVu2tNLS0uCysrIy69SpkzVt2jS4rDx2nTt3Dlnn0bcdHaSDBw9WmOPIkSOtdu3aIds666yzrGPHjiHjlixZEvLiU1JSYnXq1LERI0aEjPviiy8sMTExZHmbNm2sUaNGtm/fvuCy559/3iT9rBBGRkbali1bgss2bNhQIeJTpkypsB/MzIqKiiw8PNzuvPPOkOXvvPOO1apVK7j80KFDVr9+fevQoYN99913wXHz5883SZWGMDMzs8K+Li0ttSNHjoQs27Ztm0VFRYU89uvXrzdJNm/evJCxZWVl1rRpU8vNzbWysrLg8oMHD1pGRoade+65wWV9+/a16Oho+/jjj4PL3nvvPQsPDz9uCJ988kmTZNOnT6+w/d27d4f8d/T+SEtLM0n23HPPVVhnZc+73Nxcy8zMDP68a9cui4yMtN69e4f8fv/3f/9nko4ZwhPZN+UhPPoNnZnZH//4R6tfv37IstjY2JDtlktMTLSrr766wnLUHD4arQElJSWSpPj4+GOOK7/9q6++kiT1799fu3btCvlIZ/HixSorK1P//v0lSXv37tWLL76ovLw8lZSUqLi4WMXFxdqzZ49yc3O1efNmff755yHbGTFihMLDw48776PP15SvOzs7WwcPHtQHH3wQvG3w4MFat26dtm7dGly2cOFCNWnSRDk5OZKkFStWaN++fRo4cGBwjsXFxQoPD1fHjh21atUqSdKOHTv09ttva8iQIUpMTAyu79xzz1WzZs2OO+dj6dGjh7KysoI/t2rVSgkJCfroo4+Oe98lS5aorKxMeXl5IfNv2LChmjZtGpz/G2+8oT179mjEiBGqVeuHU/KXXnqp6tatW+m6hwwZUuHcWFRUVPA84ZEjR7Rnzx7FxcXp1FNPrdZHam+//bY2b96sSy65RHv27AnO98CBA+revbteeukllZWV6ciRI1q+fLn69u2r3/72t8H7n3766crNzT3udsqfq3FxcSHL9+/fr6SkpJD/fvyFnoyMjEq3cfS+2L9/v4qLi5WTk6OPPvpI+/fvlyS98MIL+vbbb3XttdeGXOIxevTok7ZvjnbVVVeF/Jydna09e/YEf/9jqVOnjtatW6ft27cfdyx+HXxZpgaUB648iFX5cTDLz18sWrRI3bt3l/T9FxPatGmj3/3ud5KkLVu2yMx022236bbbbqt0vbt27VJqamrw54yMjGrN+91339Wtt96qF198scI/+PIXJOn7YI8ePVoLFy7U7bffrv379+sf//iHrr/++uCL1ObNmyVJ55xzTqXbSkhIkCR9/PHHkqSmTZtWGFPdCFTl6Bf6cnXr1tWXX3553Ptu3rxZZlbpvCQpIiJC0g/zP+WUU0Jur1WrVpXXQFb2eJSVlelvf/ubCgoKtG3btpDzsvXr16/WfKXvI1uV/fv369ChQ/rmm2+q3N/PPPPMMbdT/lz9+uuvQ5bHxcVpxYoVkqTnn39eU6ZMqXDfqp6Ha9eu1dixY/Xaa6+FnIcrn3NiYmKVz5OkpKQq33CUq+6+OXo9P37ulN/25ZdfBp+7Vbn77rs1ZMgQNWnSRO3atVOvXr00ePDgX/VLcQhFCGtAYmKiGjVqpI0bNx5z3MaNG5Wamhr8hxUVFaW+fftq6dKlKigo0M6dO7V27VpNnDgxeJ/yd65jxoyp8h38j1+Uq/PNvH379iknJ0cJCQkaP368srKyFB0drTfffFN/+ctfQt4x161bV3369AmGcPHixTp06JAuu+yyCvMsLCxUw4YNK2zv6KOnX0pVR8Fmdtz7lpWVKRAI6Nlnn610PT8+IjoRlT0eEydO1G233abhw4drwoQJqlevnsLCwjR69OgKRytVzVf6/ktabdq0qXRMXFycDh069JPnLUmnnXaaJIV8cUv6/vHs0aOHJOmzzz6r9L6V/d5bt25V9+7dddppp2nq1Klq0qSJIiMj9cwzz2jatGnV+t2Pp7r75mg/57mTl5en7OxsLV26NPimYPLkyVqyZIl69ux5YpPHSUEIa0ifPn00Z84cvfLKK+rcuXOF219++WUVFRVp5MiRIcv79++vBQsWaOXKlXr//fdlZsGPRSUF31VGREQEX3hOhtWrV2vPnj1asmRJyPVX27Ztq3T84MGDdeGFF2r9+vVauHChzjjjDDVv3jx4e/lHksnJycecZ1pamqQf3rUf7cMPP/xJv8uJqOovqWRlZcnMlJGRETwar0z5/Lds2aJu3boFlx8+fFhFRUVq1apVteaxePFidevWTXPnzg1Zvm/fPjVo0KBa85W+P9I+1v5OSkpSTEzMT97fp556qpo2baply5Zp+vTpP/tSnKefflqHDh3SU089FXIUVv7Rc7mjnydHH1nt3r37uEf41d03J+pYf4WnUaNGGjVqlEaNGqVdu3apbdu2uvPOOwlhDeEcYQ258cYbFRMTo5EjR2rPnj0ht+3du1dXXXWVateuHfz6frkePXqoXr16WrRokRYtWqQzzzwz5COl5ORkde3aVbNnz9aOHTsqbLeyyxqqo/wd8NHveL/99lsVFBRUOr5nz55q0KCBJk+erDVr1oQcDUpSbm6uEhISNHHiRH333XdVzrNRo0Zq06aNFixYEPLx64oVK/Tee+/9pN/lRJS/kP/4gvp+/fopPDxc48aNq3AUYGbBx7R9+/aqX7++5syZo8OHDwfHLFy4sFofwZYLDw+vsJ3HH3+8wvnequbbrl07ZWVl6Z577qnwsaX0w/4ODw9Xbm6uli1bpk8++SR4+/vvv6/ly5dXa675+fkqLi7WiBEjKn1sq3PUVK6y593+/fs1b968kHE9evRQRESEZsyYETJ2+vTpx91GdffNiYqNja3wOBw5ciTkeSx9/282JSXlZx+N46fjiLCGNG3aVAsWLNCll16qli1b6vLLL1dGRoaKioo0d+5cFRcX69FHHw35Mof0/ZFev3799Nhjj+nAgQO65557Kqz7/vvvV+fOndWyZUuNGDFCmZmZ2rlzp1577TV99tlnIdeeVVenTp1Ut25dDRkyRNddd50CgYAKCwurfFGLiIjQgAEDNHPmTIWHh2vgwIEhtyckJGjWrFkaNGiQ2rZtqwEDBigpKUmffPKJ/vnPf+rss8/WzJkzJUmTJk1S79691blzZw0fPlx79+7VjBkz1Lx580pfuE6mdu3aSZJuueUWDRgwQBEREbrggguUlZWlO+64Q3/9619VVFSkvn37Kj4+Xtu2bdPSpUt15ZVXasyYMYqMjFR+fr6uvfZanXPOOcrLy1NRUZHmz5+vrKysav/tzj59+mj8+PEaNmyYOnXqpHfeeUcLFy6scF4pKytLderU0QMPPKD4+HjFxsaqY8eOysjI0EMPPaSePXuqefPmGjZsmFJTU/X5559r1apVSkhI0NNPPy1JGjdunJ577jllZ2dr1KhROnz4cHB/H+/jfEm65JJLtGnTJk2aNEmvv/66BgwYoIyMDB04cECbNm3So48+qvj4+OOeu5Ok8847T5GRkbrgggs0cuRIff3115ozZ46Sk5ND3uglJSVpzJgxmjRpkvr06aNevXrprbfe0rPPPhtyxFyZsLCwau+bE9GuXTu98MILmjp1qlJSUpSRkaFTTz1VjRs31kUXXaTWrVsrLi5OL7zwgtavX6977733hLeBk6QmvqqKH2zcuNEGDhxojRo1soiICGvYsKENHDjQ3nnnnSrvs2LFCpNkgUDAPv3000rHbN261QYPHmwNGza0iIgIS01NtT59+tjixYuDY8ovkVi/fn2F+1d2+cTatWvt97//vcXExFhKSorddNNNtnz58grXZJV7/fXXTZKdd955Vf4uq1atstzcXEtMTLTo6GjLysqyoUOH2htvvBEy7oknnrDTTz/doqKirFmzZrZkyRIbMmTIz7p8orKvsKelpVX4yvuECRMsNTXVwsLCKuyTJ554wjp37myxsbEWGxtrp512ml199dX24Ycfhqzjvvvus7S0NIuKirIzzzzT1q5da+3atbPzzz8/ZF9Isscff7zCvEpLS+3Pf/6zNWrUyGJiYuzss8+21157zXJyckIuwTD7/hKGZs2aWa1atSpcSvHWW29Zv379rH79+hYVFWVpaWmWl5dnK1euDFnHmjVrrF27dhYZGWmZmZn2wAMPBC8dqK7Vq1fbRRddFHxuJyQkWPv27W3s2LG2Y8eOkLFpaWnWu3fvStfz1FNPWatWrSw6OtrS09Nt8uTJ9ve//73CY3HkyBEbN25ccB917drVNm3aVOExrew6wurum/J9sHv37pD7Vvbv5YMPPrAuXbpYTExM8BKOQ4cO2Y033mitW7e2+Ph4i42NtdatW1tBQUG19ytOvoDZCXxOAZyADRs2qE2bNnr44Yc1aNCgmp7Of5SysjIlJSWpX79+mjNnTk1PB3CNc4T4xcyZM0dxcXHq169fTU+lRpWWllb4CPnhhx/W3r17Q/7EGoCawTlCnHRPP/203nvvPT344IO65ppr3P8R73/961+6/vrrdfHFF6t+/fp68803NXfuXLVo0UIXX3xxTU8PcI+PRnHSpaena+fOncrNzVVhYeFx/4LO/7qioiJdd911ev3117V3717Vq1dPvXr10l133aXk5OSanh7gHiEEALjGOUIAgGuEEADgWrW+LFNWVqbt27crPj6+2hcAAwBQU8xMJSUlSklJCf6fW6pSrRBu375dTZo0OSmTAwDg1/Lpp5+qcePGxxxTrRCWf+uvs3qpliJ+/swAAPgFHdZ3ekXPVOtb69UKYfnHobUUoVoBQggA+A/3/6+HqM7pPL4sAwBwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcq1WdQWYmSTqs7yT7RecDAMDPdljfSfqhX8dSrRCWlJRIkl7RMz9jWgAA/LpKSkqUmJh4zDEBq0Yuy8rKtH37dsXHxysQCJy0CQIA8EswM5WUlCglJUVhYcc+C1itEAIA8L+KL8sAAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMC1/wdYQC2Px4iuFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Car  Probability: 0.9991637468338013\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Car  Probability: 0.9999403953552246\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Car  Probability: 0.9999995231628418\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(args=args, sigma=0.5, model=global_model, input=input, noise_scale=0.001,\n",
    "                           method='masked_image', sign='positive')\n",
    "visualization(args=args, sigma=0.5, model=global_model, input=input, noise_scale=0.01,\n",
    "                           method='masked_image', sign='positive')\n",
    "visualization(args=args, sigma=0.5, model=global_model, input=input, noise_scale=0.1,\n",
    "                           method='masked_image', sign='positive')\n",
    "visualization(args=args, sigma=0.5, model=global_model, input=input, noise_scale=0.5,\n",
    "                           method='masked_image', sign='positive')\n",
    "visualization(args=args, sigma=0.5, model=global_model, input=input, noise_scale=0.9,\n",
    "                           method='masked_image', sign='positive')\n",
    "visualization(args=args, sigma=0.5, model=global_model, input=input, noise_scale=1.0,\n",
    "                           method='masked_image', sign='positive')\n",
    "visualization(args=args, sigma=0.5, model=global_model, input=input, noise_scale=1.5,\n",
    "                           method='masked_image', sign='positive')\n",
    "visualization(args=args, sigma=0.5, model=global_model, input=input, noise_scale=2.0,\n",
    "                           method='masked_image', sign='positive')\n",
    "#Noise0.001_Confi0.83_Cifar10_1.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42ad3b0f-0ee7-409d-ab5b-929d1715ca8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "518077c2-cd1a-43a5-90b3-ea517a2b20bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: 4  Probability: 0.998688280582428\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(args=args, sigma=0.01, model=global_model, input=input, noise_scale=0.01,\n",
    "                           method='masked_image', sign='positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce20f1ca-dc74-4eee-8295-4007a7080bf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: 4  Probability: 0.9987032413482666\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(args=args, sigma=0.5, model=global_model, input=input, noise_scale=0.01,\n",
    "                           method='masked_image', sign='positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad962869",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Shirt  Probability: 0.9690296649932861\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantization_visualization(args=args, sigma=sigma, model=global_model, input=input, noise_scale=0.5,\n",
    "                           method='original_image', sign='positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3af0c7a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Shirt  Probability: 0.9950793981552124\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantization_visualization(args=args, sigma=sigma, model=global_model, input=input, noise_scale=0.9,\n",
    "                           method='masked_image', sign='positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a6fffa5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Shirt  Probability: 0.9999667406082153\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantization_visualization(args=args, sigma=sigma, model=global_model, input=input, noise_scale=1.0,\n",
    "                           method='masked_image', sign='positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "86d8d35e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Sandal  Probability: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantization_visualization(args=args, sigma=sigma, model=global_model, input=input, noise_scale=1.5,\n",
    "                           method='masked_image', sign='positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "aa15128d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------\n",
      "Smoothing Image\n",
      "Predicted: Sandal  Probability: 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAAH4CAYAAADZ+e2OAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAebUlEQVR4nO3deXRU9f3/8dckZCMbW1JIoNmkKrssUpEQEDSyWClHA6isinhwKVq09utCAAURBSoYRKSAkaMcEVBbFREBFS1iVRC3A0jcQCAgGMGgkPfvD38ZGZNAUDRt38/HOf6RO5+595M7wzznzp0bA2ZmAgDAqbCangAAADWJEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QolLz589XIBBQUVFRTU+lSkOHDlV6enpNT8ON/Px8BQKBmp7GCUtPT9fQoUODP69evVqBQECrV6+usTnhPwshrGHvvvuuLrvsMqWmpioqKkopKSm69NJL9e6779b01P5ndO3aVS1atPhJ93311VeVn5+vffv2ndxJ/UK2b9+u/Px8vf322zU9Fb388svKy8tTamqqIiMjlZiYqI4dO2r8+PHauXNnTU/vFzdx4kQtW7aspqeBaiCENWjJkiVq27atVq5cqWHDhqmgoECXX365Vq1apbZt22rp0qU1PUX3Xn31VY0bN+6/KoTjxo2r8RDefvvt6tKli/79739r6NChmjVrliZOnKjmzZvr3nvvVadOnWpsbl26dNE333yjLl26/KLbIYT/PWrV9AS82rp1qwYNGqTMzEy99NJLSkpKCt72pz/9SdnZ2Ro0aJA2btyozMzMX21eBw4cUGxs7K+2PRxbaWmpIiMjFRb23/OeddGiRZowYYLy8vJUWFioyMjIkNunTZumadOmHXMdZqbS0lLFxMSc9PmFhYUpOjr6pK8X/73+e/51/Y+ZMmWKDh48qAcffDAkgpLUoEEDzZ49WwcOHNDdd98tSVq8eLECgYDWrFlTYV2zZ89WIBDQpk2bgss++OADXXTRRapXr56io6PVvn17PfXUUyH3Kz8PuGbNGo0aNUrJyclq3LhxlXN+8skn1bt3b6WkpCgqKkpZWVmaMGGCjhw5EhwzduxYRUREaPfu3RXuf+WVV6pOnToqLS0NLnv22WeVnZ2t2NhYxcfHq3fv3pV+LLxs2TK1aNFC0dHRatGixc8+Wg4EArrmmmuC642KilLz5s313HPPBcfk5+frxhtvlCRlZGQoEAhUOG/6yCOPqF27doqJiVG9evU0YMAAffrppxW2d//99yszM1MxMTE688wz9fLLL6tr167q2rVrcEz5uavHHntMt956q1JTU1W7dm199dVX2rt3r8aMGaOWLVsqLi5OCQkJ6tmzpzZs2BBy/w4dOkiShg0bFpzv/Pnzg2PWrVun888/X4mJiapdu7ZycnK0du3aCvN95ZVX1KFDB0VHRysrK0uzZ8+u9r69/fbb1aBBA82dO7dCBCUpMTFR+fn5IcvS09PVp08fLV++XO3bt1dMTExwm/PmzdM555yj5ORkRUVFqVmzZpo1a1aF9ZqZ7rjjDjVu3Fi1a9dWt27dKn0uVXWOsDr7pvw86ZYtWzR06FDVqVNHiYmJGjZsmA4ePBgcFwgEdODAAS1YsCD4OJSfpywpKdHo0aOVnp6uqKgoJScn69xzz9Wbb75Znd2LX4KhRqSkpFh6evoxx6Snp1vjxo3NzOzgwYMWFxdno0aNqjCuW7du1rx58+DPmzZtssTERGvWrJlNnjzZZs6caV26dLFAIGBLliwJjps3b55JsmbNmllOTo7NmDHD7rrrrpDbtm3bFhzft29fy8vLsylTptisWbPs4osvNkk2ZsyY4JjNmzebJJsxY0bIHA8dOmR169a14cOHB5c9/PDDFggE7Pzzz7cZM2bY5MmTLT093erUqROy3eXLl1tYWJi1aNHCpk6darfccoslJiZa8+bNLS0t7Zj70MwsJycnZP+YmUmy1q1bW6NGjWzChAk2ffp0y8zMtNq1a1txcbGZmW3YsMEGDhxokmzatGlWWFhohYWF9vXXX5uZ2R133GGBQMD69+9vBQUFNm7cOGvQoIGlp6fbl19+GdxWQUGBSbLs7Gy777777IYbbrB69epZVlaW5eTkBMetWrUq+Hi0adPGpk6dapMmTbIDBw7Y+vXrLSsry26++WabPXu2jR8/3lJTUy0xMdE+//xzMzP74osvbPz48SbJrrzyyuB8t27damZmK1eutMjISDvrrLPs3nvvtWnTplmrVq0sMjLS1q1bF5zHxo0bLSYmxn7729/apEmTbMKECfab3/zGWrVqZcd7yfjwww9Nkl1xxRXHfVyOlpaWZqeccorVrVvXbr75ZnvggQds1apVZmbWoUMHGzp0qE2bNs1mzJhh5513nkmymTNnhqzj1ltvNUnWq1cvmzlzpg0fPtxSUlKsQYMGNmTIkAr7uXz9J7Jvxo4da5LsjDPOsH79+llBQYFdccUVJsluuumm4LjCwkKLioqy7Ozs4OPw6quvmpnZJZdcYpGRkXbDDTfYQw89ZJMnT7YLLrjAHnnkkRPaZzh5CGEN2Ldvn0myCy+88Jjj/vCHP5gk++qrr8zMbODAgZacnGyHDx8OjtmxY4eFhYXZ+PHjg8u6d+9uLVu2tNLS0uCysrIy69SpkzVt2jS4rDx2nTt3Dlnn0bcdHaSDBw9WmOPIkSOtdu3aIds666yzrGPHjiHjlixZEvLiU1JSYnXq1LERI0aEjPviiy8sMTExZHmbNm2sUaNGtm/fvuCy559/3iT9rBBGRkbali1bgss2bNhQIeJTpkypsB/MzIqKiiw8PNzuvPPOkOXvvPOO1apVK7j80KFDVr9+fevQoYN99913wXHz5883SZWGMDMzs8K+Li0ttSNHjoQs27Ztm0VFRYU89uvXrzdJNm/evJCxZWVl1rRpU8vNzbWysrLg8oMHD1pGRoade+65wWV9+/a16Oho+/jjj4PL3nvvPQsPDz9uCJ988kmTZNOnT6+w/d27d4f8d/T+SEtLM0n23HPPVVhnZc+73Nxcy8zMDP68a9cui4yMtN69e4f8fv/3f/9nko4ZwhPZN+UhPPoNnZnZH//4R6tfv37IstjY2JDtlktMTLSrr766wnLUHD4arQElJSWSpPj4+GOOK7/9q6++kiT1799fu3btCvlIZ/HixSorK1P//v0lSXv37tWLL76ovLw8lZSUqLi4WMXFxdqzZ49yc3O1efNmff755yHbGTFihMLDw48776PP15SvOzs7WwcPHtQHH3wQvG3w4MFat26dtm7dGly2cOFCNWnSRDk5OZKkFStWaN++fRo4cGBwjsXFxQoPD1fHjh21atUqSdKOHTv09ttva8iQIUpMTAyu79xzz1WzZs2OO+dj6dGjh7KysoI/t2rVSgkJCfroo4+Oe98lS5aorKxMeXl5IfNv2LChmjZtGpz/G2+8oT179mjEiBGqVeuHU/KXXnqp6tatW+m6hwwZUuHcWFRUVPA84ZEjR7Rnzx7FxcXp1FNPrdZHam+//bY2b96sSy65RHv27AnO98CBA+revbteeukllZWV6ciRI1q+fLn69u2r3/72t8H7n3766crNzT3udsqfq3FxcSHL9+/fr6SkpJD/fvyFnoyMjEq3cfS+2L9/v4qLi5WTk6OPPvpI+/fvlyS98MIL+vbbb3XttdeGXOIxevTok7ZvjnbVVVeF/Jydna09e/YEf/9jqVOnjtatW6ft27cfdyx+HXxZpgaUB648iFX5cTDLz18sWrRI3bt3l/T9FxPatGmj3/3ud5KkLVu2yMx022236bbbbqt0vbt27VJqamrw54yMjGrN+91339Wtt96qF198scI/+PIXJOn7YI8ePVoLFy7U7bffrv379+sf//iHrr/++uCL1ObNmyVJ55xzTqXbSkhIkCR9/PHHkqSmTZtWGFPdCFTl6Bf6cnXr1tWXX3553Ptu3rxZZlbpvCQpIiJC0g/zP+WUU0Jur1WrVpXXQFb2eJSVlelvf/ubCgoKtG3btpDzsvXr16/WfKXvI1uV/fv369ChQ/rmm2+q3N/PPPPMMbdT/lz9+uuvQ5bHxcVpxYoVkqTnn39eU6ZMqXDfqp6Ha9eu1dixY/Xaa6+FnIcrn3NiYmKVz5OkpKQq33CUq+6+OXo9P37ulN/25ZdfBp+7Vbn77rs1ZMgQNWnSRO3atVOvXr00ePDgX/VLcQhFCGtAYmKiGjVqpI0bNx5z3MaNG5Wamhr8hxUVFaW+fftq6dKlKigo0M6dO7V27VpNnDgxeJ/yd65jxoyp8h38j1+Uq/PNvH379iknJ0cJCQkaP368srKyFB0drTfffFN/+ctfQt4x161bV3369AmGcPHixTp06JAuu+yyCvMsLCxUw4YNK2zv6KOnX0pVR8Fmdtz7lpWVKRAI6Nlnn610PT8+IjoRlT0eEydO1G233abhw4drwoQJqlevnsLCwjR69OgKRytVzVf6/ktabdq0qXRMXFycDh069JPnLUmnnXaaJIV8cUv6/vHs0aOHJOmzzz6r9L6V/d5bt25V9+7dddppp2nq1Klq0qSJIiMj9cwzz2jatGnV+t2Pp7r75mg/57mTl5en7OxsLV26NPimYPLkyVqyZIl69ux5YpPHSUEIa0ifPn00Z84cvfLKK+rcuXOF219++WUVFRVp5MiRIcv79++vBQsWaOXKlXr//fdlZsGPRSUF31VGREQEX3hOhtWrV2vPnj1asmRJyPVX27Ztq3T84MGDdeGFF2r9+vVauHChzjjjDDVv3jx4e/lHksnJycecZ1pamqQf3rUf7cMPP/xJv8uJqOovqWRlZcnMlJGRETwar0z5/Lds2aJu3boFlx8+fFhFRUVq1apVteaxePFidevWTXPnzg1Zvm/fPjVo0KBa85W+P9I+1v5OSkpSTEzMT97fp556qpo2baply5Zp+vTpP/tSnKefflqHDh3SU089FXIUVv7Rc7mjnydHH1nt3r37uEf41d03J+pYf4WnUaNGGjVqlEaNGqVdu3apbdu2uvPOOwlhDeEcYQ258cYbFRMTo5EjR2rPnj0ht+3du1dXXXWVateuHfz6frkePXqoXr16WrRokRYtWqQzzzwz5COl5ORkde3aVbNnz9aOHTsqbLeyyxqqo/wd8NHveL/99lsVFBRUOr5nz55q0KCBJk+erDVr1oQcDUpSbm6uEhISNHHiRH333XdVzrNRo0Zq06aNFixYEPLx64oVK/Tee+/9pN/lRJS/kP/4gvp+/fopPDxc48aNq3AUYGbBx7R9+/aqX7++5syZo8OHDwfHLFy4sFofwZYLDw+vsJ3HH3+8wvnequbbrl07ZWVl6Z577qnwsaX0w/4ODw9Xbm6uli1bpk8++SR4+/vvv6/ly5dXa675+fkqLi7WiBEjKn1sq3PUVK6y593+/fs1b968kHE9evRQRESEZsyYETJ2+vTpx91GdffNiYqNja3wOBw5ciTkeSx9/282JSXlZx+N46fjiLCGNG3aVAsWLNCll16qli1b6vLLL1dGRoaKioo0d+5cFRcX69FHHw35Mof0/ZFev3799Nhjj+nAgQO65557Kqz7/vvvV+fOndWyZUuNGDFCmZmZ2rlzp1577TV99tlnIdeeVVenTp1Ut25dDRkyRNddd50CgYAKCwurfFGLiIjQgAEDNHPmTIWHh2vgwIEhtyckJGjWrFkaNGiQ2rZtqwEDBigpKUmffPKJ/vnPf+rss8/WzJkzJUmTJk1S79691blzZw0fPlx79+7VjBkz1Lx580pfuE6mdu3aSZJuueUWDRgwQBEREbrggguUlZWlO+64Q3/9619VVFSkvn37Kj4+Xtu2bdPSpUt15ZVXasyYMYqMjFR+fr6uvfZanXPOOcrLy1NRUZHmz5+vrKysav/tzj59+mj8+PEaNmyYOnXqpHfeeUcLFy6scF4pKytLderU0QMPPKD4+HjFxsaqY8eOysjI0EMPPaSePXuqefPmGjZsmFJTU/X5559r1apVSkhI0NNPPy1JGjdunJ577jllZ2dr1KhROnz4cHB/H+/jfEm65JJLtGnTJk2aNEmvv/66BgwYoIyMDB04cECbNm3So48+qvj4+OOeu5Ok8847T5GRkbrgggs0cuRIff3115ozZ46Sk5ND3uglJSVpzJgxmjRpkvr06aNevXrprbfe0rPPPhtyxFyZsLCwau+bE9GuXTu98MILmjp1qlJSUpSRkaFTTz1VjRs31kUXXaTWrVsrLi5OL7zwgtavX6977733hLeBk6QmvqqKH2zcuNEGDhxojRo1soiICGvYsKENHDjQ3nnnnSrvs2LFCpNkgUDAPv3000rHbN261QYPHmwNGza0iIgIS01NtT59+tjixYuDY8ovkVi/fn2F+1d2+cTatWvt97//vcXExFhKSorddNNNtnz58grXZJV7/fXXTZKdd955Vf4uq1atstzcXEtMTLTo6GjLysqyoUOH2htvvBEy7oknnrDTTz/doqKirFmzZrZkyRIbMmTIz7p8orKvsKelpVX4yvuECRMsNTXVwsLCKuyTJ554wjp37myxsbEWGxtrp512ml199dX24Ycfhqzjvvvus7S0NIuKirIzzzzT1q5da+3atbPzzz8/ZF9Isscff7zCvEpLS+3Pf/6zNWrUyGJiYuzss8+21157zXJyckIuwTD7/hKGZs2aWa1atSpcSvHWW29Zv379rH79+hYVFWVpaWmWl5dnK1euDFnHmjVrrF27dhYZGWmZmZn2wAMPBC8dqK7Vq1fbRRddFHxuJyQkWPv27W3s2LG2Y8eOkLFpaWnWu3fvStfz1FNPWatWrSw6OtrS09Nt8uTJ9ve//73CY3HkyBEbN25ccB917drVNm3aVOExrew6wurum/J9sHv37pD7Vvbv5YMPPrAuXbpYTExM8BKOQ4cO2Y033mitW7e2+Ph4i42NtdatW1tBQUG19ytOvoDZCXxOAZyADRs2qE2bNnr44Yc1aNCgmp7Of5SysjIlJSWpX79+mjNnTk1PB3CNc4T4xcyZM0dxcXHq169fTU+lRpWWllb4CPnhhx/W3r17Q/7EGoCawTlCnHRPP/203nvvPT344IO65ppr3P8R73/961+6/vrrdfHFF6t+/fp68803NXfuXLVo0UIXX3xxTU8PcI+PRnHSpaena+fOncrNzVVhYeFx/4LO/7qioiJdd911ev3117V3717Vq1dPvXr10l133aXk5OSanh7gHiEEALjGOUIAgGuEEADgWrW+LFNWVqbt27crPj6+2hcAAwBQU8xMJSUlSklJCf6fW6pSrRBu375dTZo0OSmTAwDg1/Lpp5+qcePGxxxTrRCWf+uvs3qpliJ+/swAAPgFHdZ3ekXPVOtb69UKYfnHobUUoVoBQggA+A/3/6+HqM7pPL4sAwBwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcq1WdQWYmSTqs7yT7RecDAMDPdljfSfqhX8dSrRCWlJRIkl7RMz9jWgAA/LpKSkqUmJh4zDEBq0Yuy8rKtH37dsXHxysQCJy0CQIA8EswM5WUlCglJUVhYcc+C1itEAIA8L+KL8sAAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMA1QggAcI0QAgBcI4QAANcIIQDANUIIAHCNEAIAXCOEAADXCCEAwDVCCABwjRACAFwjhAAA1wghAMC1/wdYQC2Px4iuFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantization_visualization(args=args, sigma=sigma, model=global_model, input=input, noise_scale=2.0,\n",
    "                           method='masked_image', sign='positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8c28a28",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Py38",
   "language": "python",
   "name": "xy_py38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}