{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "MI-FGSM Attack for CIFAR-10",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyO3N0dN9IPGlfivmH2/Ph7X",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qzmyzuA9r0ho"
},
"source": [
"#### Load Required Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Wbgp4eA2r2ja"
},
"source": [
"# load required PyTorch libraries\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"import torchvision\n",
"import torchvision.transforms as transforms"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hoCL3ZVhseQe"
},
"source": [
"use_cuda = True\n",
"device = torch.device(\"cuda\" if use_cuda else \"cpu\")"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Syf60zHMsnps"
},
"source": [
"#### Image Visualization"
]
},
{
"cell_type": "code",
"metadata": {
"id": "6Z3YYtkbsedl"
},
"source": [
"# load an image visualization library\n",
"import matplotlib.pyplot as plt\n",
"import PIL\n",
"\n",
"plt.rcParams['figure.figsize'] = [12, 8]\n",
"plt.rcParams['figure.dpi'] = 60\n",
"plt.rcParams.update({'font.size': 20})"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "GZ58liGZsvVZ"
},
"source": [
"#### Load Pre-trained Classification Model"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "L0WWRPYTsu7l",
"outputId": "21a682a6-22ce-4a1b-cbd4-050851e2ea08"
},
"source": [
"!wget https://postechackr-my.sharepoint.com/:u:/g/personal/dongbinna_postech_ac_kr/ETrdVDcb8g1KpyZ6qjSprGMB19bCkRQt88_Sdr_VFXn3UQ?download=1 -O ResNet20_CIFAR10.pt\n",
"!wget https://postechackr-my.sharepoint.com/:u:/g/personal/dongbinna_postech_ac_kr/EYU8SZo5MzxHlzIjlu4We9QBHpk1LO0I3PgTkv2Gurqcow?download=1 -O AlexNet_CIFAR10.pt"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-04-30 23:46:14-- https://postechackr-my.sharepoint.com/:u:/g/personal/dongbinna_postech_ac_kr/ETrdVDcb8g1KpyZ6qjSprGMB19bCkRQt88_Sdr_VFXn3UQ?download=1\n",
"Resolving postechackr-my.sharepoint.com (postechackr-my.sharepoint.com)... 40.108.156.33\n",
"Connecting to postechackr-my.sharepoint.com (postechackr-my.sharepoint.com)|40.108.156.33|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: /personal/dongbinna_postech_ac_kr/Documents/Research/models/CSED703G_ResNet20_CIFAR10.pt?originalPath=aHR0cHM6Ly9wb3N0ZWNoYWNrci1teS5zaGFyZXBvaW50LmNvbS86dTovZy9wZXJzb25hbC9kb25nYmlubmFfcG9zdGVjaF9hY19rci9FVHJkVkRjYjhnMUtweVo2cWpTcHJHTUIxOWJDa1JRdDg4X1Nkcl9WRlhuM1VRP3J0aW1lPVhXU2hKRElNMlVn [following]\n",
"--2021-04-30 23:46:15-- https://postechackr-my.sharepoint.com/personal/dongbinna_postech_ac_kr/Documents/Research/models/CSED703G_ResNet20_CIFAR10.pt?originalPath=aHR0cHM6Ly9wb3N0ZWNoYWNrci1teS5zaGFyZXBvaW50LmNvbS86dTovZy9wZXJzb25hbC9kb25nYmlubmFfcG9zdGVjaF9hY19rci9FVHJkVkRjYjhnMUtweVo2cWpTcHJHTUIxOWJDa1JRdDg4X1Nkcl9WRlhuM1VRP3J0aW1lPVhXU2hKRElNMlVn\n",
"Reusing existing connection to postechackr-my.sharepoint.com:443.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1123441 (1.1M) [application/octet-stream]\n",
"Saving to: ‘ResNet20_CIFAR10.pt’\n",
"\n",
"ResNet20_CIFAR10.pt 100%[===================>] 1.07M 1.11MB/s in 1.0s \n",
"\n",
"2021-04-30 23:46:16 (1.11 MB/s) - ‘ResNet20_CIFAR10.pt’ saved [1123441/1123441]\n",
"\n",
"--2021-04-30 23:46:16-- https://postechackr-my.sharepoint.com/:u:/g/personal/dongbinna_postech_ac_kr/EYU8SZo5MzxHlzIjlu4We9QBHpk1LO0I3PgTkv2Gurqcow?download=1\n",
"Resolving postechackr-my.sharepoint.com (postechackr-my.sharepoint.com)... 40.108.156.33\n",
"Connecting to postechackr-my.sharepoint.com (postechackr-my.sharepoint.com)|40.108.156.33|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: /personal/dongbinna_postech_ac_kr/Documents/Research/models/CSED703G_AlexNet_CIFAR10.pt?originalPath=aHR0cHM6Ly9wb3N0ZWNoYWNrci1teS5zaGFyZXBvaW50LmNvbS86dTovZy9wZXJzb25hbC9kb25nYmlubmFfcG9zdGVjaF9hY19rci9FWVU4U1pvNU16eEhseklqbHU0V2U5UUJIcGsxTE8wSTNQZ1RrdjJHdXJxY293P3J0aW1lPWlXN2lKVElNMlVn [following]\n",
"--2021-04-30 23:46:17-- https://postechackr-my.sharepoint.com/personal/dongbinna_postech_ac_kr/Documents/Research/models/CSED703G_AlexNet_CIFAR10.pt?originalPath=aHR0cHM6Ly9wb3N0ZWNoYWNrci1teS5zaGFyZXBvaW50LmNvbS86dTovZy9wZXJzb25hbC9kb25nYmlubmFfcG9zdGVjaF9hY19rci9FWVU4U1pvNU16eEhseklqbHU0V2U5UUJIcGsxTE8wSTNQZ1RrdjJHdXJxY293P3J0aW1lPWlXN2lKVElNMlVn\n",
"Reusing existing connection to postechackr-my.sharepoint.com:443.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 73456793 (70M) [application/octet-stream]\n",
"Saving to: ‘AlexNet_CIFAR10.pt’\n",
"\n",
"AlexNet_CIFAR10.pt 100%[===================>] 70.05M 5.91MB/s in 32s \n",
"\n",
"2021-04-30 23:46:49 (2.19 MB/s) - ‘AlexNet_CIFAR10.pt’ saved [73456793/73456793]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mS1y5OmDq6Np"
},
"source": [
"class LambdaLayer(nn.Module):\n",
" def __init__(self, lambd):\n",
" super(LambdaLayer, self).__init__()\n",
" self.lambd = lambd\n",
"\n",
" def forward(self, x):\n",
" return self.lambd(x)\n",
"\n",
"\n",
"# ResNet을 위한 BasicBlock 클래스 정의\n",
"class BasicBlock(nn.Module):\n",
" def __init__(self, in_planes, planes, stride=1):\n",
" super(BasicBlock, self).__init__()\n",
"\n",
" # 3x3 필터를 사용 (너비와 높이를 줄일 때는 stride 값 조절)\n",
" self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
" self.bn1 = nn.BatchNorm2d(planes) # 배치 정규화(batch normalization)\n",
"\n",
" # 3x3 필터를 사용 (패딩을 1만큼 주기 때문에 너비와 높이가 동일)\n",
" self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n",
" self.bn2 = nn.BatchNorm2d(planes) # 배치 정규화(batch normalization)\n",
"\n",
" self.shortcut = nn.Sequential() # identity인 경우\n",
" if stride != 1: # stride가 1이 아니라면, Identity mapping이 아닌 경우\n",
" self.shortcut = LambdaLayer(lambda x: F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes // 4, planes // 4), \"constant\", 0))\n",
"\n",
" def forward(self, x):\n",
" out = F.relu(self.bn1(self.conv1(x)))\n",
" out = self.bn2(self.conv2(out))\n",
" out += self.shortcut(x) # (핵심) skip connection\n",
" out = F.relu(out)\n",
" return out\n",
"\n",
"\n",
"# ResNet 클래스 정의\n",
"class ResNet(nn.Module):\n",
" def __init__(self, block, num_blocks, num_classes=10):\n",
" super(ResNet, self).__init__()\n",
" self.in_planes = 16\n",
"\n",
" # 16개의 3x3 필터(filter)를 사용\n",
" self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)\n",
" self.bn1 = nn.BatchNorm2d(16)\n",
" # 첫 레이어를 제외하고는 너비와 높이를 줄이기 위해 stride를 2로 설정\n",
" self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)\n",
" self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)\n",
" self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)\n",
" self.linear = nn.Linear(64, num_classes)\n",
"\n",
" def _make_layer(self, block, planes, num_blocks, stride):\n",
" strides = [stride] + [1] * (num_blocks - 1)\n",
" layers = []\n",
" for stride in strides:\n",
" layers.append(block(self.in_planes, planes, stride))\n",
" self.in_planes = planes # 다음 레이어를 위해 채널 수 변경\n",
" return nn.Sequential(*layers)\n",
"\n",
" def forward(self, x):\n",
" out = F.relu(self.bn1(self.conv1(x)))\n",
" out = self.layer1(out)\n",
" out = self.layer2(out)\n",
" out = self.layer3(out)\n",
" out = F.avg_pool2d(out, out.size()[3])\n",
" out = out.view(out.size(0), -1)\n",
" out = self.linear(out)\n",
" return out\n",
"\n",
"\n",
"def ResNet20():\n",
" return ResNet(BasicBlock, [3, 3, 3])"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Xwg6YFLVEWNW"
},
"source": [
"class LocalResponseNorm(nn.Module):\n",
" def __init__(self, size, alpha = 1e-4, beta = 0.75, k = 1.0):\n",
" super(LocalResponseNorm, self).__init__()\n",
" self.size = size\n",
" self.alpha = alpha\n",
" self.beta = beta\n",
" self.k = k\n",
"\n",
" def forward(self, input):\n",
" return F.local_response_norm(input, self.size, self.alpha, self.beta, self.k)\n",
"\n",
"\n",
"class AlexNet(nn.Module):\n",
" def __init__(self):\n",
" super(AlexNet, self).__init__()\n",
" self.features = nn.Sequential(\n",
" # 여기에서 (3 x 32 x 32)\n",
" # 입력 채널: 3, 출력 채널: 96 (커널 96개)\n",
" nn.Conv2d(3, 96, kernel_size=5, stride=1, padding=2),\n",
" nn.ReLU(inplace=True),\n",
" LocalResponseNorm(size=5),\n",
" # 여기에서 (96 x 32 x 32)\n",
" nn.MaxPool2d(kernel_size=3, stride=2),\n",
" # 여기에서 (96 x 15 x 15)\n",
" # 입력 채널: 96, 출력 채널: 256 (커널 256개)\n",
" nn.Conv2d(96, 256, kernel_size=5, stride=1, padding=2),\n",
" nn.ReLU(inplace=True),\n",
" LocalResponseNorm(size=5),\n",
" # 여기에서 (256 x 15 x 15)\n",
" nn.MaxPool2d(kernel_size=3, stride=2),\n",
" # 여기에서 (256 x 7 x 7)\n",
" # 입력 채널: 256, 출력 채널: 384 (커널 384개)\n",
" nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1),\n",
" # 여기에서 (384 x 7 x 7)\n",
" nn.ReLU(inplace=True),\n",
" # 입력 채널: 384, 출력 채널: 384 (커널 384개)\n",
" nn.Conv2d(384, 384, kernel_size=3, stride=1, padding=1),\n",
" # 여기에서 (384 x 7 x 7)\n",
" nn.ReLU(inplace=True),\n",
" # 입력 채널: 384, 출력 채널: 384 (커널 384개)\n",
" nn.Conv2d(384, 384, kernel_size=3, stride=1, padding=1),\n",
" # 여기에서 (384 x 7 x 7)\n",
" nn.ReLU(inplace=True),\n",
" nn.MaxPool2d(kernel_size=3, stride=2),\n",
" # 여기에서 (384 x 3 x 3)\n",
" )\n",
" self.classifier = nn.Sequential(\n",
" nn.Linear(384 * 3 * 3, 4096),\n",
" nn.ReLU(inplace=True),\n",
" nn.Dropout(),\n",
" nn.Linear(4096, 10),\n",
" nn.Dropout(),\n",
" )\n",
"\n",
" def forward(self, x):\n",
" x = self.features(x)\n",
" x = torch.flatten(x, 1)\n",
" x = self.classifier(x)\n",
" return x"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "14Gp8cGy56gS"
},
"source": [
"# input data normalization class\n",
"class Normalize(nn.Module) :\n",
" def __init__(self, mean, std) :\n",
" super(Normalize, self).__init__()\n",
" self.register_buffer('mean', torch.Tensor(mean))\n",
" self.register_buffer('std', torch.Tensor(std))\n",
" \n",
" def forward(self, input):\n",
" mean = self.mean.reshape(1, 3, 1, 1)\n",
" std = self.std.reshape(1, 3, 1, 1)\n",
" return (input - mean) / std"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WR6krshIq82I"
},
"source": [
"model = ResNet20()\n",
"checkpoint = torch.load('./ResNet20_CIFAR10.pt')\n",
"model.load_state_dict(checkpoint['net'])\n",
"model = nn.Sequential(\n",
" Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # add an input normalization layer\n",
" model\n",
")\n",
"model = model.to(device)"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tAHPpIk05Y_L"
},
"source": [
"black_box_model = AlexNet()\n",
"checkpoint = torch.load('./AlexNet_CIFAR10.pt')\n",
"black_box_model.load_state_dict(checkpoint['net'])\n",
"black_box_model = nn.Sequential(\n",
" Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # add an input normalization layer\n",
" black_box_model\n",
")\n",
"black_box_model = black_box_model.to(device)"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Xpq0K2CwrAQb"
},
"source": [
"#### Load Test Dataset"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lOHe_qt9rCfX",
"outputId": "42872d31-95c9-42a7-d6f6-e7c7f84931fb"
},
"source": [
"class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
"\n",
"transform_test = transforms.Compose([\n",
" transforms.ToTensor()\n",
"])\n",
"\n",
"test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)\n",
"test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True, num_workers=2)"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"Files already downloaded and verified\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
},
"id": "MOdsNLgsrEMz",
"outputId": "0bef4c49-8227-44ad-d341-05af800af98f"
},
"source": [
"import numpy as np\n",
"\n",
"\n",
"def imshow_batch(input, title):\n",
" # torch.Tensor => numpy\n",
" input = input.numpy().transpose((1, 2, 0))\n",
" # display images\n",
" plt.imshow(input)\n",
" plt.title(title)\n",
" plt.show()\n",
"\n",
"\n",
"# load a batch of validation image\n",
"iterator = iter(test_dataloader)\n",
"\n",
"# visualize a batch of validation image\n",
"inputs, classes = next(iterator)\n",
"out = torchvision.utils.make_grid(inputs[:4])\n",
"imshow_batch(out, title='original labels:' + str([class_names[x] for x in classes[:4]]))\n",
"\n",
"print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
"for i, label in enumerate(classes[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')"
],
"execution_count": 11,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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VP26x/kr4a0Rv6P+1mf1gqxXM7Lrm9rYU9kxalxvXWBYg+nXeDIB3mVnii4OZNevRRuO/N8g6y41vy7WNJbHID6rTdDujWPu4uBbSAD5oTX5cZnYBgH+DaFz49FIbxozGf29o/rBNf10VIYRTiMbCa83sP8X3X8LM9sblPq94Xk35hRC+ZWa/i+i15YNm9jkA84i+GV2BaEBdiU9Hu+M8aWa/gcjb6j4zuwnPeAENA7gPwIuW2UUn/AkijdFHzOw1AB5HJDJ9IyKvmTNlXPkRRAPEN2Oh9zSitwmvROS787YltjuOaE7+QTP7AiLtydsQTYN8PITwXZFwCOGART5UfwTgITP7CqIbYRbRYHk9IruAy5YpZyEu4xOIRMUHEYlEX49IBPmF5l91IXr9/ceIBv0bV1QTSxBCGDWzX0X0y657mtrFGxBNZd0PaRchhD8zszcj8pJ5yMz+CtFr/7cguhHf1PzLtXVue4uDsn5TXQ13Inqg+VEz+xaiPrYZUZ97FM8IvL9LCOE7sVD7VQDuMLOvxdu8CZFnUqs3V/+AqN3/bzP7PIBZAFMhhI+FEIKZ/QwiS4ObLPKPOoDol31vidf96eZfRq0nIYS5uN3eDOAbZnYzIvH5PkS+VicQiYBb8U8A7pW2chWitvu7S2zTrjxVM/tRRHX5pfi63Ivoi8RORLqWCxH1weKSO4rouI2EEMYsMp78HICvm9nfIuoL/Yja6k5EbR1Y2/i2ZNs40+eGtY+La+V+AC8DcLeZfRXP+FANIrIoaOfCvur+ukZ+EdF1+88A/pmZfROR/msbonH4JYgsY1oJ7M9dVuuz8Fz4h0gv801EHauESGvyHwF0tVh3FOIJIsvfj6U9c/4ZolesJUQ3+k8jajAPIurQzevegGV8qJY49o0Qj5n48xcgEj+eQvTAeDci7cEerNLDZ4njtiwTokHt23G9TiEyDnzVMuUcjf8NIPr2cxSR+PERRN+obInjXxmX+WC8/kRcp38I4LXLlRXRw9cvI/pF0aGma/NtAP8KQG6JOtY6a3m9Vlh/P4FoekfbxVL1mgLw/yD6FU0x/nc3ItPT1BLHWHHbW6acH47P8Xs77G/DiExIR+PyPInoga8bS/QvRDeA/x234XJc7n+5VBuOt3l33HbK8TqjsvxSRCapxxG9KTwe18ulq+zXLdtzu3aB6Cbxl/H1qMTt738C2NZi3U/G+7kQkaXLgbjujiK6Sfe32CZRl23KOoLoLeeDcZuaQ/SA8jlEP2vPrODa3oPo7dJQJ20k3tcLET0wHY3r5ySiXzn+S1lvVePbStpGi/WHEE0VfnMdzmtN4+JKr2PTOgHRGLItbten4jazH01ede3aKlbZX9uVbbFcLT7PIXqwWjQ1Lsd94h8QvbndsIo6fj/OAR8qiwvjPEtYlDLiJIB7QwjXtVvfOTexKC3K19FCHHuustq2Z2b7AVRDCC8744VzzjviX9yNA/j9EMJ6iJXPGczshxFNjf5QeBYd4zvBojQ03wgh3HC2y/JsY5H9wm+ihfj/2WRdNVSxPuWDFuW7WjCz75jZ69fzGOcLZrYp1uU0f5ZBNN3TheibqnP+85uxzuKcSWy9Hm3PzAYQTS399hkppPNc4HpEb/o+dLYLcgZ4NaIvHufFw9TzFTN7MH6Q/M2zXRZg/TVUn0Q0J/wRRK+Ob0SUr+g1IYQzJfg8V3krgP9sZrcgMjcbRvSa9xJEeoU/OItlczpnFFGesEXUYf1s0nHbC5Gh6dn4VZRznhBC+BskTSqfE4QQ3nO2y+CsiI+DffhGz1I5AGD9pvzM7KWIklW+N4Twe/FnXYjm50+FEFr9NP45S/yrk/+E6Kemi0ZmTyMSTn4wdJYM1nGWxNue4zz/eD5P+Z0rrOcD1e8iEv4NhybTRYvMyz4AYFdoyi3mOI7jOI7zXGE9p/yuBvBYSDpYL3rKvBjP5PUh4idrx3Ecx3Gc84H9IYR9zR+spyh9K1rnqlr8bNs6HstxHMdxHOdskfD0Ws83VAU8k0CxmVLT8u9iZm9H++ScjuM4juM45zzr+UC1gNZZqLualn+XEMLNiJyDfcrPcRzHcZzzmvWc8juOZ7KmN7P42XpZ1juO4ziO45xTrOcD1b0ALondmJt5WdNyx3Ecx3Gc5xzr+UD1OURGgP9y8QMzywP4WQDfccsEx3Ecx3Geq6ybhipEGeJvBvDbZjYC4AkAP4MoYeXPdbLvd73rnRSnU2zgbCmjOGWpZZebbJ9KUZYOLJRYW/8PX/sGxaMHDyXKuGnzCMUbh4cpfvlLXkrxtm38o0f1A8tk+NLMzExTfPjoUYrnF0iihrExNu7euWsnxRdddBEfv8HHbzTqFJuUz4zrNIT6snG9XpOYlzf0+PUGxR/98B+iHb/xXz9IcXGG/SuPHeY6m13gWehi5RTF88U5iivyk4taldvZ9CwfryffQ/GOzTso3rx5C8Wnpk5TrHVULBa5PJUKxdUqF3BB2sSOHdsp7uqi34ng6Sc5sXvv8CCUkf4+/qDB17VR4L402McvrLu7ue+NjHAdzM3Pc7wwTnEa3RSHwP3k//6PzyTK3MwrfogzYXXnuTwhcLvuHdjEO0jzNd+ybQPFmQLLSPffcw/FC5PsKpMFH2/7JXsSZX7ZVVdSvGtwiOIDDz9O8T0H+DrW8lxnw9u5zlM5NcTndleTdlQ5wT/m/uGruXzfefBRih+7f5TiEek3ue/+biliaoDN12eHuc2FOW4jOy7ksW1ohvtF722PUfyBo/ehHd8+LeOdjH9pGf/kMiKli+XVRWJz4/HOIPcDvedJeTOiQtbjp+USm8ayPz3flKyReBOT2IGEWj8tcjDoOqu1yNTtV4ue04Y17HC9U8/8NID/gijT/RCA+wG8MYRw2zofx3Ecx3Ec55xhXR+oQgglAO+N/zmO4ziO4zwvWE8NleM4juM4zvOS9Z7yOyM0EpOpy8/vwnS+m58bG6KTSMuE8sTEFMWFLp7T/4Hv/75EGQcHBiju7+2lWDVVhTxXfTabozgl87ddWS7j8CAfryZalmKRdQ/ToicKVV4/JZqtRmL+Wp69Q0OWc9xIaKqCxHpNeHl9DTkm56dZb1MpsZbigj2sIZqY4TqYnue4XOZrWJxjjZKluV30z/P687N8DYZG+JpdefUVFI/PcLs7fuIEl6fEWpPuAmugCnIN67UqxQmdWoOvWV+BtSqNFkKHS7ezXmV4kHVWtYIMKYHLcOw463t6ulhzlJN+MDTCTizpFJ/z3IxmulqerNQRRCuYk/JUpPwi7UNdR9AUr9A9qG2I+2E+J20oyxo0ANizkfWZ3/OiqynuqvM2Tzx5hOKTMxMUl0THlullrV+Q8bJW4Toql/kcS2XuF6r9U60fUqzpqsn6MzJWlbq4knM10WdWRfNV4358usb9cCVoXzHpK5YSna7KbdKiMZXtg+k9TPbfRqNqcs+TW1py/LblNVBqBakaqrYiKdWIydqJ8R9JUir8WiVa5GQdtrmndHh8wN9QOY7jOI7jdIw/UDmO4ziO43SIP1A5juM4juN0yHmhoUp6cPBcZ6Ounke8vFLjOX71mkn3cLx1I+udNr+CPaR2bGctDgD09LAuQL2w0mmuap3fVc1UWoxDqjXWSdSqrO3QOfn+AmszekVvU5c6qEgdoq46M9a2qG+KPprr+dRl1jwxI28VWa4arfY0xIdpYpx9pepV1lLs2nMBb298XbNZ1tOMnWYtikg3YBm+RqoTy6R5+TxLojA4xMfv7WO/oLJoVXJ5vib9PayFqVa5TlVbMj3N3mZ9ortL6iiAgSxrgjbk2BNp0rhSpmrsndXIsuZqfJ7X7xPt4XA/a6h2X7CX4kOjTyTKuCxySkHMxdJZ7qfVGl+kIJ5NpQrXWbXEHklD4uU1O85tqL+br9mIXEMA2NHHdTyQ4r598bbdFG/fwN5Yc0XWmfWmuBKK4rcmEiksLEhDL4muTDpCXXRplTqvXzXVQHE7bcg1qEi7b8xxnRfF/w2iXTQZ/1dCQo+T0JAu70PVTs+THP+Wvx9ou03sL7H98r6B7SyW2qqJEqe/uv2r5rbVTpP76EzjpHWgldq5gsrfUDmO4ziO43SMP1A5juM4juN0iD9QOY7jOI7jdMh5oaE6/NQoxcUF1mWoP48qPzZt4nxc27awLqMgniEDQ6x76OlmzUIuk/Tn6c7yPnKiWUpleR/q1ZJw2hLfkmyGn31TBdE0yfZV8WrJ51gPtCC6h3KNj1dJs+6hLDqFqamTcjzWYXT38PmnjY9fr6tGjHUPU+LJtBKuueYaiksV1c5xLeflGpVEG1Iucx1tEC+xql7DFHenhTJrQ2ZnWF8zWxQd3Bifs5ZXNQA9vdwGiqKry8o1r9Wk3Ra4X6RNtDST7OsFAPPTrMeZnRN9zAbWAGX6Oc7nWd+zeQuX4cJdrCMbG2VPpVnRsUHaeTtU27hnC+fU3LyZy/PECc73OCN6n8os19mCLB8Z4f3t2ryZ4nydr/EFW3h59BmPV1nRN24aYI3V9pGNFD/2FOfW27mV93dsYpLiuTG+7rUSjw0ZaWeJvHYymNVkLKuKp1JVPPTKVVm/LJ59YgZWq3B55sp8PxisrkGPqZuohkk1o7KBbp5Kt9FQaQH0eImxQNdf3udJt09qvFbv+8fHU3Gi7j+xQXIfbbyvQljeq6tTVBa3FvwNleM4juM4Tof4A5XjOI7jOE6H+AOV4ziO4zhOh5wXGqo7b7+T4rr4mgyJ5mn3rl0Ubxlm3cZwH+s6+rpZizLQzX5BBclPlk0nNVTZjPo26SQy6wQykpsvl+NjpiRXVF08hNSnSvMXlspcR5qnSCyT0C8eSepZNJMRPdAsx2PHWetSPMham64c17F6i1VEBzdb4uOvhLTkgeuSvGhV8ccZn2Jd2Lx4IpUWJP+W5tOSOtdrOif6oqKkNKuIbk3zI6q/Wi7PmihTzZck8Boallx/3eyVNjfH+p9MD+ca7BEfLgAoPM2aosYE6226C1zm3i6+BsP5foo3Z1mXNszySJw8zse7/4F7KT46fjxRxuVQHca+F3A+xVdeu4/ib923n+Lb9t9FcUU0ZahLuy1wnQ+Izq4yy9tvG2Y9FAAM9rA3V63Ix8hLux8e4jpWvc9jjzxOcU50bl0y9jTqolEtsxawKpqlsuTuq0qdlCD+aNIvG9BcguJTVZGxULQ0FdlfpkV+xHYsnz0WCJoXU3PztRVFSRiW95VSEZJqtEw9ldqcgB5Pfa90f0pSg7X8csW0UQKJ8S+5C9VULV+oVodYbnv1p1wL/obKcRzHcRynQ/yBynEcx3Ecp0P8gcpxHMdxHKdDzgsNlc51ZkQAtGWEvVsuEA3ViOS22riBNVf9/axzyOXE80kmxLOiWYjKJPoWKWM9LK+XSRtfiq685IUTfU5NdGQNmXHOyJXNi09WJs3nqHVck0nx7Cauw74+rrOt4udzaPQxik8cf5Li6Un2sUpJ3rt8Vx9Wy+lx9tOZXWBvq+k51kxNz3Iuu40b+BzmRaOkwoTeHvYWM8mRNryR9TDdVdGlzbEWpVEXkZWguroeOX5W2mltnvU51Xkun2oB58RvKNdCK1iTvGiHn2I9Tu0wL++V3Hxbt1xC8cIYX5NHinwNj40fpPjx44cp7hMfq3ZkpV9u28CeTRduGqG4cPW1FAfxPLrr4QcoHp/nazp1iDVgKdH/qF6p0MLjriy+e0F8mUxyOuYLPBYNyTkeOs66ty7xa9ssPlYDMlZUG7z+QpHLNy/avKr4VpUDl78mGqt6g+tAPfm6xF8tJe8FKtLPyvlknbYjMV5LqLnrUu1eTSQEQSrgWV4fpPIe1WiFpChLVhCNF1TzK8fnrRMeUeo7pfWRZC36pNXVUbvN21hltVGNrQx/Q+U4juM4jtMh/kDlOI7jOI7TIf5A5TiO4ziO0yHnhYaqJn486TaeHd3d7L+zcQN73WzayLFKRRqBtSzZLFdTwgMKSX1Lo6GeGpIPS7xRTHysNE6neP3yPOsY6pI3ri45zlRfkxORVVm0IQ3xXNIJ5lyG9TsbN3B+sO4u1lhpXrkTJ1gLUxG9UyahoXoM7Th2kr2wJqZZzwKpQ/VlSlx3sWEy+f5RKnGZrYvreBcOO74AACAASURBVOMw+zrNSN63RlW+zwQ+YI/oj6ZmWPPVLzq2IcnvmJaLdnrsNMXdorV54h7WA90xOgqlr8Z1lDo9RnHtFPtC9QXWszw+cIDiN/7cv6D4ZW/6UYpn5lhT9YJJzuV3xdVXU/zpm7+YKHMz+QGus3yv5OkUrcdIH+vgvvcVr+T1jfvJLV+/leKFEvfTSpnro3eI9z925ESizHPbOMfjUC/rxuZEw9SbY1+pgX5uR9tlLCrIOV8s+QyHBnn7idOnKF4QP7faAvtSpRrix1ZfXq9T0/yMqt+RdruwwHU8Ocf9Er2r96FK6G3aGS+1zc63vIBHNbD6piOhUWqjyUrk1lulhEnvqck8e3L0NuVJpt1LFiihA0sIydodU7ZvaJ0sW0Qg1bmKyt9QOY7jOI7jdIg/UDmO4ziO43SIP1A5juM4juN0yHmhoWpIbqaN4it14d49FG/dxr5UfaIhSIvXi4mmoJ2nRkZNnpDMTWSaV0hj8V4RaQpqadFQieYqJLaXHbQx2cjKOZh4b2UTpyi+Wg3NNchr5/KsY7soJ3nlBtjrZmKMdRl9A3yNgW9pgRIcOvw0xSNb2I8MxhqjbtFQnT7FZciKt1gux3qbqvjdFEQ3lpf8hZB2rHG9IrkEjbUxNfEwKotWJSf6oA2DrOHatm0LxQ88+CDFU3L+O3fsgLJ5E++zUGSd2lPf+jbFE6NPUZyqsL4ltYXbwXyvaO+6uJ1duXcvxTmp83YUpI6myqz/Ue2Iif9QQXJu7t3KY8293dxGZuu8g5LoP7Xfzo6zXgoAjjzNXlz5C7hdZSVfYH+eNVQp6Zxbhnn93b3sy7dDcgF29fM5zYzx/o6fZB1dvSjtUjyRihVp1zI2NmqsuTIZjKblmpRk+0qd20xZ2uwK5JhJDVKb5ZoHrl2euIb6QrXTZLWRRCXa7aptnzrVD6lmavnzaeshBSTNt1YpBGt3jKQu7FnI5WdmvWb2W2b2FTObMLNgZjcuse7l8Xpz8bp/amarc95zHMdxHMc5z1jJlN9GAL8B4HIA9y21kpntAHAbgIsAvA/A7wH4IQB/b2ZJa3HHcRzHcZznCCuZ8jsOYGsI4YSZXQvgziXWex+AHgD7QgiHAMDM7gDw9wBuBPCJzovrOI7jOI5z7tH2DVUIoRxCSJqjJHkrgC8uPkzF296CaMb6HWsvouM4juM4zrnNuojSzWw7gBEAd7VYfAeAH+xk/3lJFLxzJyex3S5i224RhiaUpSKgy4rQVMXGFRGOplLJatPkmFkRuHWLEF5LlEheKWamKTETzaU4rieSV+rueX/VMptMZkVArQmiG5IstCbmfEXRWxfFaDSIEejey15M8SskwfXgIBse/t6H/g/aceoki6pVhL5l206K1fx0apKTCddqfM4bh1kOmBZhv4ocBwdZ7DsviXNnpuV4YkRXkuTJJWmHRTEWLc6wCeamYa7DHTv5/Ec2cz/ac8HFHF90IZThIRbqhylOtLuhwO3yjtu4LxVFpH18ggXNf/uJj1N8zWVcpuuvv57icokFzu3o6mHB9slxLn9NfiiQEoPdiphGzs3yNRwaYAF0Ns1tsFhmwfX4BBuVzhW5XwLA5AyX8fhJ/oHH1l27eQMxqNWEy9vEuLNfRNyHn+Ifd0zMcrt68hgb6J44yXVQLPLxu3LL95OymJ9WZazKmSRD7uK4oWOlmBg31vBb9qSoXEXosoH8iEh/2JQQQLdJfpzMdaw/ctL12+5AVpc60h/MJHbPlZhMliy0U4S30H8nzilxDnoMPWQ7YfyZZ71sExZtso+3WHYcwLCZ9ArHcRzHcZznCOtlm7D4lancYlmpaZ3vLjeztwN4+zod33Ecx3Ec56yxXg9Ui+/BW72F6pJ1AAAhhJsB3AwAZsmXfY7jOI7jOOcL6/VAtTjVt7XFsq0AJkIIrd5erYheSQLbK+Z/GXWhlLnTVJqXa2LioPog0daopiqdZh0EAKTkmVBNGg88yklhB4dZX/PCF7+Ij9HF2osg55QrsI6iTwwOZ6Y5ke5pMc7sKrCmqVRl3UMmx7oLfeItVfiTWuDy7tjFep0tO1nn0d3PWpOK1Fe1tPrmkhGd2UMP3E/x5BSbJu7ZfRHFmzeOUFwUg8JKSXRhJklZM2LuKtds61buHvOikZoQDZdIqlCVhNvlitSRGDguLHB5H3tylGLVeBX6OC6JkSgAHD7Gepr5sWMUv+pabscXvYCve7HM13nTHtZpNXK8/KorWEOVTfHyxx57OFHGZRHt4fHTrGGamGWjz17R61Sr3CYWJKl3Xx8n9Z6c4muaz6tZLPcbyyZVGPke3qZU4XZz7PghissVPoceGe8mj3GS7DFJbhzKktBZk4aD61DNTqvSbrJS5zoeqxaxWOE6rkq7Vj1Rl2hsU6J9REgoVttiuk0iOfLyapmGdF5V86Tkk3obJ+akZkrWlrEhSPHaGX+WJcG06tByeb5fJCRSq/TItBUYiSYSNGuy44Qsbfl9JjRVbXIxr4V10VCFEI4COA3g2haLXwrg3vU4juM4juM4zrnIeuby+zyAN5rZd19NmNnrAFyCeGrPcRzHcRznuciKpvzM7BcBDAJY/J31m2JndAD4gxDCNIAPIBKZf93MPgqgF8B7ATwA4I/XtdSO4ziO4zjnECvVUP0HAM1iiB+N/wHApwFMhxAOm9mrAXwIwO8AqAD4EoD3dKKfAoBtkoA0p8mJxcQjJUltGymeYy9WdE6e558bDZ7zr4lPS62anJMviP+OJsY98DAnid110XaKLzedH5Y59JTo/bPL6xCy86zzOjF6kuIXXflCigv9rME6McGaq2qd91cWP5266Cy6snxNBkQzVROPKJMfiM7NJ/142pEVDVVDynxo9EmKp8ZZD7RnN+t11Avr3odYk7UgXltbd7Cv007xfdq37xqKJyb4+CdOc1wVz6aKtNtKlfVEc3VuM/mMaE26OEl4vpuvSW8PJ8XN5pLDQ7ks2o9hLsO0+JNtEJ3WlsFhigviGfeqa19AcV8vl6nQzX351LHRRBmXIy2+UOUqt7vpefa1yolma36OtYnaTzVx+/ET3I9mRDfX18/nt109pQAE0SClRTNUlmTDY2Pc14uipxx9gpMtp0WjtGmIr1nvALcbWYyqetIVWVdWkf3nMnwN+qUOxqe5H8yKN1dVtIMLkqA7K0nME0nJV0BanAL1d1NJSyTpF3pPamhyZN7epO+qL1VKjA71DpRWzVYbCyfVlaluOCN6UBUYBTmftGq0ZP+WWt4LMvpIdV4Jo6nkNrSBxlLneo+V1VProKFa0QNVCGHPCtd7CMAbOimQ4ziO4zjO+cZ6aqgcx3Ecx3Gel/gDleM4juM4Toeslw/VGWVKcp51ZXmOXHNBVSqsnWmILkJ9TPJZ1ijoVGxRts+k+qCk06xrKIjP01t//EcpznbzMRdEy1GVOe2DRzl/1nyRdQl7dkueNtElXPPS6yju7+XyNTK8v64K+1xVZ7g8JfHfSUmuvslx9rrpPc1akp4hzr9YFh8s1V2shOkJ9pmqlNnPpm+A62RCdGIT4+xJtH3bDorn53n/0zOs3ahJDrKnR0cp7pE8ck88ybq6cdFU9YrurFeu6UbxIstLG8yL/1BPD2thevs4VsqlUuKzrm7WlWULXMZjs6x3OTXBcddxjge5SjA/xdmr+np5/4MbOA9dIbe674SpLOt3Jsa4zickN9+Gfj7fiuiVBiR338ZN3K5z4g931/77KL7o0kt5+y2srQQAVMX3TnKJak7KiTHOj3hadFxqsaR1Ml/j402f4u3TkhtQx9OcjKdpyYeodZLw2BP9TreMpWXxBSyK3jIj438hLN/OW6G+ToraUGkuV/VxSsp/lveZ0lyACdGV7N/k3UiqTR67imi2pstyTxQ9UyYnminZn/pKqSeUnk/L0mmltvG6apurr60ES8qcarO/FeBvqBzHcRzHcTrEH6gcx3Ecx3E6xB+oHMdxHMdxOuS80FCdPMU6h4wU+8RxnuOviX9Pt+T+6+llDVRD8nVB5s+rkhuqpJoGALPzrKdJG2/TN8THWCizFqMqc9i338U5yv7kz79I8Zzkabt4L+dEe8ePvJnia6+8nOKy6BrKNdZwlcXDqVYXPx7RleULvL+SeMNMSC7BdI71P8iw3mdgiLUrK6Gh+awkzouvUq3Ax5yfZV3Y44/zNdi6lTVVm7fspfjQ0RMUT4yzluXhh3l/OfHL2blzF8Um3mIpidPiRWZit9OQaza3IHnoxEsslRadREtNAR+zovm1UiyKyqT5Op+e4jp5+vBRimuzrBWcllyB6lm0YRPnX2xHRjyQTs9yO33gkUco3jp4BcUpMavRfIgp2f+mEdZUFbp57BmS8tdlewC46w7O3HXFRZyD8thRzuV3+BDX2cw05+qrN/h7dM8Al6kunkGlOo81PaKh6ukuSMx6ztkFHisWJG/ntJRP9TjZtPgAiu9VSfLQac63nm4e/1eCjiXtNEnqw6RovljV/2huv8Te5JxS6eXzhgbx3lJF2FSR6+xrt99DcU8X9/Pvu/4lvFzGosT5tdEjBU02iOQ5az7EpDfj8sdoqHdWQuclZepcQuVvqBzHcRzHcTrFH6gcx3Ecx3E6xB+oHMdxHMdxOuS80FDNL0heulPjFHc9+hjFpTJrYbZt5xxrqTSfts4va064IP4YpUpSQwWIL1NKdVis1VAzmK7uHlnMy4viCTQxyX4537lzP8VZ0TlccCHrc3qDXnqeUS6VJdeTzGfXauwFE0QzlcmxLgInWdcxOMQ5z7bsvECOt7wPTCu27WCNk4kuy0y0IHlxU6nz+impk2OHOQfa+Jh4bfWznqYh3mIJHYRJ/sWMtjvJDyZ+Q3XR9qkfkMo6atpsTf3XeP/pdFJUoH43dTmnhighMlnWDubUx0qu0ViFz+nY3NMUT8xwPFvksaAdVdE2ZkQ/eeQU6+DGJnjsGOgT46zAmqfHD7ImrGdA/OJE66j5yp46zMcHgKlJ9j/TvHFDwxsp3iSapWMnuY6mpkRTJcerJzSorLHq7mLtX0HGx5zkMa2e4NyCDXA/7O/n/efFg64iY026yOdXk5yWC+Jppx5PK0H1N2pi1JBaq2suO7WRkn6STuu7DNlAbafUd0qLJ3tLyTVRfeTEtGgHH36U4v4Cb/+al19NcbaLr7GWT3XMqkmzhJMVEpWWvATygdwikqPV8vkRE/tPlmjV+Bsqx3Ecx3GcDvEHKsdxHMdxnA7xByrHcRzHcZwOOS80VBXRikzJ/O+hBnvXDA32S8z5thoyv9xTV98S9lXp61VvneRsbanMepnUPJc5C9ZqFCTfVabB+7zuJddSPCNynK9+/Zu8vehhrruOc/dlcnz8iugOcmmZE5fyhsAFyORY+5KWOi2LjqNUYR+qeniI1xftzMGDh7FaNm3iPG9zRdadlebZzyydEV1YVub5RUdQLbOW48RR0cvMsDYlm+Y6bNR5f4PDvL9+mdTPdnE71HarGqiU5BJUrY2mA6uJV00Q7YpZUsemfjeqjVDtSVryCTZEX5NN8fLuYdbBbdxxJcUnHv0OxfWTfE3bobn4sjmuw5kKt5lTY6xfMslhqXnpnhrlsagYODfh972G+2UhxXqfrrpoLQFct4+9sHr7WcMURI4yvHkrxT2HuEyT4vtUE01oXdpFd15y74kPVLdooLpEl9bTy+dUkNR6qkldqHKbKld47GmIGHBe9Dsl8Tiqtc4ctyxJzY+0exExNRqqRFNk/XpCJEVhJq35EOV+IXfuonhxHXic/dQW5P50aIKvSU72v+eC3RSfEr3oyYOcPxGBr8lll7HvYTav95/k2KL+YYncfat8/5PMhyg+hVh28ZrwN1SO4ziO4zgd4g9UjuM4juM4HeIPVI7jOI7jOB1yXmioUhl+7quI3mahwjqE6ckJik9Ieqxsgef8t+/aQ7FqqPKiZWnlzzM1zb5QEL1Mn2hHauK/E2o8h10Tbcmu3ezT9LrX8Zz0li2bKb72mmt4/5KbryFz2JL2DTnJtRfEdyXfxboy1dbURCMwPcXXZF7m/B98gOf8v/DFL2O19PXxdc1KrrvxOda/9ImnkHqHqUdQl/rvFDiemZ6m+PHH2NtlVo6/Z+/FFFfEP6d/aJjifIHLW62Lbi+oCECuScKsRvzYGqyhqpZZTwQkNVKqNamLOYxIA5GS73BZ8eJKS5m6e1hTNTDEWrzJ4wcSZVyOwoz4xZW4X4ydZM+kR8QvaHYDt7HN4n124SWXcPnm+ZqOneLyb+gS364W+cl27hI9yzjrumbmWM9y9BjrtopFPudSieOFBR5LuiX3XS7LY1dB+sHAAPuvqTplaJC9x+qyfFZ8pbLSaBZEt1ZdYP1PRTRYc2XWiFWq3K5XQqOufUnePUg/UM86S4lPn/QLk3aeFg1rWu55xXm+xnfdw/kdv/H1r1L8tb/7Sz5emseO7Zfz/eHRE6wHfXSGNbzjFR7bHv3bL1F8YP/dFP+7d7+b4mtE0zuyg/M9AkBG2pXJPcQSuf14exONaSJ3nxpR6di0Dq+X/A2V4ziO4zhOh/gDleM4juM4Tof4A5XjOI7jOE6HnBcaqqEB9pUak9xWjaAeSDznXpxjfVOXzLXWxeekohoDyVM3IL5WAJASvc6czPMP9fM51CqSO080RcfHOKfXkeOs7QjqQST5CdUXCw3WGYQq62OqQfJlyaN2QXUVkntwdobn+DMZ1gSojmFmhrUks/Nc3s2bWJfx6BNoSwjL+9ccPcJ1uHPndt5eNUfi1WWib1GtycIC62W03Rw+xHno+qRd9/SwQU+pzPvrE61KTrR9qqGqar5Fze8l10g1B4l8ZgDK0q7KkmOykRItoOhh6uo/I8KuvPil5bJ8DilZbukWOcGWoVcM3TKS33BGPO9OjnEevJ07t1A8J+LD7gHuFy/cxbn7Tj7FeUePnGJ/n2xfcmxJ5/k69/RxGTeINmRsgvWKaWm3OfGQ0xxp3QU+XlbWV5+ptOY+lXbY08N1Mi16IPVwKog+My36zPxm9ptTbeNUkS9qC8lre8RXKcg9piGebw2t4wzHKdFQFWf5Hnb0OOvennrycYrvvfNOiieO8f2hKBreI0/xgFkYYo1tYYH927pewHXa+wJev3cDj3UZcP088ejDFN9/L2u8Tp3gdl5XPROAa1/+Uop3iM5qcCPnrKyrN5fmRk0v384V9XNbC/6GynEcx3Ecp0P8gcpxHMdxHKdD/IHKcRzHcRynQ84LDVVY4PnarMy/5kW/tHEjzwePbNtAsYneCOrfI3P6FfHjKRaT1TY8xPqWQ1M8R37gCdbP9A6wN8u8+OHMl1g7MjTE56Bpj4rTrEuYFZ1ZysSLpcFakkIXTyA3VE8jWpO6FGByin1K0jJ/rbYuk9NcvtkZ1gD0dvP2K0Km5dW/ZmaG/W6OHGYd19AG1jSlUqpLkzxrMidfyLOWo17n4zXqrIkqFcUvp8zlrTc4npsXLZ/4VG0a5jbY1c3lmZ7n/c3Pcp1XRHMFS2oOUqKRUu+s4oL0napoT0RLF+SYCS+wFLeTnjTXaTq/unaSEc2TyHNQkDpMD4jXmFzzktRptp/PJy86j00yNt39OHuVXbKd/eYAYPP2XRRnRNc1K2VoyDmqxqivT9s5XyPVX+Zle20DqqtryHiqOSPVsy4nurhEHju5xhv6WEuTFU+/TZM8Fk6KtnElpIzLnJa+YKI5rVQ4Ls5yXz15hPMpfvPWf6K4VzztcjIez82x5ukdP/4OLrBoFR99gjVMC9LPxk4epDhz2UUU94zw7vN9vH1uiMub7eJrcLXkor3kMs5H+Zd/wT5ZAPC06L4OPT1K8ewc9/0tu9kD7oVXvpjikRHWOyY6u4bLS6xWxLJvqMzsJWb2MTN7yMzmzeyQmX3WzC5pse7lZvYVM5szswkz+1Mz29Rqv47jOI7jOM8l2r2h+hUA3wPgZgD3A9gC4BcB7Dezl4cQHgQAM9sB4DYA0wDeB6AXwH8AcKWZvTSEsPqvCI7jOI7jOOcJ7R6oPgTgJ5sfiMzsJgAPAPhVAD8Vf/w+AD0A9oUQDsXr3QHg7wHcCOAT61tsx3Ecx3Gcc4dlH6hCCN9q8dnjZvYQgMubPn4rgC8uPkzF691iZo8BeAc6fKAqzvPcaUU0AtW06jZExyC+KoUCe2qoBkt9Sxo11b6IlgZAPsfaix3beX730CGeQx89eIhi9QjqF81U2liXkBHdw4TkCLv3rjGKBwb4nDeJz1MmzTnK5kWvMyc5yUoliSWf1tQkH78q12RS8i2eOsZz+mPjvHwljGziGWbVdpQWuAy1Kuu+apLLbnCQ60S1Hao9yYmWo0d8qrKiBTl9ir1nJiZZU7VT8jf2iEfRyePHKK6XuZ/ccMMNFJcq3E/u2n8PxeoHVBQNWrQOt0P1dinOid5Q/MUaNdGJVbnMs7OsD+rp4eNlR1g31t3LcTuCaFN68uz91ZthbWN5gdvIbI3r6GLJoblxmMszJJ51Wzby8mPH+Bp29yfPRy4b5mQ8nJIcknNz3I7yecnFJ+OhXkP1lZqXXHt9vZLHs4v3X9fceTKe5rLcDzLic1UWPVJaPPe6ZP1B8XPbtIHrcEY8kFaCesjNirdXXca7RK4+yX166gR74E1OsDbwLT//C7w/MUiblHyNm8XfrLef2/HQZm6Xp+5/iOL5GT5+/wiXv7SPNVVjkltwgYc27NrFOr+9l15K8YZd7Pm3ae82KJe89Gpep5fH88984lMUD/Tw+Fyc5Tp6ep51y+ojOCv3uKGBpAfcaln1r/wsUhRuBjAWx9sBjAC4q8XqdwC4usXnjuM4juM4zxnW8iu/dwLYDuA34nhr/Pd4i3WPAxg2s3wIgb6qmtnbAbx9Dcd3HMdxHMc5p1jVA5WZXQbgfwC4HcDi+7fF98flFpuUmtah5SGEmxGJ3WG2Hj9YdBzHcRzHOTus+IHKzLYA+BKiX/K9LYTvJjNaFEXkW2zWJeusiQX1NZGJyjnJeTYtHiA1yc+VUj8K3b9463QV+NQaddEIAJgTT58B8Xq5/NKLKR4+yZqnGZkj1zxu85N8TmXJN1gpsc7BJN/WHFjbUi5yeWcmeQ4+LToFzckGyZtU6Obt5+b5eHXZ3sTLpl7l82uItmYl9PZyGV7+spdQPH6M6/j0KdZ5zSXaDV+D3l72XtG8c9owNV1VRrR6p07z8et11r6Y6Op27mFNVa2u+iXWRfzTbf9A8Y49rItYkDY0K22wKHn6gKS3V6XCdVSTdluVdlmr8TlWK1zmqnh9jQxzmVMyzFiGr0k7Nm5jg52QYv3P9Dj7/Zjk3OxOs/5o1/bdFA+KDkPbUCbL4pPL97FfT82SvlrHTnI7OXSEdVejB1l/WJLrpj5UPT3qocRlVI3VjHjEHRL9Z9cFeyjWXKd1GV8nZljzBdG2ZMW7Kye+gYk8o3nVLvL5ao7KlVAtcx2dOsHj9QbJzbp1K3sedctYsXEDt7snn+JrmOvhdnHgMdY33nfvfoov2rZX9s+6sVBVzS3XcaPC/bj0FOcGLI1xv5wWj72a+Fpt2cZ59xbEO/Lk6GGK7//GbVC6JT/sru97K8UbdrBO69J9rCbauZv74qx6gZ3mazgxyX29d4j9zdbCih6ozGwAwN8CGARwfQihuTUsTvVtTWwYfTah032O4ziO4zjPJdo+UJlZF4C/AXAJgO8NIVBa6RDCUTM7DeDaFpu/FMC9LT53HMdxHMd5ztDOKT0N4CYA1wF4ewjh9iVW/TyAN5rZzqZtX4foIezmdSqr4ziO4zjOOUm7N1S/D+CHEb2hGjazn2peGEL4dPzfDyD6xd7XzeyjiJzS34vIAPSPOy1kJaFZ5/ngmia2E3+gfI51EtkMn3ZOtC0pmW828WmpV5PG70E9fEx1AXyM/j7WKQwNsKeGemYUizznffz4US7zEM/B94uWoyy+KdPTrIuYmmBdQ/8g+/EU+tWjQ/J/LajujOfc9RLVxfMp1Ll8+TX8/nRavF0uv5Dn1NM//HqKv37bHRQfOcLz/MUFrqOqJDTs6eJr3i36GE0ONS+5qFKigeqSfFilOT7+xCn2sskWWHNQmmOvnK3g83lyjnUbkzW+pguzrClYkDYDJPOwdRe4zOOij5kdY20GjNtJLs91sGWE/ddqFdbT5Lu4zMekH7RDfaEOH+TyLYxzG+qWvj+Q4X5bKbKaoVbgOquLf1xZfLxM+snCTFK3Nil99aR4zo2Ps8ZqWMcSGS/7Rf8zL75W6q+m3mNPPPYYxRv6+Xh50W82pB9kxYdKNVa5rPhm5XgwyIjHU0YGlw2SVzWdYQ/AlTC2wHrCvs2sr1HvrBnRJM1N8nXcIO1uZCt7LD14390U93TzOV9z5ZUUF/J8TSdOcDu+4frXUGxyf3r8MZpoQuM0t/vTd3OOycqT3M+2NnjsefGrvpfi0hz3i0yZj3/FBZdD6U1x37pvP+vGjp7g8evkOI/v3X1cppOn2NOuJP5oNdEBPzw6mijTaml321rMNvim+J/yaQAIIRw2s1cjclb/HQAVRAL297h+ynEcx3Gc5zrtnNJvWOmOQggPAXhDpwVyHMdxHMc531i1U7rjOI7jOI7DrMUp/dlHDX0091RG9EqSW0qfGvslF1W+i+duVSeiua4SgiAADdEEzdZY9wDxFNJjQGL1ytLceg3xVtH9TYsvlp6DpTiuqp+Q6MSqRdYEzIjHx+QUz8HXRBdx9Dgb6T/95JMU50o8M9yV4u1Xwknx6+nvZiePPRewV8rLxKul/i3Wv5w4wcvn59lDSX1OUOA6zYl2L0i7SYsX16YNrB8qiqfT2GnWUPWJb0q36JOueyH7VuV3sG7h8QnJLXiStS+pLu4nAFAX/xn1ODokHkIHREOl/mMbh1mrp3np6jXOw5YWLeKpU60SNCyN+gkdPcW6DBNd2+697INVGGL93Pr0/gAAH89JREFU0cSM5B7cyNewb5CvkWotGzVuc2mTxH0AJie4Dk+dZB+oDUOSx03ydmakzrM5vma9kiNSNVuZHG8/Nc1auxnxJstP8TVUvVFOcgsODnAbEMkWUqKZ0lx+C5Kzs7ub2/F2yWu3Er5125coHpJ7xpD4DPaJnjGf4XP8zlNPUfzIA5xb77U3vJbiyy55McVX7nkHxarJ7e7iNnDvFq7T22//Bpe3j9fXPHiP/MUtFO+6mH2vtl/JHlB79nHu2muv4rFmbpbbxOa3vRlKTe5RT4+yv1p5jOPvfJU1o91vYI3s2CQfsyjeYnML3G5OSf7ZteBvqBzHcRzHcTrEH6gcx3Ecx3E6xB+oHMdxHMdxOuT80FApImFSn5W6eBrp/G1lmDUD3T2Sxy7NmgHVHzVU/wSgLvn9NH9gTcrUEI2RalPKoguo1UR7EeR4dckZJueg2hM9g7rkHJudZm3IzBTrKipVLk+9xnU0J5qrw4dYB3L4MGtjtg9wifoKWsL2+bjGxEtr82bWEXQXWNewd892iktFnve/5x7RVB0XX6l51h3MLbCfT06uaVXaSEF0GXsvYb1OXfQ8+++9j+Kxk1yn2wb5Go9PcPl2bKMQe3dw/rGZMdYQlCpJPc+06BJOV/k6zk7xMRE0ZyPvc+wUa+8qoinauYuv0cwM6yYuu4zr7NjB0USZmzl4mL25spKnc4/kAzPxqilWWDeXTrGe5/BR1jftSIt+SJp1Tjyajh9+OlHmw6OP8zZcpeiSD4bEF2pwgPU+qmdMGbdDrVOT401M8tgwNyv5GlWDKtrB8jxrrroKPP7W6zw2pkVDldb3AKKxbdR5/XJx9XlB3/nal1Pck+d2UhAdWj7POlzN8zm6get43x7Wd171ItYk9fXyNVRhmd6CSmUeb/d/5x8pfs2rXklxT9/3U/zFv/5r3p94k73l1fwD/uFN7KN1txzv9a/5Hoo3iU9iizShSMl9dvMVrNva0vtGihuy/p6Lue8ixX23JveomoxFC1Vulx/5tWQZ2+FvqBzHcRzHcTrEH6gcx3Ecx3E6xB+oHMdxHMdxOuS80FDpU5+qabLifdPbw/O12TTvoVYtt4mXL09ajhcVqiFhTZbL/K34PqmGymR/2YzWgviQ5Hl5Il+W6HdMfLH6u1kTUBMdQrEoufo0v1ZWvGpmWGsyJdqauRKXZ17y2G3YwJoEoL0OYkbyqp08zXqbC3exViOb4Wty+eWsHVHurN5DsdZpSTRU5QqXpy46CM2BNrJlhOLZGa4zzTk5Pc3nd7rB1/Qf97P3zQtS7MdT2MzrPz3K+qLZ+WTWqLJoNUoihqiUWIcQ1ENO2u2CeMF09/B1Xyiy91de2tm+a15E8df+jv1zlP4h1tXtuWAPxTMT7LE0LfrLrbu4DtXjaPQoa8oemWaN2ObhYYpVQ7Uww+sDwPZN3C7Q4H1U5ZrkRd+j16gofmqPP/EExVt37KK4T3yi+vq5H82IXjIlPlM6lpRlgJ2U8gz0suarO8dtoiD9VsdW1Q6itnpPuxdfynrKuuSBU8yy+glF+665luJsWvQ9Ze43DT1eg9u95lscH+N2NzHOnnVv/pEfpXj3XtYnPSr5Gesylv7Y236M4qJobu/7+P+i+Ngh9ne74oUvoNgaSR2yeiNmRPd7zbXSD4SSXOd6Q++5XKcNaUe9vXrPWT3+hspxHMdxHKdD/IHKcRzHcRynQ/yBynEcx3Ecp0POCw1VOw+ioQGe09+6mT0yumROPyPzz6nU8jnW6pJ/S/PcAUnvk7rENfEUSqkfjfjh6Cnnu3i56r4aNY57unk+WNMRmhRAzzmIhqs7x/uX6WiU6lynQ0OsD9q5k31XpmZZNzEjGoJqo4VOrQ21wGU4Nc56mF3buAwQf5uM6NQuv+xiivUa3377HRSPiW6uWmGtiGpJtF1pzrSqeItpm8mk+YNSneMnx3j7xuPsW3XVRvEbkvxjjarkowRQEZ2Y6sbqoo9Rf7OG5rKThlla4PWnJlkndql4JPWJh1w7du5mfVBlQQxxJN9XXkyY+nskp1s/6zWnZrnfFUt8vvksX6OC6Dt3bEnqRE6c5PyDhw+yj1RVxqNJ0YGVK3yNMjk+Zr7AZT4lfmSnJ1nXNSu+TkG0fVnJE1eXXH+a/1E1VyHDeqS05MQUGR16u/ka5LpYg3XwBNfHSpif53ahPoBpKWNeNKwp8QFcEM+jqSLXSSKBocrEpG+rlvC2b99F8e133k3xj7yNcwH2FrjOJuUa79zBeU+3iD/b8aNHKT5w4ADFt3zt6xRf9eKrKM4XuA0AQFl0Y/Nyk0lJ7lXVkSV0yuoDqLGMRVbt/P2Sv6FyHMdxHMfpEH+gchzHcRzH6RB/oHIcx3Ecx+mQ80JDpT5SEO+WAfHzUb+ebpmvLYheSbUpGpvM1YZ6MsdZAzoHzmVUX6my+FCVS+I7lWXdwIL4+5TFn0c9lbpEJ6HnAJmT19yA6ouVyCUl56f5urIZrqO9F3FONsvx+g/ey3PwY5NJD6R21OT7wfSc6H2K7I2l7URSPSEtGqUrXsD6nUqFr8n9D3HOtakp9jCamOAcaOrHM1/i8u3cxTqGo4dYtzA1wfoi9UIr1VhncfBp9pkq1zn/VlnzOU7J/pHUJWTFK6ZSE1+qqvpSSZHlg0pFcmCKVnDTBtYY5UT31Y609IO5ae5HJloXLa/6ZAXp9wslbnOzc1z+keFBijPaz8vJdl+pcJ3OFtlnKYjn0cAgH6MkfbtLxsvte7lONQ/n0wfFn6zM7bYqeqGxOa7ThowdXXkej4uisYLkRxyU8pro0MoyHlclF2BB9EIr4cnD3NdmNV+h6Hm6cqL7kmR7VbmuM3OsWRoT376jR1nvOCPtVH2xHryPNVOjBw9S/KlPfYriwQFuI48+/CjF5TKPBTd99iZePiMa2BnWqd37wH6Kv/IPX6U4kUwWQEHG454u7muan1a9FvtFS2f63CAa2Ybe6INrqBzHcRzHcc46/kDlOI7jOI7TIf5A5TiO4ziO0yH+QOU4juM4jtMh54UoHSIA7JZEugMiFFWhLERcXBNDL5TVqJOPlxZxmxpAAkAjyD6N93F6nAXJh0T0WBMhZRBDwYos372dhaQjQyz0nBHjTBXjqkjeTAwXRXycTrOQvyqJeNXrdGaCBdmnxDiuPK+mkaz+nZhtk6G6BSU1hltgoefcHIt5N0ii3FpVROIiau/q4mvy2te+iuKBDZwo9/ZvfZviilSSmv9BElar4HmbGJM++SgLSYMYgaZNzGDlhwyjB+6neHDLFop7u5OC75ERFsoPDg5QvP8eFqPOifhWmh1SKUmyLSLzjCSRHR/ndnXoMCdhbUdllttEl/Szqpi99oggOiM/cJme4/2Nyw8RipIEPCtC21RWxpZ8ckge3MjJkEfkByopEbYPDm+geHZe+oH8OGO2yn3v+CS3k6ky18ms/HqjmuLlc5MsUNYfHqiYWA1wNcltTx+bqXYV2Di0NM/1cegIC7qnZyVZ8gr4wq3/RPHTTx2ieF4SMOsPOIpzkuxYEqWjzsL/OTFLHRdz1bImT67r+CjjuSz9/F/9JZYjleKxQtvI2Ek2k+2XRMKXX3whxZuGWPR+2z9+k+LTE0mz1Qt38fhz7RVX8DHFxLeaEQNuqWMVpaflhwM5MZi11OrNpBV/Q+U4juM4jtMh/kDlOI7jOI7TIf5A5TiO4ziO0yHLaqjM7IUA3g9gH4AtAIoAHgbw30IIfyPrXg7gwwBeCaAC4EsA3h1CON1pITf28XztiGgKtklsDUnQKiaWmrBVnyoTeijRC9Vljh8AKmJAWBeDwPmpkxSPHec5+VKJy1QRPVC+wDqCkT6e750Ios0QkzLVlQ2ILqG3R/QyEqruIVXj/S+UVb/DdT4ruorJcdYI9OZZZ9Go8/lNFcVotAUlKUNlgbeZmGJdwMhGTqIdGqxr0KTaedG3dEky422bWZO1SxJCz4neZm5ezVq5/ONl1mUcO3KEYpM6rhmXL5WRdi9twsDXtFs0BWpiCQCzs6wROvj0E1xmSUidybHuYcMG1kls2LyD4oK08/EJTgw8JQaJ397/YKKMy6HGnRBtYn8/J9YdlGTFXbJ8Zow1XDMLXL4Tp/gaXixtYNOFnHS2kUt+x62IBrR3gfUps3LMUzOsT5wUU8ijx1ljtCBGnUXpRwslSfINbveZHLc7U79ENQFWo2RZPj7P5cVRbve1Co/3kPIfOclj7cz86jVUV122l+KNvdwu03JPmZziOt//5NMUP3qQ28nsGLe7hujSQmEjxRZEEytmrVrpmui8v5fjF4rR8uOPcT8ui67tTW/6IYr3vfhFFG8Z5n6REj1oJstji2qCASDdJVrAPtYvZkVPqeesyecTyehFwFmRdq7j6VpoJ0rfDaAPwKcAHAPQDeCtAL5gZr8QQvhEXJAdAG4DMA3gfQB6AfwHAFea2UtDCKr6dhzHcRzHec6w7ANVCOHLAL7c/JmZfQzA3QDeDeAT8cfvA9ADYF8I4VC83h0A/h7AjU3rOY7jOI7jPOdYtYYqhFAHcBhA83vntwL44uLDVLzeLQAeA/COTgvpOI7jOI5zLrMiHyoz6wFQADAA4IcB/ACAm+Jl2wGMALirxaZ3APjBTgs51MPF7Mvzc2Bxhr1urM46hRR4fjcjnh010RelJGliQ/yCGupjBaAsHhia/DgnfjMX7mJtxrx4pai3SloSQs+fZl3B3JToXURPk03z/PTA5ZdQnBKfqYbo0ILMLwcpT0p0Ho0Gz/GrNdhgv5RXtSNy/GOTK9BQSVLWSpk1UadF33PxXtav5HNcR9u2s94nK9dwXJIT94hH0TUvvoriELgS9t/LPlBT4tW1azvrHMqiHwqiCdAkuertpddQtYCnRHuybTtrwACgWuO2Py1+OyNbd1G8eQf70/QPsm6tUuc6LYkeZmgL+17lRbdmYXV+ZSnRJgbxhcoPsz6nZxN7Om3ZxuUpZViHdmiMx579j3OS2nsPcaLhmiR0LZd5ewCYmmHd2okJbieq35ma4bFkWjRUlQrXgWnS2RyPBRkZfzPSjtROLS3+QDnxycpIElzV25TEr60i7X70GLdTE73PvMSS33pF7N3GGqY9G1kfiSDJj+VW2rOX+0HlvscoHjvF17kq4/3cJPsWFiWuiy9VUO8u8eraOcxj055d7B+n1oo94vn0yldcR/G+q1/M5Z3lNlmTa2BSX709rI8CACSS1fM5JXVZclPR+3ZDE61zmSpyj2600HWtlpUae/4+gF9YPC6AvwDwi3G8OOoe143iz4bNLB9CSKZRdxzHcRzHeQ6w0geqjwD4HIBtiKbw0njmd2CLX/FaPTCVmtah5Wb2dgBvX01hHcdxHMdxzkVW9EAVQjgA4EAc/omZfRXA35jZywAsvnvMt9h08T3jgi4IIdwM4GYAMLO1vJV1HMdxHMc5J1hrLr/PAfhDAJfgmam+pOAi+myi0+m+UNG8QqyNmZ3h+eFLLmXdhkyloipzqeqZlE6zLqKiPi0LfHwAmBHvl5kir1Nq8DNjuSheWRV9phTvK9EU1WTOPRjrIhriOVTWK2B8jtu2saarUODlGdEPlSX/19w8H2DsFPsHzUv96DO0zuGHsAZPEMk/FapcR+PTrKFSVVZXQfUsPMc+I3qhBWkHKelOA+JZdO0+1h2ozu7QIdbbDIkPS0P8ejT/o9ZYSvzUNHdgI2EYJN44LTQFXd18Tpe9iH2kuvtZg1QRv56JWc5Z1pA6y3d1Syz6HvEwyqk4rw1VuaY9W7dRXBd/NtM8ct18TYa38NgzeIK1LlnJaXZCOuLCQdZUpZGs86q0k/FZbocnx1nLV1H9iuQ0y8o5lUVrWKly3N3D62ck59mC5Ao0McmxbtHuiT9QRq5hWtppRnRmDfHsk7SjyMr6Jjk5V4JmdVPdrOoR1edvQw+3i4FuXj4/yHWKguiE+zjuF03XhIyvfDTguksvkpj1oqk66+r27bua4l07WQO2exf3c/V4KvSxxiwtvoWqqVKdMpDsy6qhqsoxtadou9E4JWOHxqq5WgtrdUpfnOYbCCEcBXAawLUt1nspgHvXeAzHcRzHcZzzgmUfqMxspMVnWQA/jWga7+H4488DeKOZ7Wxa73WI3mDdvG6ldRzHcRzHOQdp9778D82sH5EL+lFE6WfeCeAyAO8JISz+PvcDiATmXzezjyJySn8vgAcA/PGZKLjjOI7jOM65QrsHqpsA/ByAdwHYAGAWkUv6r4QQvrC4UgjhsJm9GsCHAPwOnsnl9571sEvIikdRkGIH8aeoB56/RZrn1CsyF5sSfVNKZtDL4r0zO5/MpHPyNHvFTMm8fVbypOUl11K2S/JhZXnOuVHjOJtTjw7Rx4iGKi9VcvoU64lOnhLth/j9FLr5Nwc1yT1VXND8iVy+jElyQNP8XhxbWjVlSX8epTHHOq1GjbUgp8ZYbDEmvk87tmymeEq8wWqSr1F1AJmUenVx+fpFu3Ldy/ZRXF7g4919990Uz85wHdSl3ZpopmqihUmr0kL6TVraZHcv638AYHCEvbFKQXJKzopXlhxDc1Kms9yuTPQ5tTq3q7LkrQu1pJ5xOUzyM5p4h5nkL5xc4H78xHHOyZYXT6fD4he0cQdrtPK9rMFKSZ3XKsnhcl50VyUZCxZEY9UtGqKeXvYUUk1WRvp2l3hzFbo5rpS4PLluqUNp9w1pp+r/M7cgvoGifcnlePDKyWCm2ps0n27Cf2glpDN8TpkeGf9E31OTe8TuQdYavvIy1jTdc4Q1UHNyjdVzaV60h31S57u3sIbpjfsup/iiAe53VZmcMvER3LSB/ddUN5eWsU59CFMyniMnY2NI/g4tSDvJiA5LzQzrosuqSr/QdqRlVu1eudZ5hrx2qWf+HMCfr2RHIYSHALyh4xI5juM4juOcZ6xVlO44juM4juPE+AOV4ziO4zhOh6zVh+pZRWdbs1lxCZFJ+yefepLiKckzNLKZvXJ6e1lzMF+SfGVT7NkxOcl6KQBoiN9OT551B5qqLqO58NQSSD000qKxknPWuC4Cnpz4+eTFcmOuKLkES6xNUR2E6m8aoktTl5F0Gw+Qml7kNXi9Nhb4OvWK1uLYSdYt3HPfAxT3ifYkm1OtnugEsHzc0Fx6kt9wcIDFHq999fUU/534nz0weYDioLoGqTK1kWrUeYWhjawZu+SyyyjOd4kYBcCY6LjmK3yQfBdrNQqixwnq/SJalIr4RNVEW6L6nVppdbqHbB+fU0U6Xk50GyXRpT1yjHNolue4PqbEr2jjVrbnS4tuI6hHno5tSObGq4lfzu4C13G/+J/VpR2q2456KAUdcdt0xZT0C/Uv07FE9TOptI4l4ocmdVoUbeOk6j9l7OzbzB5OK2GhyF5fjbqYXckApxqqrNTJi3ZwXxsSn78TM6yRKoo2rzrE13To8ospvvoS8Y0aFj810ebVVDcsY0mf6O60DaTlmplUiKm+U8Ye1dkBQF36mknK3ISvlOTy0/UTt5SE7x6HOn6vBX9D5TiO4ziO0yH+QOU4juM4jtMh/kDlOI7jOI7TIdbKD+JZL4QnR3Ycx3Ec5/zh5hDCO5o/8DdUjuM4juM4HeIPVI7jOI7jOB3iD1SO4ziO4zgd4g9UjuM4juM4HeIPVI7jOI7jOB3iD1SO4ziO4zgd4g9UjuM4juM4HXKu5PLbD+BJADsAHGmzrrM8Xoed43XYOV6HneH11zleh53jdbg0e/WDc8LYcxEz+6waZTmrw+uwc7wOO8frsDO8/jrH67BzvA5Xh0/5OY7jOI7jdMi59kB189kuwHMAr8PO8TrsHK/DzvD66xyvw87xOlwF59SUn+M4juM4zvnIufaGynEcx3Ec57zDH6gcx3Ecx3E65Kw/UJlZ3sw+aGbHzGzBzL5jZq8/2+U6FzGzl5jZx8zsITObN7NDZvZZM7ukxbqXm9lXzGzOzCbM7E/NbNPZKPe5jJn9RzMLZvZgi2WvMLNvmlnRzE6Y2X83s96zUc5zDTO7xsy+ELetopk9aGb/Rtbx+lsCM7vYzP7czI7E9XPAzH7DzLplved9HZpZr5n9VjyeTcT99cYl1l3RuGdmKTP7ZTN72sxKZna/mf3EGT+Zs8RK6jCukxvjfn04vsc8aGa/bmZdS+z358zskbgOHzezX3pWTugc5VzwofokgLcB+Mj/3965x1pVXHH4+4mg+KBFjVy4oPwDKbEm0FoQq00t+GgkUVPtiz5sqyDEpNbS1zWlabUaNfEBKMZKtYagEqGxVtsm1lDSYmu1Ugta0UaCj4tArZCicCuu/rHmhM12n3vO8crd57rXl0w2Z806NzO/DDOzZ9bMAZ4DLgAeknSqmf2xxHK1I98DPo4HCj4FdACXAH+TdKKZrQOQNBpYDWwHuoDDgHnA8ZImm1lPGYVvN5JOXcDOgryJwO+BZ4DL8PtY5gHjgE/3YzHbDkmnAw8ATwJXAP/F72QZnfEJ/eogaQzwGP7/cxHwGjAV+DHwUeDs5BcaOkcB84FNwN+BTxY5tdjv/RT4PvAz4K+45sskmZnds5/qUSbNaHgIcAfwZ+BWYAt72+U0SZ+yTNC1pNnJbwVwPXAKsEDSIWZ2zf6rShtjZqUlYDJgwLyM7WDgeWBNmWVrxwScBAzJ2cYBu4ClGdstwBvAMRnb9KT1rLLr0S4JuAcfsFYB63J5DwGvAMMytguThqeXXfYSNRsGbAZWAgf04hf61demK+lwXM7+i2QfHhruo8tBQEf69wmp/hcU+DXV7wGdQA+wKGMTPhl7ERhUdp3L0BAYApxU8N35yX96xjYU2Ab8Oue7FH/BGl52nctIZW/5nQfsAW6rGcxsF7AEmJre5IKEma2x3OqSmT0HrAcmZMyfwRv6pozfw8AGIC5pAyR9Am9/lxbkDQNOwyepOzJZd+GdRZU1/CIwArjczN6WdKikffqR0K8hw9Lz1Zy9G3gb6AkN92Jmu81scxOuzfZ7ZwOD8QlYzc+Axfgq4NT3otztRDMamlmPma0pyPplembHmFOBI8lomLgZOBQ4692WdSBT9oRqErAh12GAL4cDTOzn8gw4JAkf4Lalz53A0cDjBe6P4ZpXGkmDgIXA7Wb2jwKX4/Ht8H00TJPZtVRbw+nADqBT0rP44L5D0uJMnEXo1zur0nOJpImSxkj6HDAHWGBmOwkNW6LFfm8Svs3/TIEfhLZ5OtJzW8ZW0yiv9xP4S0ElNSx7QjUSfyvLU7ON6seyDFRm4kvY96bPI9Oznq5HSDqoPwrWxlwMHAv8sE5+Iw2r3C7H4QP9/cDv8FWBn+Oa3pF8Qr9eMLPf4m3vNDwObRO+/bzQzL6V3ELD1mil3xsJvJpWpfJ+ENrm+S7+EvWbjG0ksMfMtmQd04T/31RUw7KD0ocCuwvsuzL5QR0kfQhfYn0Uj7+AvZo10rUo/32PpCOBnwBXmNnWOm6NNKxyuzwMD1691cxqp/pWShoCzJY0n9CvGTbiMTsr8AHoLKBL0mYzW0Ro2Cqt9Hsx7jSJpC58VXqumb2eyRqKx6EVUdn2WfaE6k08WC7PwZn8oABJHcCD+ImW88xsT8qqaRa6FnMlfqpqYS8+jTSssn61ut+dsy8DZuPxJ28kW+hXgKTP43Gj483spWRemWLRrpF0N9EGW6WVfi/GnSZI29BXAkvMbHEu+008iL2IyrbPsrf8utm7VJulZnulH8syYJD0AXz59YPAmWaW1am2bF1P19fMrKqrU+OAWcACYJSksZLG4h3A4PT5CBprWOV2Wat7PqC6tvQ/nNCvEXOBJzOTqRq/wlf/JhEatkor/V430JHiT/N+ENoivwvyLvyl/eICl25gkKSjc98bggerV1LDsidUa4Hx6URLlimZ/CBDCvx9ABgPzDCzp7P5ZvYysBU/GptnMtXWtBNv8wuAFzJpCq7nC/gR4XXAW+Q0TJ3FRKqt4RPp2Zmz12ImthL6NWIEMKjAPjg9DyQ0bIkW+721+MR1Qs4vxh1A0hT8ZN/jwGfN7K0Ct5pGeb1PwPvYSmpY9oTqPrxjmVUzpMDBrwF/MbMXyypYO5JOp92Lb6ucb2aP1nFdAczIXjshaRo+aajyr4evA84tSOvxwOBz8eXt7cDDwJckHZ75/pfxGKIqa7g8Pb+Rs1+ITwBWhX4N2QBM0jt/4eAL+Ampp0LDd0Wz/d79wP/wlcKan/CVmJeBoqsDKoGkCfiq1Eb8hb3e1t0jeOjEnJx9Dr7l/+D+KmM7o3cedOjnAkjL8YHsBvxCz6/ibxTTzGx1mWVrNyTdCHwTX6Fans83s6XJbwx+euh14Ca8A/4O8BLwsapu+dVD0irgKDP7cMb2EbxjfRqPdxkNfBtYbWZnlFHOdkHSEuDreBv8A37r8vnA1WbWlXxCvzqkO9AewYPRF6XnDPz289vN7KLkFxomJF2ChziMwgftlXgfB346cnsr/Z6ka1PebfhN6efgBwNmmtmyfqlUP9NIQ3wyvx5ffe7CJ5dZ/pV9iZc0Fz8UdR9+4vcU4Cv4HXVX7b+atDFl3yyKx69ch+/J7sLvAjmj7HK1Y8Lvr7F6Ked7HN7IdwL/wW+wHVF2HdoxUXBTerKfDPwJD7Dcgg9+h5dd3rITvjX1I/wttgf/yahLQ7+WNJyM34TenTR8Fh/EDgwNC/Xa2EvfNzbj11S/h+/O/CD93d346vXMsutZpoYp1R1fgDsL/uZFwD+Ths/jFyWr7LqWlUpfoQqCIAiCIBjolB1DFQRBEARBMOCJCVUQBEEQBEEfiQlVEARBEARBH4kJVRAEQRAEQR+JCVUQBEEQBEEfiQlVEARBEARBH4kJVRAEQRAEQR+JCVUQBEEQBEEfiQlVEARBEARBH/k/cVuAH7+1vWYAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: dog (5)\n",
"Image #2: automobile (1)\n",
"Image #3: cat (3)\n",
"Image #4: airplane (0)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zOKuFhacrE_K"
},
"source": [
"#### Test Phase\n",
"\n",
"* White-box model"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 863
},
"id": "dZx8OVMzrHou",
"outputId": "4cc00355-9669-475c-8aa4-7aa8f04c14fa"
},
"source": [
"import time\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"model.eval()\n",
"start_time = time.time()\n",
"\n",
"with torch.no_grad():\n",
" running_loss = 0.\n",
" running_corrects = 0\n",
"\n",
" for i, (inputs, labels) in enumerate(test_dataloader):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" outputs = model(inputs)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" running_loss += loss.item()\n",
" running_corrects += torch.sum(preds == labels.data)\n",
"\n",
" if i == 0:\n",
" print('[Prediction Result Examples]')\n",
" images = torchvision.utils.make_grid(inputs[:4])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[:4]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[:4]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" images = torchvision.utils.make_grid(inputs[4:8])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[4:8]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[4:8]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
"\n",
" epoch_loss = running_loss / len(test_dataset)\n",
" epoch_acc = running_corrects / len(test_dataset) * 100.\n",
" print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, time.time() - start_time, len(test_dataset)))"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
"[Prediction Result Examples]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: deer (4)\n",
"Image #1: automobile (1)\n",
"Image #1: ship (8)\n",
"Image #1: deer (4)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: deer (4)\n",
"Image #1: automobile (1)\n",
"Image #1: airplane (0)\n",
"Image #1: cat (3)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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nZleY2dAS9V5Sk2RmW83sb8zsvroljZvM7I/i7RfXOUSfJEsrV0peZ8Zts8fMSmZ2yMw+Y2ZnLFGvU83sKjObjNvwBjN78fK9sHLM7OVm9mkzuzfOf97Mfmxmv9+sXetImNlbzezuuH33mtlfWxTOp1k5W+P2f8DMimY2bmZfNrOntFHX55jZNXFZRTM7GI+H97Z94ksQQvjvEMJ94RHwsInb6koA3wohfGy1y1slfjv++57FyRQAhBB+iCiW4zosHTvScVYNf0LlPJY4GcD3AdwG4B8AbEK0DPA1M/uVEEKzwLmvRhTE9msAPoZ4GcSisDHXIArSeg+AzwAoIAqq+1EATwPwOsnrcgC/j2gJ7+MAygBeFu+bQRQIuCVmdiGiQMojiILFfgFAL4AnALgMwPsRBbd9H4D/VVf2IrfU5fWC+PjF89kBYCuAVwJ4sZk9N4Twk7r9T0PUhqNxm9wC4FQA/x6nu8GHANQA3IgoyO4QgJ8F8BFE8d+0XRf5a0Ru858D8CVEffO/ADzHzJ5d/+VqZucjCkI9gqgtv4AosO/LAVxvZq8IIXx1uUrGbfcVRAF6vxzXdQTAWYie7ryvbt9tiIxbdx3j8eT+BsAaRIGIV4thM/sNABsRPYH9cQihK/opM8shCi6dB/DdJrt8DdH4+VkAn+xGmY6zYkII/s//Hdf/AGwDEOJ/fyHbLkQ0sZkEMFj3+aXx/jUAL2iS52Xx9o8CSNZ9ngTwT/G2l9V9/sz4sx0ARuo+zyGaoARESyFoUodL6z7LIPpiDgB+pUm9tkp6p+Zbt21NfN5jAJ4g285GpNn5iXz+X3HZb5bPX1bXxpc2K2+ZNrxYPj+lyb4JAJ+K93+abLsy/nwMUeDg+mM+H2/7o7rPU3E/FABcJHltRjQxOgAgu1xd6/I+t0l91y4xBhv6Iv782hW22cXx/p9ehevkFXHevyl129vFMsIS/24BcE4X8n9inN9tS2y/MN5+Yztj0v/5v2788yU/57HENKL4eQ8RQvgRgH8FMIzoC0X5Ugjh6/UfxMtOvwfgIIC3hBCqdflVAbwN0U35tXWH/Xr8909DCBN1+xcAvLONc3gpoi/nL4cQPqMbQwjtCHtfj+i83xtCuFPyuR3APwI4z8yeAERLZAB+HtGE7grZ/0vo0htSIYT7m3xWQ/SECoiePDXjIyGEXXLMOxBNin+jbr8XAzgFwEdDCFTnEMJ+AH+O6OnJ81ZY5YaYi6ExgPE+RE+uVprnI4qZbUD01PRrIYR/WsWiPgzgWYiW3QYQPXG8GpHo/ttmtqXD/BeXz5fSHi5+PtxhOY7TNr7k5zyW+EloLkS9FsCvATgP0VOQem5qsv/piJZ27gPwnuilogYWEH2BLnJ+/LfZpON6ANUmnzfj6fHfbiyvPSP+e278dpNyevz3LAB3ImofALi+fhJZx7XowhtSZjaKaCL0IgDbAfTJLkt96Ta0bQjhATPbA2CbmQ2HEKbw8HmftMR5nxb/PQvAcst+/4poafRGM/ssIrH495pNakMIZQB3L5PXo80/Irrfv2E1CwkhvE0++hGAS8zsagCvAvB2RIJ4x3nM4RMq57HEoSU+Pxj/bSYOP9jks8VgxqcBWE583F/3/8W8G+oQQqiYmT7RWIrFX9b7Vrj/ciyex2+12G/xPJY8h5hmbdUWZjYM4IeI9G43AfhnABMAKojO/c0Askscvly9TkJU/yk8fN6XtKhO/3IbQwhfMLOXIHoi+RsA3hifw48BvDOE8I0W+R8TmNnrET35/LX4Cd2jwccQTah+psN8Fp9ANX3Ro+7zqQ7LcZy28QmV81hiwxKfLzpTN1smaPZm1eJ+XwwhvHKFZS8eswHAA/UbzCyFSBC9kuW6xS+CTpdG6ut0bgjhp23s36odO+ENiCZT7wvyar2ZPQPRhGopNiB6QWCpek3L35eFEL589FUFQghfAfAVM+tD9HLBSxC9ZfYfZnaeLqUeoyw+Pf2UmekTWgDYUvd265r4KV+3ORL/1aeR7XI/oqe9280sFUKoyPbFp4/3dliO47SNa6icxxLnm9lAk88vjv/evMJ87kY0sXl6/LbfSlh8U67ZktizEYnZV8Li21AvXOH+1WXyXszrOSvMa7F9nm1mzfK8eIX5LMep8d/PN9nWajmxYbuZbQdwAiIx+OJEoN3zbkkIYT6E8O0QwlsRmWJmsPI+erT5PqIXKZr9A6I35hbTxVWqw+JS9gPL7tWCWJN4A6K3Xpv172KffLuTchznaPAJlfNYYgjAH9d/EFsQvBbRU4svriST+FfvRxHZLvyNmfXoPma2aVHMHXNl/PfdZjZSt18OwAfbOIdrEL2594tm9v81KXerfDQOYF2zOiJ6bXwKwHvN7KlN8kpYXUyzWBv0DURPkN4k+74M3XGY3hn/vbj+QzM7D63F+282s4fcveOXB/4C0X2s/hX5LyF6kvG7ZvaiZhmZ2TPMrHe5wszsZ+Kni8riE7x83b7p2O/rlBbn0BUsCjm0oviGIYTPhhDe0OxfvMtk3WcPCfDt4fiGl62wTk9q9gPEzJ4E4E/jZENInUX/tJWUEbNoRvp/4utrMZ+nILJJOYLmE3bHWVV8yc95LHEdgDeY2dMAfA8P+1AlALwxhDDTRl7vR/Rm0v8E8FIz+zYiXdN6RMsKzwLwbkRiboQQvmdmH0X0duDtsQh30YdqEisMLxNCKJnZJYjsCz5jZm9E9MQlh4ffIqu/br+F6E2qr5vZdYieMNwaQrgmhDBuZq9GHOLGzL4F4A5Ey5wnIBJvj8Z5L/K7iJ5oXG6RIeqtiJ4qvQLRZO+lKzmPZfhnRIL0y83suYiE/6chWkr7AqL+WorvAbglFohPI3ob8FwAP0b05h6ASCBuZq9E5D/1FTO7AdFr+3lE5/0URGL4TaibFDXhbxAth30P0USwBOACRB5HuxCFP1lkC4C74s+3tWgDwsxejsgfC3h4+fIZ9rBB61gI4e1y2OKPYV3y6ibtlvFWRNfKdwHsQTQWz0Tk85ZEJIz/f/UH1Bm5rvSlDSBq91ci8pC72cyuQTSOfyku57favNYdpzs82r4N/s//dfoPD3sAXYlo0vElRJOYPKIv4ec3OeZStPBUAmCITAK/hUg4XUI0qboewLsAnNBk/zch+mItAtgP4G8RPTnbiRX4UNVtOxFRQNcH43LHERlhvkv260P0i30voi++AODKJu1zBaLJSwGRUeXdAP4FwMublH0qolfdpwDMI5pgvXglbSb5XIbmPlRPQGSUeTjO/8eItFUP9aPsf2X8+XZEAvG74/PYh8jQdHCJ8tcjMhG9PR4Lc3EbXA3gVwGklqsrgNcgmgDcFx87E+f1pwDWLTEGdzapx7I+VHVlL/VPx80aRBOQ67tw7SzpQ4VoIl4FcPoK83o5oknxjritSoh+SFwD4BeXOOZcHIXvFqIfFW9BZOK7gOh6/yqAZx7NmPR//q8b/yyEdp60Os6xR51L9adCCJc+qpVxHCFezvpOCOHiLuX3i4h+NLw4tHB776AMQ7R09u0QwmtWo4y4nN9HNCk+J4Rwx2qVU1feZYje3H1uCOHa1S7PeXzRVQ2VmWXN7M/MbL9F8cduNLOf72YZjuM4xyEX2cOxFjt9W/IiALes1mQq5mxEy2jt6P+OhosQmdiu6mTKzG6PJ7bL2aA4Tkd0W0N1JaJ17csRPSa/FMBX43hh13e5LMdxnOOB90l6rpPMQqN5ZtcJIdyGaAl7tct51WqXEfN3iJaAF9n5CJXrPI7o2pJf/BbRjQDeEUL4y/izHCLNweEQwjO7UpDjCL7k5ziO4zzadPMJ1asRCRg/vvhBCKFgZv8E4ANmdkIIYU8Xy3McAEAIYScegV/TjuM4jrMU3ZxQnQfg3tD4uupirLQnI3qVtoE2PUgcx3Ecx3EeTX4SQrig/oNuitI3obnXzuJnm7tYluM4juM4zqPF/fpBN59Q9aB52IJC3faHiM0LWwUvdRzHcRzHOebp5oRqAc2jxOfqtj9ECOEqAFcBvuTnOI7jOM7xTTeX/A4gWvZTFj/b38WyHMdxHMdxjhm6OaG6BcDpZjYonz+tbrvjOI7jOM5jjm5OqK5GFJjyfyx+YGZZAL8O4Ea3THAcx3Ec57FK1zRUIYQbzewqAB80s/WIAmT+GqKgob/ZSd5jU+OUHhpcQ+lqhQOVp2WaOD09rZXl/IYkv2qNd5f8ZguNAepzvWlKTxzhFc7+niEuI3DTV2plLmN2ltKlUonSa9eMUnrywA5KH97DkRxqNWmjkZMpnVp3Cm/vZTlcNsn1TaeSlJ4ucJse3nkfpYemd1L6gbu/T+lDhw9TupTnPnjPFV9AKz5xz6mcR5HbtAbut1wqI9u5D+fL3OapNG+vlkkWiJ4SvXeB0j7u8/HD7ChyyqlrKb2wf4rSA08c5u1985ROG7dRNst9VgkVrk+F6xvZxj3MfKlA6TJvBgDk0lxGJsVlWOA2D3JtVmq8fyLVywWEHCULUqepIrdByPDx734SjyNlzfCwfML2ZVEIuyU3o5URcsPxQiLBN5NW+68EzSEhktSk7FDhYYOS3O/0/pmSOldqnH+Qc0gk+N7Q6hy1TVu1cQ3L79+qvLHDR5bdDgD/608+3F6darVltyvdjqGrZ2yJzp6VJFrY+rWq/9GcX8M4kH6utejnTq8lLf9v3/8HbefR7dAzrwfwfgCvQxQR/acAXhJCuK7L5TiO4ziO4xwzdHVCFUIoAHhH/M9xHMdxHOdxQTc1VI7jOI7jOI9Lur3ktyqUC6yjmAVrovLzrA0ZHBqh9MQUB3dPJHitNJVm7UuxyjqPZFbmnVIfAAjTrHmqTnF6LMV1LFa5DsUi55nrZW1IQjRL+TznXyypXoj1PmWI3kaWm9cN9VN6rsD1zSYlvznug7QMpT4pIF9m/VAizS+Drh3h/I9UdqFdKjX2lc3kOE8Y93NhXvpMxCZF0SiVK3yOmWneP3mY9UV3f3MfpUsFHle906zhGh7l+qWTXP5kYYLrm+L9y6JdqUmfl0s8jpOJlKQ5v2b2cDIMAdGlmckODXoa7qNSlTVWmn+1xh+U5bqpiqaqJQnRXUD1P8trnFrpfVrrgUQHsoIQlMvnAKChnzQt/Z6U+1lteW1KUtqkqudo0mbS54kWbR4Cj1PVIzVopBr6DLy9C7q0VrSr21J0/2yG7wXFEl8nquttyE8aoVONVlX6INGgLVz+utDz0+uqWf0adGiyS2MVltdQNZTRoNFqUf5R4E+oHMdxHMdxOsQnVI7jOI7jOB3iEyrHcRzHcZwOOS40VHOzrL8pl3l9eWKCfUVSgf2DasVJSlfBeiO1lcqXeS21epD1TbV97IsFAPfedRel79j1AKUPTbLHUKHIGqWq+FBlekXPkuGuUq3K8MAApXv6WM9TNj6Hk07iNiyL6VBStCylNOefEn2OasIyokNLjZxI6XSRdW4L0ge17JP4A/w7WpHLsQ6hVBTPI9F2BNFilGs8bvKiebIC5584yDq36h5u88Rh8aUSnV1iax+le57AvlTjpZ2UPlRkDdXAwEZKZ0TzValw/dPG9U8l2fMpVEULg0ZNQUW8t1SmkBNfqGA8bqui16kEHodp8QZLiP9ZJsPnVDXus1ao1iQh+p+EDHwLLXQZKuwQPZB66TToNlQPpBUGkBFtn/pKBfGY60tzm/VmWEuoPk7TC5yW2x9SooFSP6BSg9cYZ6CaraQI5VIprq+2kep5rKbaGLSgfT1RKx1Xp6imqOEcZVymUstrkDrVdCnaR+1qCZWVtF8rDzj1xmq3DnqtNnh3dUF750+oHMdxHMdxOsQnVI7jOI7jOB3iEyrHcRzHcZwOOS40VDOThygdGbLXpefZZ2rnIY7DnBhirUqpyLqL7910G6XzB7i8k6us68gnxN8IwE8PcQyxjMQb3Hf73ZSeObiX6yi+U1XxJZmaZB1YYYo1WTnxyuod4Thy0+JzNTQsPlCbtlD6hE0bKH3qGWdS+ilPeypvP3E7pWuiqbr9tnsovWMvt3Fq7dmUvmWO9UgrQbV1hQKna+JphBqvmUuVIdZZSE+w5mikxLowK3Cde9OsLQnizVUVD6baWvHuSqsfkMT2K3OflyRIW1+ud8WDVWsAACAASURBVNm0qbdNg48V65UAIJPmNkiIRgrSxjN58YnSO06Sx3kQzVRWYmT2yjkW2pNQNeh5VKtiCdVRyHaVaajOQ9tD9DvpJKd7ZEiu7eX2AICNa1i/ONTD+5Tl2s6KyKq3n+9/ZdEgzeT5+PFZbtRSSbSE0kdFyW96lvt8XuKQqveYmo9pzMyE6NiqFfWtkutMtTVH4S/U4OzVpmapUQ+0/LWv6U7j1LU6XtO98v2Ty/C9TL8z5+YlpqZqB9v2Z2vdZk1ET5xu0DfK/g22VN3VnQH+hMpxHMdxHKdjfELlOI7jOI7TIT6hchzHcRzH6ZDjQkM1tP4ESlcXWE80m2cPpzWb2c9n01bW93zv29+n9P/72L9R+umjo5QePvNUSn9nH8doA4AnP/vplD73KRdSesM6XpO+96brKb1jL2uqBjdxHU4+h/U6ex98kNI9CV6Dz4pP1fQd7NU1Oc+6iIGBdZTe+eBuSt90/XWU/vLnPkfps57M5/viV7yc0nPTrDHrnefz1YG4tucJaJfpadaVibQC6RTHK0wE0WrI/mGW2zA7xrq40hzXemGWtShV0UBZn+gWRDNVTLPPVKjy9oEE91FpgStcFK1Ipl88ocSXSrU2KdGylII0CJp4CoG9toJsz/bytVk19S/jOi2IKCppy+sc0sn2tHbJZKNGSQrgpAgxRHLVqLWpqUaK09vXsp7pjBNYq3jiiXyvA4B1a/l+lhNfqWSK06oLm57luJvzefYSK5e5D9TmaWg91zGX4+soLzq5ffv5/vjA/Xyv2r2f7wUHp0RztSCxUtU7TDRcidTyHn1oEQevG7TS36iOq9MYka1otX+r2Hr5PBsDFosaW7BRX9lO+c1oGUuvlYaqoRK6e2e6tJXgT6gcx3Ecx3E6xCdUjuM4juM4HeITKsdxHMdxnA45LjRUuSxrCKZFCxKG1CeFNQW7d7Ln0RV/fyWlUyVewz9367mUvl08oyrzEngOQH6SNUp33/ZTSk9OsL5naIA1UiO9omuosA7g5JNYB3bihk2UPrR7F9dHfEIGRzl2Xl70N3NTrN/ZOMLeNycNsq/VPQ/spPTn7mCfqR/d+hNKP/n0MyidmGSdxeHD36L00LkvQLs0xIlTX6Y+HhcZiW3XL/5i1XEeV/NHuE8Oj0tMyWnW9mnswMFh8anKsadTLsP1Xajy8T0J7pPBXtlf4tr1i19RMc/by6KRCiaarAqPSQCoSMzJdILLUP1OMsl6mASkjBKfQxkSK9Akdl9N8k+0p6HK9nMbqkZKvWxUZqHak6Sk0xL/cOsQ1+/MrXzdb9/Gmqntp57WUOd16/j+l83yOM31saapLG06duQgpedmuY0L4oFUmGfN1bot7FE3uo7vPZUyj6u16/leo9o8jW+4ef16Sh8Rz70H9/P9e26e65/K8nWUybGuryH44QpoYWGEpAY71f1baIha6Xk0f82vpW+Vav9UTyTpknj2Vap8nWsLqJZQ26umMTBr7Xs+Nbbh8ufUku5LphrwJ1SO4ziO4zgd4hMqx3Ecx3GcDvEJleM4juM4ToccFxqq/3v1Nyn9wE//k9JPP4c9iwaSrLu4+U7WF/WID8vzz2DdwsgCB3F74iyvL58l3jsAgLt5nT/HkioMz0lcuTleIz+tfxulg8ZV+wnruFBgbco26cpqmj2TnnYGexjNZFh3MC4xyGo51n4kE6zLSI1wfTeLZmtijNvjJzewpmrNkJQ/wccP3HoD2mWon3VeeYlRVquw1qN3gPsxJf42yQ2sTbnLxii97whrPfLSx5skXuLG4Y2Unt7D4yyXZ4+izDpZ9K9y/UOJdQ5DEhguk2SdxYLol/IV1gLWRFNVkNiIAGA1rkNCdAwZ8QTKpDmPjPhAZWTcmcQ/FAkVqiUelzVr9Mpajqq0mSWWj+WXTGr9RIcn+pz1/bz95K2sDzrltJMpvXk9X5ejI3zdAsCG9ayh6unjcVtc4PvZ2DjffOYn9lN6fJKvNevj66Yq2pVD4rs3O83jtn+Ar5Ok9MmoaMBy+/jeMCW+VRecyZ575z6B9aP37jxA6bt2sGfe3DRrwJLZ9uOCtvJEatc3qpVmSrV5WalzSeIhNmioWmgBVW7U6vxa+WQ1htHT+IkaOE/ya6hR6zq0qlMrdP9WXlxHgz+hchzHcRzH6RCfUDmO4ziO43SIT6gcx3Ecx3E65LjQUO36wZco/c1rvkzpfT84hdLnPe1sSqfF0+lV6zg21fp791DaZH15s8g0shnWDADArKwRH5iZpfTYfazjGiyxfqVXvGU2ir9PSuo0XmFtymyZdRTpLB9/Qj/73+SGOM5bUeIXzhZYvJIXbcz2QfaiKfXy/pPioTQ7z+1RqHB7VTdtpfR0+7IH5ERHkBU/mmJJ9DwJ1tMkRHs3vJ5/b5xzEWuidvSyhmr3XZz//CRruIpznF9JjFQmd7FXGQYknlaN61cuSuy+IvdRfy/rLopF8Tqrcf4V0b6Um8RAS8stI1RFh9AjWousiKBEG5hKsZauWOY6JQP3UaqF/09LWugyGtN8eE08l1TCddI61smdsm0bpfsGWK80NMKeTcNyHQJAKs3nPD/JWr6FWdFEBYkbl+A+mxMfvfkJ1hz1DXOdenp53OfL3OdzE3xtz02PS5o1V8Oi9UuPsjdYb4bzP/Ek9sE650y+399y105Kf+07N1L68ASXvxIa9DVy/2031l67+p+WmqkO49C1rL+p3qi92IMN5Um6Wf27fU6ttndDM6W0fEJlZv1m9j4z+7qZTZhZMLNLl9j3rHi/uXjffzGzdc32dRzHcRzHeaywkiW/tQD+GMBZAG5daicz2wrgOgCnAngXgL8E8GIA3zCzFiHeHcdxHMdxjl9WsuR3AMCmEMJBM7sQwA+X2O9dAPoAXBBC2A0AZnYTgG8AuBTAxzuvruM4juM4zrFHyydUIYRiCOFgq/0AvArAfyxOpuJjvwngXgCvOfoqOo7jOI7jHNt0RZRuZlsArAfwoyabbwLwok7yXzjERm4vf8lLKB3GWRy83tggcVREmb2H76R0tcziYaj5nwjOyxo3EsDEId5n1z6u8+nGhn19sgp65yQL43MZEY1LV+2d52DGG0TcO5ZmMfDUOAtFz84OUzptbI63VsS/tYwYh4pIsSpi42JaDBHFVLMkwtmwwPVN9LLw/xNoTVZ/HoiwNCFCeTV5zBd5HKQynO49get0ngSBPf1sNnG87z9ZrFuZ4/z6e3mcTtzMY2DTWn5RoadXxMkVbuPpEouLFwosPpbYz6iA+2ShxH1QqTaKRHsyLPTPpvntgWqCxbNVsFA/Ib/hEiJ+rRW5Ttkcn3O2j+uUTrd3C1PTShnGjYaFIsxfI6aap21h08qREb6uJmdYEH1ETDfzeRaUJ1KNovtq4HtHQuo0LWXcdvtdlN6zV38Pcz/PLYhh7AGu44YT2GhzwwYe5yWpz9Q8j4F9+8QQVwKrn34yC/m3bOUXXpIpvlcODfJ1c+GTzqT0oUluj+/9iAPVzy00Bv1WGkTkDcGH2zteaRXsuG10HGsg4TaNOpV2jUsbLix5v+Vo9OdtG3lKulNj0JXQLduExSvgQJNtBwCMmNlRvLflOI7jOI5z7NMt24TFn22NsSqAQt0+D203s0sAXNKl8h3HcRzHcR41ujWhWnyG2uwpVE72AQCEEK4CcBUAmFn3n705juM4juM8QnRrQrW41LepybZNACZCCM2eXq2I/RNzlH7Vq15O6eph1ggUdrO+KHOY19SrPaxPwolsKlnZw8E7i2OsTanpgjCAg+Nch1M2svncL5z5Aq7j5AOUfvB7V1L60IIE9pUAqedvfBalz9lyHqV33s/Bhf9j53WUPjXJZnqDSV79rQXWWajZaY9oouZyfPzEWjbjSyZYbzSYYa1I8gC3X2b3/WiXVFrXyHm7VfgcylXeYWKWtRVJ0VDlMnzOIitD3wZu083bWdd25FY27hzq4XFXGmdTx8wD3KbpXj6/nj7+/VJR/Y1EFhYJFaqyCl8t8fnXyo3jvCxBT0OSL+uUREtOyLgqaYBnCdDcmxGNlOSXlIfg2Saao+XQ+tSgaS5/pIfb6ILTeFwP9okBr2jMynKdLMyyrm7f7p2Unp1krSMAjI7ytTO6jq39FsQ0977dHEh94gjfD7ds4tt0zxrWRO2d5za5+w6+Nrft5fvjyZv53tQ/wEagal561533UnrXPtZsnX46B6vP9nCbqrZPNVGqueoTrSIwgVZUKnztNGqIeH81vmyF7l1rpedpEARJUjVesn9SNLGtNFA10biqxksNdhuCjIcGURcfn9K7EVCtcptX5TsnKWXUNAAzVB8p59TQiN03+uyKhiqEsA/AEQAXNtn8VAC3dKMcx3Ecx3GcY5FuxvL7PICXmNkJix+Y2fMAnI54ac9xHMdxHOexyIqW/MzsTQCGAWyOP3pp7IwOAB8NIUwD+AAikfl/m9lHAPQDeAeA2wB8squ1dhzHcRzHOYZYqYbq7QBOqku/Mv4HAJ8GMB1C2GNmFwH4MIAPASgB+AqAt3WinwKAUOH1293XsT5o6zrWrqCf19CD6IXSgf2Exmu8HpwY4rXcviKvrWbErwcAslOHeZ8Kp5FnrUg+yR5IOQl8u2aUvVkOF9ivZtMw6xb6htgrppriNtqUYt1Ybi0HiE6UxQdKNFRBtSxV1pZkkzyUcnMSmHcb17c6xeeTm+chkky2b1SSr3EexQLXoSIGYlWTOopuICOqo3yFj5+tssaqWuNxmhtg7UhOAgXPTPGY2LjmZErv/ykH1C5Ms5ZwgLsQiY3cxqNbedxv2Mzalqr4EU1XWWtYyjRqqPJpPudZsL7GqtyvEA+4So3bHIHboF80VEkJEl4Ur7Bauj0NlWo3khL0OyNamKc+YRuln7xd9EYHWfO0IAG4+/pYR2dFTs+UJNDwXKOOI9sn+kXxjZqZ4zZct4V1Xhs2s1ZvboY1R2LHhj4JrF5e4PxrPdyGJtq7dJWPT4ugpzfHxz/h7LMoncyyZkq9w0pyr7pvN7v13Hbn3ZQ+dIR9sFaG6G/k2g5yr0jIOLXE8uOyQd8j+Wl5llh+f4geVDVMrfRBGgx6dIjvFf19/P0xP8/XuQZzLsi91uT7obe38f21gthB1lRTVVGNld5LJNkQ6FwCXqsmS3VfR8GKJlQhhG0r3O8OAM/vpEKO4ziO4zjHG93UUDmO4ziO4zwu8QmV4ziO4zhOh3TLh2pVyYnXzL4fc2ymhRKv8U+Ib4ltYrHJhSefTulSnjUAfSyxQnWCF3fL4+xLBQBr+1ivcujgPZT+7g8+zgcYrzEPyPEnj7AeJhxiX6bb7/0ypednd1B6bJq9Xk4+heNd9W1mzVVyms8pIX5BMFnjz3Ij9YpG65QSe+NUp9R/iLNPDvL5a+xAsPVNU4qiiZouseZI41uVRHNVUxsn1SXU+PdHQbR9EG3dSYOsExgeZv3M4d2iB8qwT1UmzX5DuSSPieIh3h/jPO7v/O4+Sh9cz34/I+tY43XSGazhyq0VvzYAlV4+x33hdq5Cnv3VFlJ8bVYTPI6ySW5jDc2XEB1bsVaW7e3pHkoFvtbVM+msE7jNn/nEbZTuzUr8xHnuw4Lok4LEW5yfY51aTc5vw1ouHwCyPeyjNCUxIcdFj3h4jMdFRfx6dj/A2ry1Q5z/Wafz/TG3ibdblfs0I0E0h0bZTy21hvWg9+zguKGHx3hcqsZKfaV2H2Ifqe/exN8H09N879HvD97anESDb5NollpolKrVRv1hPcks3xtS4m1YEd8na/BY4jZXfRHk3pQwHodJ0TRt3sR99KQz2EcxLZrWosTcXBCN8F0P8PfRgtQvP8fxd6M8eVyn1esq8Dklwelimes0JNd2Se7PBcgNP8nj7GjwJ1SO4ziO4zgd4hMqx3Ecx3GcDvEJleM4juM4ToccFxqqAYlllRa/iep+1orsybB2JrGedQnPOe0EStckFhZmJXbfA2LU0sSHqjfH+pWt21mzNF/gOvUNsjfMRlnjrska9aatT6T0niN7KL1zmnUJI6ecTenRtdspnRpjHYJYciAkWMegWhX1/wmijanlWC+EPu5DiDVZRvRK1bQcvwJqCV6DT+Q4z1qZ0ynRRC2UNbYTn1NvinUPOeM2KogOYr5XPJbWsCZp9jDv3yex+aYkBtvkNGsGNm5kjcAJ21j3UJ7ncZ8IrO+piA6jOCl6pkqjpqAXrI/ZPsL9lE5ym+wv3kXpmugWtJ8LaY0hyftXTHypRMbWirLoLEoSB64i2pepBa7P8PAQpbeIPnMyz3H0xsa5D9eov0+Gz2dNT5PfuKIdeXAve3/t2M2ed9OzrKmqlTm9bf0arkOOx93kPN+rBuWc9+9mjda9D7IPVLmH8+8ZHKZ07zCnH3iQ9abrRniM7djNmq9b7hKdntwreyX+Yk58tY7M8Pk1Qz2QGnycGkLV1ZZN6/HFBf5OyaQkbqd4dw0O8rhZu5b1lNNT3CflIo9rDT6YEv+1DSOsYZ2eYl3blMSY1NiA2Sxfx4P9nO6R66qcbZx6lCt8jMbqK6mPoMYXTKlOjOuo99dakfMrVRu/19vFn1A5juM4juN0iE+oHMdxHMdxOsQnVI7jOI7jOB1yXGioevrYByV/iNdzK7JeXRR9zzqJWzcnkQVtmNfsC9MSj2yatTnNwswlxKfJJN7WWtHbaDzARJF1XOWErPeK9kI1VTmJK6d+PTvvZ51CWfRBwcTjQ2xUdKBUe1iX0JthDVlSYhMmpby0nH9ugesfpP4rQo5JSz/1SfyosoyDRND4WXzWA2nuw1QQfU+/xKGTuHQ57kIkA4+RXbv2U3rrBo4blxyQ+Ik9XH5hgWOWJRPcxr057rNkhjVdBfVpmW3sg0SC26g3wXqY7VueROlihf1mxo31PlUZWcWSaOlk3JQljlu7uodUhnUaRYlBdp/0gdqhBbBHU7+2qfgFZUS/UxNPJRPPvFnReAFARWLplUXjCYkf2CO+SwO9PPBGe7kOWdWzyPFJ0ViNbuT76cH93GY3fvf7lF4vOrNEVfU83Cfjs3y++w7xGMov8PmuWcPnl5XrdGKy0TewFTkR6gbRuCYkJiQa4s5pbFI+Pi16nx7R0qVF1JqR7SmJ9bemj8dRJSP6o5KG0+XranqCdXnj4kOoeiWz5f3f+vpZk9Xfz9/htVqjT1epyHWck++Eknp7iaxN+2RcdGWnncIa03NO3EbpqanW2rpW+BMqx3Ecx3GcDvEJleM4juM4Tof4hMpxHMdxHKdDjgsN1fMu4Lhz+8Eaqm3nXEjpymHWfpjMG++/5j8p3SfrySOiq+gTT46gpk0AkOH121qedVjVisQ+SnBMr0yC16RN0iHP55zIcCy9ILqBdIHLtzKvR9+0wLqCHfO83jwH1gRUxNOj2Mtr4jDenlGPENmuOjQL3Ge5oq75tyanGqcKL7KPiM6rLN5Z6RT3UUL0LgnRSWREz2Np8W0aFF+nIb7cRoZYO3Ln7gcpvW0La6imDrOWZGyB++xJp7BWpSaasAOHxRdrlPc/6TT2Ttu7i3UVALDr/vsovXH7ZkpvqrCeZbiXPYkmRvlaKki/2wKPk3Kaz6EkOrlssr1bWBDhRVnG6VSex8DtD3IQycl5HpenbWWvry0SH7EmWpPBIdacpcV/aHqyMcZZTgIcjgxzGxfKXMZCkcflWtlfLILQI/qbnmHus94B9qFayPP2YoX7ZPIejiOan+Fz6unnNihKH9x6J18HsxJrdb208dAQ63VKZb6uh+Xeg32tA4Nu38xlTMyIt1dC8qzJDU087nI93OjqkVQu8LhKy1eMxq0rF0VXJ9q7mmhq9ftEyw8SXLVvgNs0Jfc2lVCVy6q35BMIYtxV1jioABYKEot1ljVNM/nldWmq84L00UKB22SzeHmdfeppDXVqF39C5TiO4ziO0yE+oXIcx3Ecx+kQn1A5juM4juN0yHGhoRoc4bXOw328pt+7lX2k1g+xliZfldhO+9kLxx7YSenSPK9PZ0w8layx2ZLG6/xW4XX8qqxpB439VBUNlcQ8S1Vlzb7IMcJKoh/qkf03iefQi9Zxmx3eyPtP1Hg9e3wT6x4mzmcfrBn1DyqJ9qXEbVqCrLnP8vlkfnArb5/h7c1ISvzBpAgRUuDtpp5KWd6/KvG0KtIm+mtE1/Dz6oslPil9a1kj9awLTqb03ATHSBvfy/qjnNQfBdZpDI3ydTM4xNeN+vmMH2LtytbN3OcAkBR9Sk/gfsVBvtYy/eKfs4bTpcDXRTbwdm3zmuhhNN2KiugjIfHCqqLtmBbdxvSDrCubEW1N/1NY27hlPffxhk0bKZ2XWIKqbQGAbIbbNC8ed5vTrIGqyjn29PK46B/kOs7OsQeeehYNi69fWeqYEi3Lxs2szTvldNamDA2qBov74O6776f0YB/Xf6P4WmXE06lQFr2RWh7ddhdaMZDia1d9nabzEi9W2kA1U7kcb9fQgL1DrElNSJzNmhzQK/5ltcD396roPYuiH0rL98XYGHvYzUuQzMFBvncUZYwUJZ7igmjCVKu4IOMeaPSmquq1KXfchPhShZoaU8m4Fa1fQcZ9zyh/Jx4N/oTKcRzHcRynQ3xC5TiO4ziO0yE+oXIcx3Ecx+mQ40JD9ePdvB57zwO89pkvsc9KuV9inq3j9eWeNbxePXDuGZSuiqaqdpB1E9VK4zxUfUA0pleQNXC1MalKfKtSRX2gxEdEF+FFGlISn5BUmdes+7PcRn0bTqD0CQObKD3PtizYfeFTubwkt2kuzTqNao37sFZk7Ul2gr1qTpzgPvvUbo5F2IxSjXUBCYnFN5kX3ZY04XyV61gSL66MaElgqt3g/EvisRTA43ZinNNbE+z9kqyJ7k50C5tEO1hY4Pwmd7HX2dnnP4XSPVkeJMUSe5NV5ySmJYANI6yFmJzZQ+mJaS5zsoe1GemTeNwVweMyJUEkLcn7qwYqlKUTW5BL8rWrEqwgMdJQ0RhmojWUOHE/+MntlD73jFMpPSOeSj0SC1D1SABQkTr1D7B+Jifav4U869r6Bvn+uG4jX9u13XspPTnBesWZLHvgpXN8bWbVk25CfPtEXzMs2r7zz+f4j2vXyL1DLtSeAdZgTc/yOJ2UMWiFxviIrSgW+drTOJglGYdzJe7XnOjWsj08jpPyBaCeSia+TSW5txQkEKmGxsuLn1pexkRFNU15Pt9yhc/n4BFu04rokRo9oOQ7UnywKpXGGJx6zhANa0J8A0Pg/Xtz3Obr1vL98cxTWaO6TmKlHpxo9IBrF39C5TiO4ziO0yE+oXIcx3Ecx+kQn1A5juM4juN0yHGhoTowxjqF2x94gNKDfaw9OXnjVkprmKUDezmW00KF149HRd+TadB1NM5Dq8ZryjXR1ySlEpkgnhuSn4luoibeKhpLSmMtVXQ5WnxJJMwbysbHF9ZuofRcWr1qeI2+nJNYfhlen67WJK5SVfQ5GYldmGhPGwMAswVu05q0YSpwvyWlDabz0idydaRlTT8vPigaty0t55AaZe3L3DCPmQfvFy+uMmvCpsXra+8Y98mJ21gj0Cv+PPk86yCqeY4FWJzl62BeYv8BQC/LW5DM8rVXE18p1STVuAqo5bhPJkRnNhjEC0vaJFh74yStPk/SZ0Guy4RsN0kn5F5wZJr9ia678WZKn3qyaBUlXmOPBnEDMDzMHkDzs9xGamqkLTI1ztqQicPsw5fNiIdSittgboo1VKXA9+PDE5xeEP3OoX07Kd0/zH2ayrD2Zf0Gjo9Yrei9lcfAQkHimKpPVi9fdyth3xE+J9XAqmYoSNzPktyv5+b5ulANUUX2Vw+mgvhIFSTWqe5fLKkPolyHKroSEm36u6kGbGiIdXspuU4q1UYNVU3OIZ3hY3p6uJ9HJebkhnU8bjau42srK/fDe3aw39lt93D6aFj2CZWZPcXMrjCzO8xs3sx2m9nnzOz0JvueZWZfN7M5M5sws38xs3XN8nUcx3Ecx3ks0eoJ1R8AeBaAqwD8FMBGAG8C8BMze3oI4XYAMLOtAK4DMA3gXQD6AbwdwDlm9tQQQvuvWTiO4ziO4xwntJpQfRjAr9RPiMzsswBuA/CHAH41/vhdAPoAXBBC2B3vdxOAbwC4FMDHu1ttx3Ecx3GcY4dlJ1QhhBuafHafmd0B4Ky6j18F4D8WJ1Pxft80s3sBvAYdTqh0zX1402ZK332IY56dfMGFvP8or+c+KAKj9CHWrgyLvqcm/hdINll/TvH6r/Ww2KQsa8aJAutTTHUQSVFCiLbDasv7Wqk2xMRPJycHJMTnpFJgDYHqgxL3sv+QhjesDYpniKrEFtjbKynxE6sLrBlYCcWC+EKJ5qgvx1qKbEq8vdSXqSreLyVOm8RjzCZZR2GiYyiJXc8J53Fct6lx9u+592Yel4cWOL9iYB3F1BzXb8sWvm4mJ9kT6sjh/ZQuT/MYHRlkbQsAZHvYg6hXYk4GuTb6+zmPOdHDZCXmZK3CbZgRjVUlIb5NWdZVAEewHOks10e1MapHMvHTadRU8eFV0YyVZYyMTfO9ZWZ+J6U3rGVdCACUJM7nnGioeqQNMinpE/EsGh7ie9PYFGuscik+56EBvn/2iOZqNsN9sm4L+1xtXs/H94lvVv8A94nqyDTuW0X0qGW5LlM1vs5na1y/lTBX0TL42tB+h8RuzRdZLJhMsrauWFw+1l1Nxp2O09BCAyUWTY3jXOsvmMn3jRze18c3s23btlF6wzr2GhsWL7RmMStV15UQ78WK3I83rBWtXZn7qFfu94eO8P3v1tvYM252QXwKj4K23/KzSJW5AcBYnN4CYD2AHzXZ/SYA53VSQcdxHMdxnGOdo3nL77UAtgD44zi9+HPkQJN9DwAYMbNsCPxz2swuAXDJUZTvOI7jOI5zTNHWhMrMzgTwtwC+D+BT8ceLz9WKTQ4p1O1D20MIVyESNmBOrAAAIABJREFUu8OszXefHcdxHMdxjiFWPKEys40AvoLoTb5Xh/CQk9HiAne2yWE52eeo2C7+OolnP4/SP7jhu5Qen+K1Uuvhlc2CLHSGNK+15sus58mIL0tKA+cBSIg3SnKUPTCKIvVI7t3FeVY4z5p6JsmUMyEaqoWqxkTjrg0Jyb/Ka/rpCa7PUA/v3y+L7iMPiI+KxCirpW7itLZPWbx0RMM1mOf6rYRQ4X4MqmEKXIeScZ1FqoKFBf6gIOZdiQz3QcZYCyLhHTFmrKvYLDElawnxgRJ9kbQYerO8/WBBYhEeYY3UoGhVhk5kr7G0eM+EQmMfTImXlfqZZUW3UJMYlT1yy1mT0HHBdciJl9e8yByKpUY/m+Uoi39ag4eTaqhUMyXalmRDzDLJXrSKs6IHWreGfbxCrfG3ZUliOK5dxzqrkRFOrxli7Vy/xKEbGWXtyeFD4kslOrg1oqEaXMPlqaarKPVNSB9KWDdURN/ZL3HwyhXW7RUqMqZ6+PzGJrjNC2ED2mVOPO3KZfGc03EiGtdkQmNA8kmrR576Blb0XqMSKNXQSv7r1vEYyGS5jRZEo9onXl19Eut1cIB1d1s2s445LTq6+Rm+T2Qz3CeqIQOAqvj6pcUbMT/P185kinW+I4N8zurNNS4ecQsl8RJrJSxbASuaUJnZEICvARgG8JwQQv2denGpb1PDgdFnE7rc5ziO4ziO81ii5YTKzHIArgFwOoCfCyHcWb89hLDPzI4AuLDJ4U8FcEs3Kuo4juM4jnOs0sopPQngswCeAeCSEML3l9j18wBeYmYn1B37PESTsKu6VFfHcRzHcZxjklZPqP4KwC8iekI1Yma/Wr8xhPDp+L8fQPTG3n+b2UcQOaW/A5EB6Cc7reRAP6+hZzO8vrtl6ymUzvRz7KvpWV47nZf16ZDmZpiQBUqr8LyzL92oc0iqb4eInmyQ16CrSdaOVCu8PmwSkSstwoMEdA2ftSQ1kZbUNMLXrPhAzfD2yjjHNVINQEqMTtLiDZOSRX/VhGlswYpoEsZa+Kw0I5nhcZKqap9wP0/OsM9TpSa+U0WJBSg+VQnRxxRFZyHFIzMkMcbk8pPqYc2JfD5HZniMZHq5Taf7OYMpY23LmgQfv2Ut6yASA3xdlSea6Nj2chsdyXMZIutCYYLbcLgwSunqEOdX7GVdREJ+81VEJ5bIcRu1RGQSyZT6WDEN/j1CVTVYEpNN9UHVEp/P6KD4evU21mfNMN87Nm1i/7KNm1htMZCTPGbZZ6qnyvqWraNch1KR9TW9omnq7+P69Ii31/w0x/4bO8IvgNfETyiX5kFjqh+Se0tFNFQ1qW85yx5IJeP2WgkLRdWcysWssVLl+Jr4j5ktr73LSJ/lsnwtZxISb1G+P7Zu5DFw2mmsO85keP8DB7hPBvpZyzci3o9Z1WCJZnb/AdZrpiW27I77WaO7Y8d9UNJpicko42ouz/2ckWt3zRB/76sma3xG4tGKvjM0RMFsn1YTqifHf18a/1M+DQAhhD1mdhEiZ/UPASghErC/zfVTjuM4juM81mnllH7xSjMKIdwB4PmdVshxHMdxHOd4o22ndMdxHMdxHIc5Gqf0R5zJ2SlKp3t47XR0PXs+nXrmWZQ+uG8npe8VXcO8tEJF4o/l87x/73yj901fSXw9dnFMscKCxPIbHOF0krUlNTExSsr6bk1i49XEB0UFPIkGLQjrGBr8d2Rv9UFRHyzdX31SqqKLqEkMOI3jVCxLgTwEmmJJ7rdkVkySJMu0eCZVCry/SDvQl+H8U7KGX1uQAyQmWcW4T+ckfmFaYqSt2cb5ndrP/j+FIzzmHji4j/NbJ945I6x9mQPr6NbJGDxxlMcoAKRnuI57p1jztCfP51STeFw9W1inVZP4hv0bWc+jMb/WGB+f7dXYd7uwHP0SU0yDsqlWpuGy0RhpOs4rfJ2L3BO9feJ5V+Uxsn+S2w9oHGeja/gcSvPcB2WIlnCBtSNH9rM+EjKuSxKXbnqaNVdz03wxZk30kAu8vy3wOVUKrL+ZFU+mlGhpLMWNaBIzM53m7YXAY6JabYwb14qgIlTVRMmziKTEP8z1cJ16erjfB8Xbq0/iK6bleI29OtLPx2/dwBqqjLThjHiFDYj2cFS8y/Tr5N67d1B6/37WTBWkTzduOYHS45M8JvqHWecGNF7rYxM8zmbz/J2VFr+06Vn2U1Pdr/oMNlzbXbAX9ydUjuM4juM4HeITKsdxHMdxnA7xCZXjOI7jOE6HHBcaqkKBtSLQeFpZTg/08vrzPvEXGpe4eUNJPn7exAdF1s9Hao3N1gde16+JeGJqSOK2jfIaeConPlUSD6um6SAaqBqfg+ouFgqsYyiqz0pYXgtSVT+dquiTZAFaNVk10SRUxYdKNVRlKQ8H9qAVNdGzqNeL1qEny7qGBDQwHdcpk+I+liZCn/RhSfpodoa1Ln0z3EcZqW/POj6f7WdwDLbJu1i3ML1H4jFuYJ3E1vNZIxUqrJ05/CPWLKwJjbGt+os8rg+McyPsmGKtxMAo+9skRFvYM8Tjcugk1nLUanzt10T7l89zm7ZCvW6UmvrHSXzDhPRRVfQ/qqnK5UTzJXH1NK5dM/e1ZGL5GIuJAvfj4AaOXZfq4zJRnKdkSfIrFfjeUBbNUs961ij1yDgpBx53Jh5KKbnuinOs78nLvalc4nFekXvr3sB6nGnwvVX7bCVkxHsrKd8Rvb18jqp/3LCBdb3rReerxyfEC3F6ir3D+tNcn3WqeZL75Yy04aTo4MbH2SusIp5Mc3Pc5rt27ubt8zxmRkf43jI1xdtHRvj8Vd8ENOqyGr9iRBOlPlJ6/xQfqyC65GqDaKpzEZU/oXIcx3Ecx+kQn1A5juM4juN0iE+oHMdxHMdxOuS40FCV5mUNvci6DbEAwaED7Mdz8DCvzR4qcn4nnsixANWHZX6B9x8XSQIAVNewLiC9lde4Eyle/90ka/RVyVM9M/KioZqe5jX2YkFi6aW5Pvc9eBel5+f5eF3S1nQ6wxUslHg9ulRmbYuJ5iBI/C3VJGiBQY1QVoCukTeuifO4yeo5i2eShB9ETeI1lgucXy7F2wt57rPCAqerM+olxvqe1CDnlx7k8xkWT6XR9azT23AS91kyybELC3tFL7RT6tvTeHu4fz9rlm4+wONoTLy8hlJyjqLXOaVPPN9E+1Gtcp3Kkm43spWOqkRSY6yxXkk1VwnxyjExpko0GlVx+TWNH8aUmujWDkxym6WNr7WkxPHMia/UyIjo0uScwgLXoifHx/eKH9na9RwbT7V/+RmubzKvGi1OF8XDKC8x1xbEe2ysxlrFsTTXL5kVvaqKHVfA1hNP5DxE46meSX3iL7b1xK2UzomeZ1r0lIUJ1grOTPP2Ug9rrgYkbmlZrpsJ6YODhw7x9gnWUB0ZZ/1kTb4DS2Vuw6Eh8fqScT1+kD2hjhzh+4RqaAGgIv2USPH9pyfL40D3V7TP9GLTa9eOQmun+BMqx3Ecx3GcDvEJleM4juM4Tof4hMpxHMdxHKdDjgsNVUU0TKUCawg07l1+TnxWFnj7jPha3TfJ68lDA7wGP1fiNftysjE2VJ/oCsbHxyhdkVh/c3nenk6LFqOFL9TsPJ9jtSY+WIHX4KdnWD/TI/446ouSz3P+w4OswygWZc1/jn1OZufEK8dYt7Fp/WZKZ0WjlRJh3B133Y5WpCTgYEXGRUI0UKp3SYrepSK+VAXRiRWkT7JVifUnepy+FOsshhLs0ZQZlNiC/dyHNfGaCUXxXxMN1dAot8dQhfVGWyvc57X1rMm6X/RRAHAow3kMn8/eWFnRUFVlHG44X855vXgOiUaqJLH10lm+ZVXK7Wmocr3cRqqJUk1VVnQbKdF1NPPTqUf92yqiHQmq82iIignMSxtMyynPz7GX18w461f6JDapZIdKUA87PseE8faFPF/7QTRXyYEhSqdF7zOxl+8NQfRFoyecTOndk3zdTZTZ8wiSv8YCLKmn3QrIie/TkSPsnzY8zOc41M/3x16pU1U0SVNjrFk6eJA1TqWi6C1HWCc2InE9iwvcJ0fG+DtN4zGGoJpVSjZooopy7yvJva8i29XXCivoA9U8qbdWpoe/I/TaK0sditKGqntrde0eDf6EynEcx3Ecp0N8QuU4juM4jtMhPqFyHMdxHMfpkONCQ1ULrLNQjyL1jhkXT40Dew9wfqLT2LH7Hkrryqoutfb1D0AZ28/eVxOTrFkqlXmN+/7dfA5ahq5p63pvMsE6gf5+1r+oVqMo8bmGxUdkyxb2XdH1Z9U0HTrE3l5bt7LvSrkq8RPHWEuzZg3rIHp7Ja5esv24SsF0nHAe5QKnVbeWFN3EQom1HmZyvKzpp6rcJ2WJ5ZfO8eW2scg6i/l+Tk8nWPfQu5fzm9vL20/YxGNgu+jW+vPy+ynB53tHgsfw7PZGn5cnnX8CpQvigTQ7xdq7SuA6bT2Fx12pyp5DFsRrRvU+BdFYVVg/1Iq+AYktmFo+Vl9CrjvVEyk1uTelMnx8rSReZhKwLNksnpjE5SxKv9aSnB5cK9eWxM5T7V9F6pAQH6upMdb3zEyxPgeBjy+KJqtnkOvTJ/ceG2D90fgMX8fjOYnN18u6PZPnAkF0cLXQvoZqcoK/Q9RDrtTDbTgvut1D4sOk+qAF8airlORaEw1TSbwX9+3n77SqCONKJdF7FlRryOMyL7q4mmisimXxk5PzaYiXqF+iK7id63egXksa01fv761otX+7+TXDn1A5juM4juN0iE+oHMdxHMdxOsQnVI7jOI7jOB3iEyrHcRzHcZwOOT5E6TUxNBQBngUWlu7as4fSOx64j9IFERuXVaRZWV6ANznHQtq4FpwS8Wqj4G35tIrQ9fiKhHktTbJRaKO+jj/IL7CIcmKCjy+IcWevGByqO+CuXbso3T/EQlINhrxn705KlysS8PUoAlUW5WWDWpXzKBTFfK7G203M6HT/WkLGRUL6AFy+1qdSZYNaNUcd6GfzvkJNBN+7WSibnWDD2VOefAalRzJc37K8aLBnXgK0stcqMusbDWwne1hsW1xgUXh2hAXSfRkZN0kWHFuFb0G1MvdBX45F5JWq1ClwGwIzWI6MBCVPJDQ/Lr/BbFAzFPFww/EirNWArHpdJJp5DcqlILp2TEvw+PF9u/lwESj3SFDt2Sm59sVIeUGCGx+QF3AOzXAf7J3h8jZvP53Sp2znF2CCiOwP1dZSeibD9c32SINoFzQootu/lyyIWWrKeJwURcQ9bXwtlcV8uiL3t2kJflyWe42egZqpzs2KsXNFDWP5eA3KrQLwkpRfkGDIZflOVPPXmn7/Qce9BAVvIgBveBFLLoZah6LxRhNdptP8AX9C5TiO4ziO0zE+oXIcx3Ecx+kQn1A5juM4juN0yLIaKjN7IoDLAFwAYCOAPIA7AfxFCOEa2fcsAH8N4NkASgC+AuCtIQSOKnkU1DSgqKzvBjmNdJp1G2VxB1zQ4MqSv64vq+4hWLO1Vl0jbhX0VDVXurmF0+cK1qSXY0rM+TStZNJsYjk4wPqdgqzxz8oaf0MbrsTprU3KkOCYYkaXSMtwl2DKCwXWjmiTNugSElzefHVGtvPvFdUhjBW4jU5J83aTcTkjAbFPzbFBYiXP++/eL+aySe6jfTWub3mNmMGmuT0AoDArGijRPFlStBaiOyuXWC8DMajVcZwQLUZWAt8mkzwOgcNYjqQE8tVflKpFUY1Tw2Wqw1iu25oad0r7VCuszVG9KNB4rczJ/es+MYkszbLW7gwJXJ7uYaPPA2ISWRKTyQL42t83xX1yYJ6vqwVjg9qZI6KxKvO9Zs0m1ljlRrZQGqpH5a1IS3BlbcNyqb0A2gCQFrPUioyLvGisVHc2m2GdbUW+s6rVRtPcejRweyHwOei9qcGcVUe27J8UQ9t0ZnlD3VZG0w36pLD89+FKvq80zwadli2fVlpppLph7NlKlH4SgAEAnwKwH0AvgFcB+LKZvTGE8HEAMLOtAK4DMA3gXQD6AbwdwDlm9tQQxOrccRzHcRznMcSyE6oQwlcBfLX+MzO7AsCPAbwVwMfjj98FoA/ABSGE3fF+NwH4BoBL6/ZzHMdxHMd5zNG2hiqEUAWwB0B9AKZXAfiPxclUvN83AdwL4DWdVtJxHMdxHOdYZkU+VGbWB6AHwBCAXwTwQgCfjbdtAbAewI+aHHoTgBd1WsmFAq9XW3l5Pc7adexjctaZ51I6K942+/beT+nCAnuErEjv07B828p3SoOuqnCL06qsUF+P9mkQii2LBlsul3gVdyDDmoOE+AUlguoglg+uWa5xhSbKjXoeRQMyV6HeKVxGbx9rSUw0SLWq6gJEV2G8/0JFgjNLG2i85zk5Xi2b0iX5vZOR7b2c4aGxSUpPG6cXMnwd7S/xOO8Rf59csvH2kKhxJUJB/GZYPoNygrUfs0ECPvcNUXrN2vVc53nxxxEvLdWCtEIlSgn1gZJxqJ53rZSRCdFhqHbQklzfhqDntcbzabj/SBl5GWf3lVlTNHmQNUwp8LU0Pcf5FStcp4roJwupPq7OMO/fr32SluDGQ9sp3bP+NN5dytM0ZFyqx1KpyNeh6thWQlXaXNMVHUgaxFuDB4sOrEEP1MJLq1ISfWWDTx+nqw36T/FHk3EdpM1SMk4b9Ezqa4XlCVI/DTQPNPNOZBriLTfotFpopKQPGzRVXZD1rtTY868AvHGxHgC+AOBNcXpT/PeAHhR/NmJm2RBC+8pAx3Ecx3Gc44CVTqguB3A1gM2IlvCSePj38uLP/GYTpkLdPrTdzC4BcEk7lXUcx3EcxzkWWdGEKoRwN4C74+Q/m9l/AbjGzJ4GPPT8ONvk0MVnzw3rNSGEqwBcBQDW7Pmf4ziO4zjOccLRxvK7GsA/ADgdDy/1bWqy3yYAE50u96k3jMl6rHrTbNq8gdLjE+zLEtKsoRpZy1WfHOfVy+kZ9vNZWODYVgBQlbhtut5bLou+R9b1G30+OJ1Iamy/5eMStYpb1CQAFpenMc6kQqpl6ZdYVykpXvVMQc5PdRIZ8YGZmG6toUqLrCCZ5kLSImLqH+Dhb3LORdHvaMzIXJJ/Q9RE21cWXUKPaLhqA6I9WSt6osOc3pNgTdTeCmughopSvvhazcyzhgq90j4JPp/qVOMYshq3WUb0MikRb5T///bOPtiu6izjvyf3I8lNmgghEEiizHTCgBUHKg2CVq1AsVNmakdarWhFaaEwVavQVlPFqa1V6kxtQ5Rai9TK0BAKTqmtdaZWZFpqaz+QJtBCOzBACF8GuEnuV+7N8o+1z+Ts95x7zz3Z5O5ze57fzJ5z99rrnrP2c/Zee521n/2+IWdkzG03NlD+XpdOlds4FU+M4Hkam+oyIkuMaUf0isztNWk5UWO+seiNDF6UFutLx/MUBqLvKjQhxjSaCW1+erp8bs2EvHJpuOyJGlhe/o7j56+IPrOg4ehYWYORkXKSyLUbyh4qRU9U/E7C9ujn3D9ajvk0tq/cP09OxHyPnYn5XeM1KPaH0Qs3HAyRK1aUNR4dDTHgQpyqgdj/BqKHKdISoyl8h/GomwrXp4Hg+WrJWdkh92xrYMXod6INnUxUMV9g+O8Onqp4zYnbO8Wxmg9HGim9cZtvdUppN/AMcFabepuBe4/wM4wxxhhjFgVzDqgkHd+mbAh4M/k23v1F8e3ARZI2NtU7jzyDdduL1lpjjDHGmB6k0y2/v5e0ihwFfTc5/cwlwKnA1SmlxtzqB8gG8/+U9BFypPR3At8BbjoaDTfGGGOM6RU6DahuBS4DrgTWAPvIUdLfnVK6s1EppfSYpJ8HPgT8FYdz+V39YoRLiHFFmI4eqnLclZEVIRhOuDe7NPg+1h63obS+elXZgzU+PhHWgxcFmJoue0WGQt64eL92376Q943g7Wi5ZR3iohwqa7IkeDmi72HfaNlvMzZW/vzhkP9w5cpy7Ji168v5tWbCHeyZyZA/K+QbG9tf9rENBF/EyEjZY7BiZfAPfaWUOrItI8PBK5JirJjy+r7RZ8vbQ26/JUtC7JWQ4yzNlPdhMMQQGh4st2fpUPn9JgdDTsmR8newZtma0rqGnyqt76Z8HI4fDHGklrQY8Uqr0dM1GL0r0UdBa3yb8YPlc2N6IniIwrk2E5o0FfZh5rlyLr6kEJNosBw7bCL2DR2IeTtb8oQGr8iA5vZEDYbz/FCLdyXEuQrtiXGr4nkLbfyYwTfW4qcJ6ykc94PBcxr9PtFhE+P1pMEQ5+q5cv2hZeVzd9OpP15aX7n62NL6eIixNDlVPi+mQ2K5mbA/zz8X4q/tLXteW5Jwzocu877F+GVxfTzk+ou5ATvGceoyz1z8/Mh08Ey1fv7ccaZacvm9CHnwuqVqG47GPnRKPbMd2D6fN0op7QIurNwiY4wxxphFxpGa0o0xxhhjTIEHVMYYY4wxFTnSOFQLSow7Qsg1NRk8BTHGRwjhxEDw1iwZnDumiEIOt+GhskcAIKnsC4jvMTJS9nVNhbgjk9PB79PhHn0MRhPtMtGLMR1izxwMufGGgq9iYKDsXVke/D3Dy8rbDwYfxNhY2VtzKOa5i56wGL/oYPe+h6GQL3AofK+TE+U2TE+WNVm2NHxHY2X736GQX3A6xNY6FLx9S0JsrumD5foHJsv+ob1TZc1Wr9hYWp+e3lVaHxsKPohD5e90+XTw+8SfT2XbH5Mq6zO8rLV7GFxaLjs4U/6fieB/WabyccVYub7C957CcafB8vb9B8t+GbWNJzw7k5NljRV8YgPhPBsYiu2JMZLidz63G6ZjpJs28YVmYqyqGJor5okLfcPA0rJGLfkKYyyt6MFaUv4OD0yUNRgPXdfpP1nOnbr2hLJH9fl95bhR0aM6NlY+jqM/NPrcpoOPLsadGn0heKqOgJZrQgf/zURow6HwHbbGQFoy5/Zu2xfjHMbPj7RadueO0XQ0PFOd4kB1amNVzV4MPENljDHGGFMRD6iMMcYYYyriAZUxxhhjTEVUR/yIlkY4ObIxxhhjFg+3pZTe2FzgGSpjjDHGmIp4QGWMMcYYUxEPqIwxxhhjKuIBlTHGGGNMRTygMsYYY4ypiAdUxhhjjDEV8YDKGGOMMaYivZLL71vAD4ANwOM1t2WxYw2rYw2rYw2rYf2qYw2rYw1n56WxoCcCezaQtCMGyjLdYQ2rYw2rYw2rYf2qYw2rYw27w7f8jDHGGGMq0msDqtvqbsAPAdawOtawOtawGtavOtawOtawC3rqlp8xxhhjzGKk12aojDHGGGMWHR5QGWOMMcZUpPYBlaSlkq6T9ISkcUlfk3RB3e3qRSS9QtI2SbskHZD0qKQdkk5pU/c0SV+QtF/SXkn/LGltHe3uZSS9R1KStLPNtnMlfVnSmKQnJW2VtLKOdvYakl4u6c7i2BqTtFPS74U61m8WJG2StF3S44U+35V0raSRUK/vNZS0UtJ7i/5sb3G+XjpL3Xn1e5KWSHqXpIclTUi6T9KbjvrO1MR8NCw0ubQ4rx8rrjE7Jf2JpGWzvO9lkh4oNHxI0u8uyA71KL0Qh+oTwMXAh4GHgEuBz0t6VUrpyzW2qxd5N/AzZKPgfcA64O3AtyT9dEppJ4CkDcDdwAvAFmAlcA1wuqTNKaWpOhrfaxQ6bQEOtNl2BvAfwAPAH5LjsVwDbAJes4DN7DkkvRr4LPBt4H3AfnJMlg1NdazfLEjaCHydfH5uA/YC5wDvBX4KeF1RzxpmjgOuBR4F/hf4hXaVuuz3/gL4I+AfgP8ha36LpJRS2n6U9qNO5qPhCHAT8N/AR4GnOXxcnifpF1OT6VrSFUW924EPAa8EtkoaSSldd/R2pYdJKdW2AJuBBFzTVLYM+D5wT51t68UFOBcYDmWbgAng5qayvwPGgB9tKju/0PryuvejVxZgO/mCdRewM2z7PPAEsKqp7C2Fhq+uu+01arYKeBK4A1gyRz3rN7s2WwodXhbK/6koP8YalnRZCqwr/j6r2P9L29SbV78HrAemgG1NZSIPxh4DBure5zo0BIaBc9v877VF/fObypYDzwL/GureTP6BdUzd+1zHUvctv4uBGeBjjYKU0gRwI3BO8UvOFKSU7klhdiml9BCwCzitqfhXyAf6o031vgg8CDhIGyDp58jH3zvabFsFXEAepI42bfokubPoZw1/HTgBeE9K6ZCkFZJK/Yj168iq4vWpUL4HOARMWcPDpJQmU0pPzqPqfPu91wFD5AFYo14CbiDPAp7zYrS7l5iPhimlqZTSPW02/Uvx2nyNeRWwhiYNC/4WWAG89kjbupipe0B1JvBg6DAgT4cDnLHA7Vl0SBL5Avdssb4eOB74RpvqXydr3tdIGgCuBz6eUvpOmyqnk2+HlzQsBrP30t8ang+MAuslfY98cR+VdEOTz8L6zc1dxeuNks6QtFHSrwJXAltTSgewhl3RZb93Jvk2/wNt6oG1jawrXp9tKmtoFPX+JvlHQV9qWPeA6kTyr7JIo+ykBWzLYuUS8hT2rcX6icXrbLoeK2npQjSsh3kb8GPAn86yvZOG/XxcbiJf6D8D/Dt5VuAfyZreVNSxfnOQUvoC+di7gOxDe5R8+/n6lNIfFNWsYXd00++dCDxVzErFemBtI+8i/4j6t6ayE4GZlNLTzRWLAf//0aca1m1KXw5MtimfaNpuZkHSqeQp1q+S/RdwWLNOurbb/kOPpDXAnwPvSyk9M0u1Thr283G5kmxe/WhKqfFU3x2ShoErJF2L9ZsPj5A9O7eTL0CvBbZIejKltA1r2C3d9Hu+7swTSVvIs9JXpZSeb9q0nOxDa0ffHp91D6jGyWa5yLKm7aYNktYBnyM/0XJxSmmm2NTQzLq25/3kp6qun6NOJw37Wb/Gvn8qlN8CXEH2n4wVZdavDZJ+jewbPSWl9HhRfEfhRbtO0qfwMdgt3fR7vu7Mg+I29PuBG1O5uSM0AAADlklEQVRKN4TN42QTezv69vis+5bfHg5P1TbTKHtiAduyaJC0mjz9+iPAL6WUmnVqTFvPpuvelFK/zk5tAi4HtgInSTpZ0snkDmCoWD+Wzhr283HZ2PdoqG5M/R+D9evEVcC3mwZTDe4kz/6diTXslm76vT3AusJ/GuuBtUU5FuQnyT/a39amyh5gQNLx4f+GyWb1vtSw7gHVvcApxRMtzZzdtN00URh/PwucAlyUUrq/eXtKaTfwDPnR2Mhm+lvT9eRjfivwcNNyNlnPh8mPCO8EpgkaFp3FGfS3ht8sXteH8oZn4hmsXydOAAbalA8Vr4NYw67ost+7lzxwPS3U83UHkHQ2+cm+bwBvTClNt6nW0CjqfRa5j+1LDeseUH2a3LFc3igojIO/DXwtpfRYXQ3rRYqn024l31Z5Q0rpq7NUvR24qDnshKTzyIOGfs4evhN4fZtlF9kY/Hry9PYLwBeB35D0kqb//02yh6ifNdxRvF4Wyt9CHgDcZf068iBwplozHLyJ/ITUfdbwiJhvv/cZ4CB5prBRT+SZmN1Au9ABfYGk08izUo+Qf7DPduvuS2TrxJWh/EryLf/PHa029jJqfdBhgRsg7SBfyP6GHNDzt8i/KM5LKd1dZ9t6DUkfBn6fPEO1I25PKd1c1NtIfnroeeAj5A74ncDjwCv69ZbfbEi6CzgupfQTTWUvJ3es95P9LhuAq4G7U0oX1tHOXkHSjcDvkI/B/yJHXX4D8JcppS1FHes3C0UMtC+RzejbiteLyNHPP55SemtRzxoWSHo72eJwEvmifQe5j4P8dOQL3fR7kj5YbPsYOVL6L5MfDLgkpXTLguzUAtNJQ/Jgfhd59nkLeXDZzA+af8RLuor8UNSnyU/8vhJ4MzlG3QeO3p70MHVHFiX7V/6afE92ghwL5MK629WLCzl+TZptCXVfRj7IDwDPkSPYnlD3PvTiQptI6UX5zwJfIRssnyZf/F5Sd3vrXsi3pv6M/Ct2ipwy6h3WrysNN5Mjoe8pNPwe+SI2aA3b6vXIHH3fyU315tXvke/O/HHxvpPk2etL6t7POjUsllmvL8An2rznW4HvFhp+nxwoWXXva11L7TNUxhhjjDGLnbo9VMYYY4wxix4PqIwxxhhjKuIBlTHGGGNMRTygMsYYY4ypiAdUxhhjjDEV8YDKGGOMMaYiHlAZY4wxxlTEAypjjDHGmIp4QGWMMcYYU5H/Bwy4G9F19iUBAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: automobile (1)\n",
"Image #1: deer (4)\n",
"Image #1: dog (5)\n",
"Image #1: ship (8)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: automobile (1)\n",
"Image #1: deer (4)\n",
"Image #1: dog (5)\n",
"Image #1: airplane (0)\n",
"[Validation] Loss: 0.0092 Accuracy: 85.6300% Time elapsed: 1.9890s (total 10000 images)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8x173RoUAhwM"
},
"source": [
"* Black-box model"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 863
},
"id": "pPVJpWyAAd9K",
"outputId": "2cd9401d-4055-486d-9960-617bb4979bf4"
},
"source": [
"import time\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"black_box_model.eval()\n",
"start_time = time.time()\n",
"\n",
"with torch.no_grad():\n",
" running_loss = 0.\n",
" running_corrects = 0\n",
"\n",
" for i, (inputs, labels) in enumerate(test_dataloader):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" outputs = black_box_model(inputs)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" running_loss += loss.item()\n",
" running_corrects += torch.sum(preds == labels.data)\n",
"\n",
" if i == 0:\n",
" print('[Prediction Result Examples]')\n",
" images = torchvision.utils.make_grid(inputs[:4])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[:4]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[:4]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" images = torchvision.utils.make_grid(inputs[4:8])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[4:8]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[4:8]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
"\n",
" epoch_loss = running_loss / len(test_dataset)\n",
" epoch_acc = running_corrects / len(test_dataset) * 100.\n",
" print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, time.time() - start_time, len(test_dataset)))"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"[Prediction Result Examples]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: dog (5)\n",
"Image #1: automobile (1)\n",
"Image #1: truck (9)\n",
"Image #1: truck (9)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: dog (5)\n",
"Image #1: automobile (1)\n",
"Image #1: truck (9)\n",
"Image #1: truck (9)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: airplane (0)\n",
"Image #1: horse (7)\n",
"Image #1: deer (4)\n",
"Image #1: dog (5)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: airplane (0)\n",
"Image #1: horse (7)\n",
"Image #1: deer (4)\n",
"Image #1: dog (5)\n",
"[Validation] Loss: 0.0088 Accuracy: 86.1400% Time elapsed: 2.3873s (total 10000 images)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G02hVADvsf3-"
},
"source": [
"#### Prepare Adversarial Attack Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Coaxy34Cshxr"
},
"source": [
"def get_distance(a, b):\n",
" l0 = torch.norm((a - b).view(a.shape[0], -1), p=0, dim=1)\n",
" l2 = torch.norm((a - b).view(a.shape[0], -1), p=2, dim=1)\n",
" mse = (a - b).view(a.shape[0], -1).pow(2).mean(1)\n",
" linf = torch.norm((a - b).view(a.shape[0], -1), p=float('inf'), dim=1)\n",
" return l0, l2, mse, linf"
],
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NWmJMMap_15E"
},
"source": [
"# FGSM 공격 함수\n",
"def fgsm_attack(model, images, labels, eps):\n",
" # 이미지와 레이블 데이터를 GPU로 옮기기\n",
" images = images.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" # 입력 이미지와 동일한 크기를 갖는 노이즈(perturbation) 생성\n",
" perturbation = torch.zeros_like(images).to(device)\n",
" # 손실(loss) 함수 설정\n",
" attack_loss = nn.CrossEntropyLoss()\n",
"\n",
" # required_grad 속성의 값을 True로 설정하여 해당 torch.Tensor의 연산을 추적\n",
" perturbation.requires_grad = True\n",
"\n",
" outputs = model(images + perturbation) # 모델의 판단 결과 확인\n",
"\n",
" # 손실 함수에 대하여 미분하여 기울기(gradient) 계산\n",
" model.zero_grad()\n",
" cost = attack_loss(outputs, labels).to(device)\n",
" cost.backward()\n",
"\n",
" # 계산된 기울기(gradient)를 이용하여 손실 함수가 증가하는 방향으로 업데이트\n",
" diff = perturbation.grad.sign()\n",
" # 결과적으로 노이즈(perturbation)의 각 픽셀의 값이 [-eps, eps] 사이의 값이 되도록 자르기\n",
" perturbation = torch.clamp(perturbation + diff, min=-eps, max=eps).detach_() # 연산을 추적하는 것을 중단하기 위해 detach() 호출\n",
"\n",
" # (최종적으로 만들어진 공격 이미지, 노이즈) 반환\n",
" current = torch.clamp(images + perturbation, min=0, max=1)\n",
" return current, perturbation"
],
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "oATgOv1Vs3LV"
},
"source": [
"# PGD 공격 함수\n",
"def pgd_attack(model, images, labels, targeted, eps, alpha, iters):\n",
" # 이미지와 레이블 데이터를 GPU로 옮기기\n",
" images = images.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" # 입력 이미지와 동일한 크기를 갖는 노이즈(perturbation) 생성\n",
" # 노이즈 값은 음수가 될 수 있으므로, 평균이 0인 균등한(uniform) 랜덤 값으로 설정\n",
" perturbation = torch.empty_like(images).uniform_(-eps, eps).to(device)\n",
" # 손실(loss) 함수 설정\n",
" attack_loss = nn.CrossEntropyLoss()\n",
"\n",
" for i in range(iters):\n",
" # required_grad 속성의 값을 True로 설정하여 해당 torch.Tensor의 연산을 추적\n",
" perturbation.requires_grad = True\n",
"\n",
" # 현재 공격 이미지(원래 이미지에 노이즈를 섞고, 그 결과가 [0, 1] 범위에 속하도록 자르기)\n",
" current = torch.clamp(images + perturbation, min=0, max=1)\n",
" outputs = model(current) # 모델의 판단 결과 확인\n",
"\n",
" # 손실 함수에 대하여 미분하여 기울기(gradient) 계산\n",
" model.zero_grad()\n",
" cost = attack_loss(outputs, labels).to(device)\n",
" cost.backward()\n",
"\n",
" if targeted: # 타겟이 있는(targeted) 공격인 경우\n",
" # 계산된 기울기(gradient)를 이용하여 손실 함수가 감소하는 방향으로 업데이트\n",
" diff = -alpha * perturbation.grad.sign()\n",
" else:\n",
" # 계산된 기울기(gradient)를 이용하여 손실 함수가 증가하는 방향으로 업데이트\n",
" diff = alpha * perturbation.grad.sign()\n",
" # 결과적으로 노이즈(perturbation)의 각 픽셀의 값이 [-eps, eps] 사이의 값이 되도록 자르기\n",
" perturbation = torch.clamp(perturbation + diff, min=-eps, max=eps).detach_() # 연산을 추적하는 것을 중단하기 위해 detach() 호출\n",
"\n",
" # (최종적으로 만들어진 공격 이미지, 노이즈) 반환\n",
" current = torch.clamp(images + perturbation, min=0, max=1)\n",
" return current, perturbation"
],
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "oCZnrhBJwMu1"
},
"source": [
"# MI-FGSM 공격 함수\n",
"def mi_fgsm_attack(model, images, labels, targeted, eps, alpha, iters, decay):\n",
" # 이미지와 레이블 데이터를 GPU로 옮기기\n",
" images = images.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" # 입력 이미지와 동일한 크기를 갖는 노이즈(perturbation) 생성\n",
" # 노이즈 값은 음수가 될 수 있으므로, 평균이 0인 균등한(uniform) 랜덤 값으로 설정\n",
" perturbation = torch.empty_like(images).uniform_(-eps, eps).to(device)\n",
" # 손실(loss) 함수 설정\n",
" attack_loss = nn.CrossEntropyLoss()\n",
"\n",
" for i in range(iters):\n",
" # required_grad 속성의 값을 True로 설정하여 해당 torch.Tensor의 연산을 추적\n",
" perturbation.requires_grad = True\n",
"\n",
" # 현재 공격 이미지(원래 이미지에 노이즈를 섞고, 그 결과가 [0, 1] 범위에 속하도록 자르기)\n",
" current = torch.clamp(images + perturbation, min=0, max=1)\n",
" outputs = model(current) # 모델의 판단 결과 확인\n",
"\n",
" # 손실 함수에 대하여 미분하여 기울기(gradient) 계산\n",
" model.zero_grad()\n",
" cost = attack_loss(outputs, labels).to(device)\n",
" cost.backward()\n",
" \n",
" grad = perturbation.grad\n",
" grad_norm = torch.norm(grad.view(inputs.shape[0], -1), p=1, dim=1)\n",
" grad = grad / grad_norm.view(-1, 1, 1, 1)\n",
" if i != 0:\n",
" grad = momentum * decay + grad\n",
" momentum = grad\n",
"\n",
" if targeted: # 타겟이 있는(targeted) 공격인 경우\n",
" # 계산된 기울기(gradient)를 이용하여 손실 함수가 감소하는 방향으로 업데이트\n",
" diff = -alpha * grad.sign()\n",
" else:\n",
" # 계산된 기울기(gradient)를 이용하여 손실 함수가 증가하는 방향으로 업데이트\n",
" diff = alpha * grad.sign()\n",
" # 결과적으로 노이즈(perturbation)의 각 픽셀의 값이 [-eps, eps] 사이의 값이 되도록 자르기\n",
" perturbation = torch.clamp(perturbation + diff, min=-eps, max=eps).detach_() # 연산을 추적하는 것을 중단하기 위해 detach() 호출\n",
"\n",
" # (최종적으로 만들어진 공격 이미지, 노이즈) 반환\n",
" current = torch.clamp(images + perturbation, min=0, max=1)\n",
" return current, perturbation"
],
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Qfo788Fiv6kJ",
"outputId": "a0639c13-d0b3-40eb-fe61-9e45cc2608b4"
},
"source": [
"import time\n",
"\n",
"eps = 16/255\n",
"alpha = 2/255\n",
"iters = 30\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"model.eval().to(device)\n",
"black_box_model.eval().to(device)"
],
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Sequential(\n",
" (0): Normalize()\n",
" (1): AlexNet(\n",
" (features): Sequential(\n",
" (0): Conv2d(3, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
" (1): ReLU(inplace=True)\n",
" (2): LocalResponseNorm()\n",
" (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (4): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
" (5): ReLU(inplace=True)\n",
" (6): LocalResponseNorm()\n",
" (7): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (8): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (9): ReLU(inplace=True)\n",
" (10): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (11): ReLU(inplace=True)\n",
" (12): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (13): ReLU(inplace=True)\n",
" (14): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" )\n",
" (classifier): Sequential(\n",
" (0): Linear(in_features=3456, out_features=4096, bias=True)\n",
" (1): ReLU(inplace=True)\n",
" (2): Dropout(p=0.5, inplace=False)\n",
" (3): Linear(in_features=4096, out_features=10, bias=True)\n",
" (4): Dropout(p=0.5, inplace=False)\n",
" )\n",
" )\n",
")"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "38kF0ouoANno"
},
"source": [
"#### FGSM Attack\n",
"\n",
"* White-box attack"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "a59p9ZxHAO5M",
"outputId": "c4514e0f-fea4-4b98-b7bc-75631c03ee96"
},
"source": [
"running_loss = 0.\n",
"running_corrects = 0\n",
"running_length = 0\n",
"\n",
"running_l0 = 0\n",
"running_l2 = 0\n",
"running_mse = 0\n",
"running_linf = 0\n",
"\n",
"start_time = time.time()\n",
"\n",
"for i, (inputs, labels) in enumerate(test_dataloader):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" adv_untargeted, perturbation = fgsm_attack(model, inputs, labels, eps) # adversarial attack\n",
"\n",
" outputs = model(adv_untargeted)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" running_loss += loss.item()\n",
" running_corrects += torch.sum(preds == labels.data)\n",
" running_length += labels.shape[0]\n",
"\n",
" l0, l2, mse, linf = get_distance(adv_untargeted, inputs)\n",
" running_l0 += l0.sum().item()\n",
" running_l2 += l2.sum().item()\n",
" running_mse += mse.sum().item()\n",
" running_linf += linf.sum().item()\n",
"\n",
" if i == 0:\n",
" print('The dimension of an image tensor:', inputs.shape[1:])\n",
" print('[Prediction Result Examples]')\n",
" images = torchvision.utils.make_grid(adv_untargeted[:4])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[:4]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[:4]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" images = torchvision.utils.make_grid(adv_untargeted[4:8])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[4:8]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[4:8]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
"\n",
" if i % 10 == 0:\n",
" cur_running_loss = running_loss / running_length\n",
" running_acc = running_corrects / running_length * 100.\n",
" print('[Step #{}] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(i, cur_running_loss, running_acc, time.time() - start_time, running_length))\n",
"\n",
"epoch_loss = running_loss / len(test_dataset)\n",
"epoch_acc = running_corrects / len(test_dataset) * 100.\n",
"print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, time.time() - start_time, len(test_dataset)))\n",
"\n",
"print('[Size of Perturbation]')\n",
"print('Average L0 distance (the number of changed parameters):', running_l0 / len(test_dataset))\n",
"print('Average L2 distance:', running_l2 / len(test_dataset))\n",
"print('Average MSE:', running_mse / len(test_dataset))\n",
"print('Average Linf distance (the maximum changed values):', running_linf / len(test_dataset))"
],
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"text": [
"The dimension of an image tensor: torch.Size([3, 32, 32])\n",
"[Prediction Result Examples]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: dog (5)\n",
"Image #1: cat (3)\n",
"Image #1: automobile (1)\n",
"Image #1: airplane (0)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: bird (2)\n",
"Image #1: frog (6)\n",
"Image #1: frog (6)\n",
"Image #1: cat (3)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: automobile (1)\n",
"Image #1: truck (9)\n",
"Image #1: cat (3)\n",
"Image #1: airplane (0)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: frog (6)\n",
"Image #1: frog (6)\n",
"Image #1: frog (6)\n",
"Image #1: automobile (1)\n",
"[Step #0] Loss: 0.1351 Accuracy: 6.2500% Time elapsed: 0.4378s (total 64 images)\n",
"[Step #10] Loss: 0.1364 Accuracy: 6.2500% Time elapsed: 0.7472s (total 704 images)\n",
"[Step #20] Loss: 0.1371 Accuracy: 5.7292% Time elapsed: 1.0010s (total 1344 images)\n",
"[Step #30] Loss: 0.1372 Accuracy: 5.7964% Time elapsed: 1.2344s (total 1984 images)\n",
"[Step #40] Loss: 0.1375 Accuracy: 5.4497% Time elapsed: 1.4703s (total 2624 images)\n",
"[Step #50] Loss: 0.1371 Accuracy: 5.7292% Time elapsed: 1.7041s (total 3264 images)\n",
"[Step #60] Loss: 0.1384 Accuracy: 5.4559% Time elapsed: 1.9463s (total 3904 images)\n",
"[Step #70] Loss: 0.1390 Accuracy: 5.5678% Time elapsed: 2.1811s (total 4544 images)\n",
"[Step #80] Loss: 0.1389 Accuracy: 5.5941% Time elapsed: 2.4153s (total 5184 images)\n",
"[Step #90] Loss: 0.1390 Accuracy: 5.4773% Time elapsed: 2.6516s (total 5824 images)\n",
"[Step #100] Loss: 0.1383 Accuracy: 5.7240% Time elapsed: 2.8907s (total 6464 images)\n",
"[Step #110] Loss: 0.1385 Accuracy: 5.6588% Time elapsed: 3.1313s (total 7104 images)\n",
"[Step #120] Loss: 0.1382 Accuracy: 5.6689% Time elapsed: 3.3701s (total 7744 images)\n",
"[Step #130] Loss: 0.1385 Accuracy: 5.5821% Time elapsed: 3.6085s (total 8384 images)\n",
"[Step #140] Loss: 0.1385 Accuracy: 5.6184% Time elapsed: 3.8478s (total 9024 images)\n",
"[Step #150] Loss: 0.1387 Accuracy: 5.6291% Time elapsed: 4.0834s (total 9664 images)\n",
"[Validation] Loss: 0.1394 Accuracy: 5.5800% Time elapsed: 4.2448s (total 10000 images)\n",
"[Size of Perturbation]\n",
"Average L0 distance (the number of changed parameters): 3053.2951\n",
"Average L2 distance: 3.428367002105713\n",
"Average MSE: 0.0038288730435073375\n",
"Average Linf distance (the maximum changed values): 0.0627451241016388\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xHxQeJ_yASh2"
},
"source": [
"* Black-box attack"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "N0VsQdX0ATzy",
"outputId": "8bf160c9-26ad-4ed5-e0ea-cc051bcc0dfc"
},
"source": [
"running_loss = 0.\n",
"running_corrects = 0\n",
"running_length = 0\n",
"\n",
"running_l0 = 0\n",
"running_l2 = 0\n",
"running_mse = 0\n",
"running_linf = 0\n",
"\n",
"start_time = time.time()\n",
"\n",
"for i, (inputs, labels) in enumerate(test_dataloader):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" adv_untargeted, perturbation = fgsm_attack(model, inputs, labels, eps) # adversarial attack\n",
"\n",
" outputs = black_box_model(adv_untargeted)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" running_loss += loss.item()\n",
" running_corrects += torch.sum(preds == labels.data)\n",
" running_length += labels.shape[0]\n",
"\n",
" l0, l2, mse, linf = get_distance(adv_untargeted, inputs)\n",
" running_l0 += l0.sum().item()\n",
" running_l2 += l2.sum().item()\n",
" running_mse += mse.sum().item()\n",
" running_linf += linf.sum().item()\n",
"\n",
" if i == 0:\n",
" print('The dimension of an image tensor:', inputs.shape[1:])\n",
" print('[Prediction Result Examples]')\n",
" images = torchvision.utils.make_grid(adv_untargeted[:4])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[:4]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[:4]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" images = torchvision.utils.make_grid(adv_untargeted[4:8])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[4:8]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[4:8]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
"\n",
" if i % 10 == 0:\n",
" cur_running_loss = running_loss / running_length\n",
" running_acc = running_corrects / running_length * 100.\n",
" print('[Step #{}] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(i, cur_running_loss, running_acc, time.time() - start_time, running_length))\n",
"\n",
"epoch_loss = running_loss / len(test_dataset)\n",
"epoch_acc = running_corrects / len(test_dataset) * 100.\n",
"print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, time.time() - start_time, len(test_dataset)))\n",
"\n",
"print('[Size of Perturbation]')\n",
"print('Average L0 distance (the number of changed parameters):', running_l0 / len(test_dataset))\n",
"print('Average L2 distance:', running_l2 / len(test_dataset))\n",
"print('Average MSE:', running_mse / len(test_dataset))\n",
"print('Average Linf distance (the maximum changed values):', running_linf / len(test_dataset))"
],
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"text": [
"The dimension of an image tensor: torch.Size([3, 32, 32])\n",
"[Prediction Result Examples]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: frog (6)\n",
"Image #1: frog (6)\n",
"Image #1: bird (2)\n",
"Image #1: bird (2)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: frog (6)\n",
"Image #1: frog (6)\n",
"Image #1: truck (9)\n",
"Image #1: deer (4)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: cat (3)\n",
"Image #1: frog (6)\n",
"Image #1: airplane (0)\n",
"Image #1: airplane (0)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: cat (3)\n",
"Image #1: frog (6)\n",
"Image #1: airplane (0)\n",
"Image #1: deer (4)\n",
"[Step #0] Loss: 0.0366 Accuracy: 53.1250% Time elapsed: 0.4168s (total 64 images)\n",
"[Step #10] Loss: 0.0412 Accuracy: 50.0000% Time elapsed: 0.7262s (total 704 images)\n",
"[Step #20] Loss: 0.0409 Accuracy: 50.5208% Time elapsed: 1.0220s (total 1344 images)\n",
"[Step #30] Loss: 0.0417 Accuracy: 50.1512% Time elapsed: 1.3188s (total 1984 images)\n",
"[Step #40] Loss: 0.0412 Accuracy: 50.4954% Time elapsed: 1.6161s (total 2624 images)\n",
"[Step #50] Loss: 0.0417 Accuracy: 50.4902% Time elapsed: 1.9194s (total 3264 images)\n",
"[Step #60] Loss: 0.0421 Accuracy: 50.1025% Time elapsed: 2.2170s (total 3904 images)\n",
"[Step #70] Loss: 0.0421 Accuracy: 50.0880% Time elapsed: 2.5224s (total 4544 images)\n",
"[Step #80] Loss: 0.0418 Accuracy: 50.3472% Time elapsed: 2.8215s (total 5184 images)\n",
"[Step #90] Loss: 0.0417 Accuracy: 50.4808% Time elapsed: 3.1199s (total 5824 images)\n",
"[Step #100] Loss: 0.0415 Accuracy: 50.6188% Time elapsed: 3.4203s (total 6464 images)\n",
"[Step #110] Loss: 0.0413 Accuracy: 50.8305% Time elapsed: 3.7217s (total 7104 images)\n",
"[Step #120] Loss: 0.0409 Accuracy: 51.1234% Time elapsed: 4.0170s (total 7744 images)\n",
"[Step #130] Loss: 0.0412 Accuracy: 51.0377% Time elapsed: 4.3143s (total 8384 images)\n",
"[Step #140] Loss: 0.0410 Accuracy: 51.2190% Time elapsed: 4.6209s (total 9024 images)\n",
"[Step #150] Loss: 0.0411 Accuracy: 51.2210% Time elapsed: 4.9221s (total 9664 images)\n",
"[Validation] Loss: 0.0412 Accuracy: 51.1500% Time elapsed: 5.1252s (total 10000 images)\n",
"[Size of Perturbation]\n",
"Average L0 distance (the number of changed parameters): 3053.2951\n",
"Average L2 distance: 3.4283670623779297\n",
"Average MSE: 0.003828873209282756\n",
"Average Linf distance (the maximum changed values): 0.0627451241016388\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q5M14tkAwDXa"
},
"source": [
"#### PGD Attack\n",
"\n",
"* White-box attack"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "_HlRw0RRu2kp",
"outputId": "ae8ce6b9-d267-44f7-fbf7-7d83f215149b"
},
"source": [
"running_loss = 0.\n",
"running_corrects = 0\n",
"running_length = 0\n",
"\n",
"running_l0 = 0\n",
"running_l2 = 0\n",
"running_mse = 0\n",
"running_linf = 0\n",
"\n",
"start_time = time.time()\n",
"\n",
"for i, (inputs, labels) in enumerate(test_dataloader):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" adv_untargeted, perturbation = pgd_attack(model, inputs, labels, False, eps, alpha, iters) # adversarial attack\n",
"\n",
" outputs = model(adv_untargeted)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" running_loss += loss.item()\n",
" running_corrects += torch.sum(preds == labels.data)\n",
" running_length += labels.shape[0]\n",
"\n",
" l0, l2, mse, linf = get_distance(adv_untargeted, inputs)\n",
" running_l0 += l0.sum().item()\n",
" running_l2 += l2.sum().item()\n",
" running_mse += mse.sum().item()\n",
" running_linf += linf.sum().item()\n",
"\n",
" if i == 0:\n",
" print('The dimension of an image tensor:', inputs.shape[1:])\n",
" print('[Prediction Result Examples]')\n",
" images = torchvision.utils.make_grid(adv_untargeted[:4])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[:4]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[:4]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" images = torchvision.utils.make_grid(adv_untargeted[4:8])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[4:8]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[4:8]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
"\n",
" if i % 10 == 0:\n",
" cur_running_loss = running_loss / running_length\n",
" running_acc = running_corrects / running_length * 100.\n",
" print('[Step #{}] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(i, cur_running_loss, running_acc, time.time() - start_time, running_length))\n",
"\n",
"epoch_loss = running_loss / len(test_dataset)\n",
"epoch_acc = running_corrects / len(test_dataset) * 100.\n",
"print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, time.time() - start_time, len(test_dataset)))\n",
"\n",
"print('[Size of Perturbation]')\n",
"print('Average L0 distance (the number of changed parameters):', running_l0 / len(test_dataset))\n",
"print('Average L2 distance:', running_l2 / len(test_dataset))\n",
"print('Average MSE:', running_mse / len(test_dataset))\n",
"print('Average Linf distance (the maximum changed values):', running_linf / len(test_dataset))"
],
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"text": [
"The dimension of an image tensor: torch.Size([3, 32, 32])\n",
"[Prediction Result Examples]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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nvcT2o/CCGwfgxVw8YfDr/VoAP3fO/esyhxvGumNvqIxnE5sB3AbgIQD/BqAXXqDeH4nIm51zlQLnvh7AywD8CN4DbBMAiBc25vvwggs/DuBrANLwgup+CsD5AN6qrvUJAO+Bt3zzWXghPl7tHxuGFwh4WUTkXHiBlNsA3AzPGWgdgFMAXAPgLwEMwdOE/FFJ2ovcX3Ktl/nnL97PPgD98N56XCkilznn7i05fju8Mmz3y+R+ANsAfMe314KPAyjCW346CqAZ3nLUJ+EF+tXlusg/wfM2/18Avguvbv4IwCUicnHppEFEzoYXhLoNXll+C17g5dcAuEVEXuuc++GxMumX3XXwAll/z89rG4CT4b0R+UjJsYPwHLcecs4NrqgUlifn/9aBh9cMEemC1z4z8Nr5WhARkd+Et6SYAPAggJudc4U1uv4i/wygFcA71vi6hnF8OOfsx35O6B8AgwCc//N3at+58B5MswCaSrZf7R9fBPCyCte8xt//KQDBku1BAP/u73t1yfYX+Nv2AWgr2R6FN0Fx8HQ0qJCHq0u2heE9mB2AN1fIV7+yh/R1S/a1+vc9BeAUte80eJqWe9X2n/ppv1dtf3VJGV9dKb1jlOEutX1rhWMDAL7oH3++2netv30KXrDd0nP+29/35yXba/x6SMMLmFt6rT54E6NRAJFj5bXk2mdUyG/HEm2wrC787Tetsk3XwPvHwAF46Rr2lXP9e/0rv1yn4U3Y/scaXX+opJ2U/hzQdVFlOq/1r/sOVc7Dy5y32JYG1yov9mM/iz+25Gc8m4jDi5/3BM65uwF8FUALvEFY813n3I9LN/jLTn8IYAzAH7uS/6z9v98Hb1B+S8lpb/d//7Vzbqbk+DSAD67iHq6C93D+nnPua3qnc26pr5gq8Vvw7vvDzrlH1XUeBvA5AGeJyCmAt0QG4Ap4E7pPq+O/C+AXq0h7SZxz+ytsK8J7QwV4b54q8Unn3CF1zvvhTYp/u+S4KwFsBfAp5xzl2Tk3AuBvAfQAWOnXbWUxF1150N+j8N5crdUXcx+HN+n9oXPuJ2t0TcCbUH0YwP8G8DZ4by7f7pxbK/3RF+CVQQ+AegCnw3tbPAjvTXHVgnAR6Yb3BvhHzrl/r/Z6hrFW2JKf8WziXlc5Rt5N8B4eZ8F7C1LKnRWO3wFvaWcvgD8TkUpppeA9QBc52/9dadJxC4CVLndc4P9ei+W1C/3fZ4jINRX27/B/nwzgUXjlAwC3uMrLMzcBuLTaTIlIO7yJ0Cvg6Xbq1SEblji1rGydcwdE5AiAQRFpcc7F8OR9b1rivrf7v08GcKxlv6/CWxq9Q0S+Du8rvF9VmtQ653IAdh/jWitGRN4Db9K+G0svfx4XztMa/auIROEtkf8egC+JyEXOud9bg+t/RG16GMDvicgCvHu6BpX/sVkNn4P37HpnldcxjDXFJlTGs4nxJbYv+r+pJA6v5BtnMZjxdnj/zS9FQ8nfi9cuy4NzLi8i+o3GUrT4v4+u8PhjsXgfv7PMcYv3seQ9+FTtR0hEWgDcBe9hfieALwGYgbfs1ALgvQAiS5x+rHxtgpf/GJ687zcsk52GY+10zn1LRF4JbyLw2wDe5d/DPQA+6Jz72TLXXzUi8m54b+oeBXB56dvOtcR/c/oYgPeKSATAu0TkeufcN9cjPXj6xPfB08AdNyLyW/De4r7Nf9toGM8YbMnPeDbRvcT2Rc/UlVwUVPLvs3jct51zcoyfzRXOKcuDiNTAE0SvhJj/e6m3NKthMU9nLHMfX1THL1eO1fBOeJOpjzjnznfO/b5z7s+c95l9pY8GSllp/S7+fvUy963fppThnLvOOfcieHq0y+EJ408F8IPFpdK1QkT+CJ5m72EAlznvS7+ngsW3obvWMY1J/7d+G7laFt8Ef1F92brYjzeUbGtZ6iKGsR7YGyrj2cTZItJYYdlvl//7vhVeZze8ic0FIhLyl3OW4154g/2l8AS4pVwMT8y+Em73f78cT342fywK8ITsS13rdQAugfel1XIsls/FIhKssOy3awXXWI5t/u9KfpWWW068FN5Xj08gIlsADMATgy9ORhfL8BJ4X+dVjXMuAc8h5g0iMgtPq/dyeG+SqkZEPgBPN3U/gCsqaLTWk8XJ+7p9TYgnl7J131gtt2HpN4vvAJAE8B++nakyLcNYFfaGyng20QzgL0o3+C4I3gLvrcW3V3IR51we3puCXgD/LCK1+hgR6VVvKK71f/9vEWkrOS4K4GOruIfvw/tS6lUi8hsV0u1Xm6YBdFbKIzyBcAzAh0Xk+RWuFZCS2HW+Nuhn8N4gvVsd+2qsgX4K3r0BanImImdhefH+e0XkCY/Y/scDfwdvHPtCyXHfBbAfwB+IyCsqXUhELhSRumMlJl64lkr/dC6+KUuWHBvy/X1tXeYeKqXz5/AmU/fAW+ZbdjIlT4Z4GVxhGmX172/fCuBDvnmd2rfoJ+3aFaZxsoiUvYHy87j4kcNXKuxf8b04577unHtnpR//kNmSbWUfExjGemJvqIxnEzcDeKeInA/gV3jSD1UAwLucc3OruNZfwgtR8XvwQnPcAE/X1AVPW3URvC+lHgUA59yvRORT8L4OfFhEvokn/VDNYoXhZZxzWRF5Azz3BV8TkXfBe+MSxZNfkZX225/D8930YxG5Gd5/5Q84z1nktIi8Hn6IGxH5OYBH4C1zDsATb7f7117kD+C9BfiEeA5RH4D3Vum18CZ7V63kPo7Bl+AJ0j8hIpfBE/5vB/BKeL6i3nSMc38F4H5fIB6H9zXgGfAmIn+7eJBzLicivwbP/9R1InIrvDc/SXj3fR48MXwvSiZFFfhneEtIv4I3EcwCOAeez6xDAP6z5NgN8DRJh+B90bYiRORt8N52FQD8EsB7KnwEMeScu1ZtW/xneKVvlX4qIhPw3kIegdeGtsLzwVYD74tIrQlbbRpvAvA+vx0eAjDvp3ElvDb2QwB/X+G81aZjGM9Mnk6fDfZjP2vxgyd9AF0Lb9LxXXiTmCS8h3CZHx9U8AFV4RiB95XVz+EJp7PwJlW3wPuvfqDC8e+G92DNABgB8C/w3pwNYQV+qEr2bQTwGXguDLLw3kTdAeBD6rh6eCE3huE9kByAayuUz6fhTV7S8BxV7gbwZQCvqZD2NgDfhPd2KwFvgnXlSspMXecaVPZDdQq8pbgJ//r3wNNWPVGP6vhr/e1b8OTXb2m/Lj6BEv9i6rwueG9+HvbbwoJfBt8E8JsAao6VVwBvhLd8tNc/d86/1l8D6FyiDQ5VyMeSfqhK0j3Wz03qnFZ4E7BbVtFH3gPgB/AmOkm/fR4G8A0s4ecKnl7MAXjxCtO41C+vxSXzHDzt1M/gufCQCues+l6Okb75obKfp/VHnKukyTWME4cSL9VfdM5d/bRmxjAUvmD6F865XWt0vVfB+6fhSreMt/cq07kXQM45d/46pvGU3EtJetfCc6Gy2Tk3tN7pGc8t1lRDJSIREfkbERkRL/7YHSJyxVqmYRiGcQJyacnXZ9V+LXkpgPvXeTLVDG85dTX6v+Nh3e8FAERkwZ/Yvm090zGe26y1hupaeLHRPgHvNfnVAH7oxwu7ZY3TMgzDOBHQ7hkWqrmYc+591Zy/wjTiWPmXqdWks+734vNR8NewsaUONIzjZc2W/PyvSO4A8H7n3N/726LwNAcTzrkXrElChqGwJT/DMAzj6WYt31C9Hp648LOLG5xzaRH5dwAfFZEB59yRNUzPMAAAvhaiYnwYwzAMw3gqWMsJ1VkA9rjyT9MXY6WdCe9z3TJKvNwahmEYhmE807nXOXdO6Ya1FKX3orKvncVtfWuYlmEYhmEYxtPFfr1hLd9Q1aKyq/90yf4n8J0XLhe81DAMwzAM4xnPWk6oUqgcJT5asv8JnHPfgOdUzpb8DMMwDMM4oVnLJb9ReMt+msVtI2uYlmEYhmEYxjOGtZxQ3Q9gh4g0qe3nl+w3DMMwDMN41rGWE6pvwnME97uLG0QkAuDtAO4wlwmGYRiGYTxbWTMNlXPuDhH5BoCPiUgXgH3w3PwPAnhHlddWW4bJmkh2kZ2a4rnbpo2DZKfH2QFwVF1+vIc/Vrz5W7vJ/uaN3ynLY0P9PNkbM6TBRzIfJftw7jDZPbWbyO47vZHswFiC7HiMJGkYznF6kcwY2cXOU/j6kSLZ4d5usltGuYwTuVmya/PNZOfb2fnzyEHOb3cTy+tm0tz06oN1fP22DrL/+AN/hFWj/10otpDZpnbPKOfJzWp/vCwBvl5EnV/pC43V0LLchtgxzeUvqE9o4jrAXLL8GlFlp8sPOWYWVB5iKg9l96wov0eVZ1TIcwm3/ej7ZI/Eua/39m4je/Yo94PuHh474ipDE+kC2akM94O9U3eRfVrr2WQXt+hWB5y75TKyg108YKU4i9i4vZ/sFkyTPYZ2srUDt+5Jtsc62U7NT5A9mecTAvkGssM5biRTWc7PxuhmTm+Yb6hvA48VCw9wHYzXcx3Gj06RnZ3k9N7ynvdiOWbVMyfGwzV6NrJ96L59ZI90cJ6DYyGyp2YOkD3Qzu2u4RRuR+1ZroTRW68neyGdI3u6lUe3rbUc7eiAGhvDSc5PNM49MdvOdZjPc/5G1PUSR/eQXVjgOsrMbocmVxgne9vgANmTEzwC16nBpH/rINkPTN1J9mkq4lMhxPZ4A4/Yv3vpi8vyuBxrHXrmtwD8JYC3wosi/iCAVzrnbl7jdAzDMAzDMJ4xrOmEyjmXBvB+/8cwDMMwDOM5wVpqqAzDMAzDMJ6TrPWS3/owxmurRbV2mqljjwybNvIae1EJPcbUevHGHtYPtQ4rEUHbUTI3B8PQ7J/kuWkyxxF4CkHWdrRFWVM0leR1/v44V02ihjVT0/EZsmtGWcM1GeT14b4k39OCcPq9bVxmsRDnx8XryQ4J38+RI6yxanC8/n0oo+pkLzuZfbSXdXCnR1gTsDLUB6ZF9f9CDQteZvLq9FY201o0VVT2MpqpgMpPfT23ifmEVgxxAjGoKE4xLsMOsI7hpB3cjmemuN90d7OO7uHYo3z9Ms2U/mAXQK3KU3oZYZbqKlozpdG7tUKqXAd2bM2U5rtf/zbZ/aeyZikzwmNFvI61NIU4a2FSTaxdDNfyDY/t4zrdXLeF0+NujZ4KZd7awPcoOe4rjVFOs04NXxORLNktKokoWBOVUffcM+3Ufh47cllud8kca6AyAe6HgxHWh+YSXOvhZh67Zif4/Am5l+zmFI9VAI+F8YGyVrQsWdWZkxtVEJDD7CGoqU9p6/ZxxXZGud9Mqr7usqz7bd3N7Wzf6BDZB4b4mZRr547RzJJWTIGfF3UJbiTzRdbwFlpYNObAGq5AiK+XHeXjZ+c5A+0Bzl9yijVWADCT4noeqWUt3NhuPudADQ/QvXdyu8zn+Xp3FLgOL2hiQei+3kpen1aHvaEyDMMwDMOoEptQGYZhGIZhVIlNqAzDMAzDMKrkhNBQjTWzT43EFK/510WVnqeDPavU7X2A7NlZXs8+nOI19+itt5OdT7BWRaa1+AYIN/Aa+m7li6W3idOomeDjI/W8pj4/y1WTV35RAgnWbqRaWccQiHIewwXWJOXyvP5c3K/8XLVy+jVJLuNxdnOF0VoWHQSSrNvIpnh/sJO1Ko6LB4cay70+LY/S9yjBTauqNlZ9AVD+yEIsY0CtilQZ08kpwVARWjOljz+2E6YmtTvbxO0wr8rsZS/7DU4/yf8v1at2fuHpF5P90zseIvvQIfaZBKBCoXEmy7xCZdUGVUYt6oyYul6ZQiq2nDOtY9Peyz6PztzJmiaX4xRrMqy7SBbYf059mMs4kWY9T2hA+TOe5vudmObj24usVQSAGaWVC9YOcR6V7iqV4vGvtkwzxT6T4sN8D81KSqK6PuaHOT9tEdbTtHVz+nOHuOMFOjm9aJzbRE2WyzzXx2XSHeGx7uBB1goWplmfmV+ooAVchqQSt0mKn0EPKY1Q8zQPDqFG1jgN7ecyqVc6rwce+iXZmUFuZ4d2sy/EZC0LPptCrIEKBLjSEhnOX1fdyWT3qY6dzSnNUz0/bw7u5+ObargOZ/M8mM41swYr2Fc+9UgomVpDmss828rP1I4o51HGWbt3OMGDT2VJaG8AACAASURBVHKEtdOPdwySnRktG9BXjb2hMgzDMAzDqBKbUBmGYRiGYVSJTagMwzAMwzCq5ITQUAUTrIHKKIdBhXnlIGjvEJkxYTHMHbfeQfZCK8e2mruP/fO0KT8qtTXlvm8S+1ij5Np5/bfR8Rp2rF1phKY5DwdnlN+qAfaZEe3jMugJ8Jr1TJx1BZmCEhANsg5iX4E1A12jnH/PCf6TzDVwmQcW2J9Pcz9f/9AjXKY1cxvI7m7j8ksOr86/EIAKgeJ4Db1M/tPCa/KIsS5CyuLOcXdpVefPLudkaRlqlIarqARJabXEf+52jgPXdyr7J6pzyndMPZdp9wiXT9s2Fs/sfkxHeQO+e/MjvCHB7TisYv2Fy+INZo9p15bFR9Tn8wVXq6gaHOgjez6h9JY1bPc2n052VPlMms9zULe0ikc2oIpwaIz99WzuYu1Lg9tRlufuKBdqbor1KvEsN5Ttm/j4mBIHToDFfM397PcJ4L5Yp/z1HFlgDdZRFWNtR4Hb3Uyax4qBaR5r0kUeO1KdfHzDGI9dyQjfT2fzINkLtUp/Or96Pebjd91N9sZO1m1193G9Pf4Y+0yKZvkejkyzQKixnVt239knkb3vALeTyUn2G9V9Evf1hlpud4kgawOjDVynwQ4V+y/DbSoS5zpMH35cXY/bTDjAOrcXcWhCDNdwm5Mg3y8A7LyAtXgLk3xO627uu6EQt6OJSW6nO7bzM2a8i/f3BrhOc7XqeXAc2BsqwzAMwzCMKrEJlWEYhmEYRpXYhMowDMMwDKNKTggNVc0Y++zIzfH68+jM/WTPPLaXj1frwfOb2K45yr5grnzd68iubeD14e9+/ydleaxzt5GdmGLFzsEW1gk0ZlUQrzbWQZy6ldfIk3XKv82Cih2lfM+ElKaqfSP7NSmq2IC5AK+pL8RYAxVYULoEsJ+VeseagUyM89MfYo1YMcR6pVrHOonWTiXGWQlKw1QmqSoT2IT0BiJYtoXzOKuuV6cSzMW4THJtLIIKKOmf9m62kCzz6sTnt7OObvcQ69QCs9xPzruIdRUDPRxvcevzWPjQoXSAALAw/VOyf/4g+88piz+oCHEYN+SUn6pUrbKX0aXF9L+EZfEWGRfmdueEtSNNBW6387OsTQkEt5Kd7+DrK/c/GNnLN3jXKDtw26n8atU0l9/vHmEtSSzD42G70rPMdJ9HdjjK96AkVDg8xX29M8h9r0UJ4dws+/vZd8P3yR646K1kB1VswIkx1qrUhthvVCDFPS/vuJLDSs+ZHmV9UriRx7bmHGtpVsKdC1xPmXHWYZ0S4jLbrHxvPfjLQ3zBLDeMcILLeGATj/f37VbPsBYuw5AaHwuNPFaEM5zfmgUuk2Qd9/26HB8/luY6Twhf3/VwR22d4TrS+RuYU36qziwfWwZU/MQfHVa6s05+Dk89zp092MvjeVMj+x+rSfMIG+rkzls7oBWbq8feUBmGYRiGYVSJTagMwzAMwzCqxCZUhmEYhmEYVXJCaKgO7eb16aHCPWTvz7H+55KLTiN7Q+/5ZCfSLOTo7+E1/QV1va46Xn/eM8/7AeCWLw3xNXtVoDXlKyvVwOu7G7p5fThczz456pUMIlqn4nsF+fzD07yenDzCmqeNHawj26L0NDWn8Fx79mH23TUb4+vP5lkz1teofDQdUGv0UU6/u4bLZ2N9WRC4tSemHD8pfY+W75RrsjjPyWViCerAdLVRLsOBrufz2VeoNhFmzdSWFLeh0DRrSw4e5jhyAaUhuPgUbtfFtlPIfuG57GMJAJpSLyb7wMQeTnNMlQFLJ5DjZouWDJdRLLM6P1MtRVH7VZ0q8nVchn0t3A5RyxlOTnA/nI9yP9rQxNrEmXnWSj56F2th6sOsRfnl4zeTfUTFsQOA+nt5mG4aZHtLE/fdgvLDd+se1pimlS+tqQUWVeU7+J4Gm7lM63JcBhGlafq3//gU2aHNnL9cin02Iao0USn2h1azwINfWIkPL3/xpWTXJrmdT7eVqyGXo26Gy+iBGdbXZPpYi9eq4i3OFw+SHTzCZXzO1awz23PjrWQX46zhmo9zT0htVXrKWR4rMvXstyqd5TaTOcx12KDiMcbm+P5zarzekGM/XMUurpOZAqdfSPNY0xcqC2yKkQkWUaVmWYcWEtZljQRGyO4c47FgUrWz/Ci3C3EqpmW83O/earE3VIZhGIZhGFViEyrDMAzDMIwqsQmVYRiGYRhGlZwQGqr79/yI7Ik2Xr8d6OP4V8kjrNeJdfMafXsL6ySUrAJpFUEstofXl8PzvH7uXYP9LKWUX6j2Xta79IZ5LjtwMvu3CRR4fXgiy3nqdJyH0ASv2efjvIY92MwCoVMGWGc2otLbvvFkstsHLiD7yPW3k51U/n/iIwfITrWy35K6MOvQEgkuv2TdccRVajq246lyDZRS5CwntVhYXeS4DRt4/+btF5L9svNeS/YZW1k70tTIuohUgNvxviTr1kJ5bqfRk84g++7rv0m2y7P+aVuMdX/zm3ZCk5pk7UWxSfn4Uf5zyshyP4gpjVVTlus9Bm7nLaoSY3lVB+Vdk4gridVonLUo+TTr2upbuWG7ec7/7DDrNNqzrKG68DT2/fXYMOe376pzyd7ec3FZnjuV76xYkOt9/Cb24/TxA3eRvVf5JDq9mfO4kODrHb2HNUu3B1nb8q7TOQZa5w5uA7kc32PbDOtlUgUeyyaS3K5F1Wltns/fEOT0QkquWohxRx6IlEXxXJbhPRy7rjPC4/XDd3CsvUwvj289Ge67Jw+y36XiNPsVPDrL15stqAF1gfWQex/iMrjkIvZFllXtMtXCPpfm5pRfqjxrthJHeCzpVLEHZ5MPkR1SurbdDfxQzal4isn95XVy+PbHyA4EeCzYt5fzNDPOdZRW2r/69BAfrxSZbpbrNNCsJgLHgb2hMgzDMAzDqBKbUBmGYRiGYVSJTagMwzAMwzCq5ITQUM128Frq6SefzgcUWEPQLLx+nQ+xXqenl7UqGOX13YSKGdTayEKP7BCvNwNAbz2vIY8usDYjqIJ+NUfUGvMMa5hmjvAae1rFdQu1cZ5dB6/R7xTWemihwfDD95Jd2zxI9p4sa7Ja63j9eUuQdWxj9bzGHw9wfhvmeP+GPPsESSqHRa7m2HH2KqL+PSjzWXRsyVPZCVoxVcxzHZfpeWLcnV73xleRvev1Lyd7s1O+XBqVriDPWpD6TRyjrfAY69TqZ7hd5mq5DO9JcZ089AjH/qsbZz8vBx8r1zlcdyf7TZpXOi5daAFVpk3KV1dMaapEiaB0KyguE9tvOTaqmJORXq6zlixrH2fn+IaaA6zn6Wpl/zzj+1kH0r6FNVLnNXE/b9zG8RMni6rfAugfZC1cdoY1Tq0td5I9l+X9yceVsOxsHnu2b2G95L59rMEKh7iMYkq/ubGTa+mic99Mdl+GNVj5AdZgBWO8f7pDxbAs8tghQzzW5RyPlYiq8V1YT7oSMknWGOXj3FfmdnDsvdgYawc7QqxpCu9gH29xpWGaTPE91uS4TAKONUyJhNKozrKmKh1mTVd6nK/XkOLnR1zp6CIbuF3PFPn5EVaC05qiirc4zfcXU366Jsf5+QIAhxe4XW3o7ye7fniI7K5O7iutrey7MDbF/s9qu1WZhlXMyFrlJO84WPYNlYg0iMhHROTHIjIjIk5Erl7i2JP94xb8Y78sIp2VjjUMwzAMw3i2sJIlvw4AfwHgZAAPLHWQiPQDuBnANgAfAvD3AK4E8DMRFdLdMAzDMAzjWcRKlvxGAfQ658ZE5FwAdy1x3IcA1AM4xzl3GABE5E4APwNwNYDPVp9dwzAMwzCMZx7LvqFyzmWcc+WioXJeB+AHi5Mp/9zrAewB8Mbjz6JhGIZhGMYzmzURpYvIBgBdAO6usPtOAK+o5vqpIosMp5TTslP6WCB41lWc3AzYIZhmopn3N86xMDXazALDXFP5PHTyEIsAAxkWvOXAwsmFIDvavO8WFhmOp1hoX19kcezOBQ5c29rCwtBAAwv508Ii+f0hFufGb+fgnFDO/Lo7+sieVaL35nolQiywsN81DPL1m1mU3lHH5XfyKVzmK2IZwXLZXu3pUwmiY7G82q8C/6oLnnkKO+689DJuh90RFn3HQuzMtLue6yg3x2WSCbFYV0Is7m3s4+vXJXaTnc2wV8vAHIswHwpy+sUkC2UBYOe5V5B9/Xc/xweortGkRphsXjts5RPiam9DA9fR3IKqgxblAHEZzXoxPUF2ZpLz01DPOehs5/35GW7XC3UczDhaz+LhaCeXcbplM9nxHDu1bB5gIS0AdHcOch6zfM17I/yRTu0UC99fMMDt6DXn7SI71qMEzEe5HRw6zEL7GSUODkzxxxLbX8Ri4vwUi+Rb8lxGOSUI6U2y8D/SzIJrN8j9IMZFiNGM+uAluXonwQMbOM2jB3k8bphRgcVrlJPdXhb+n3HO+WR/56tfJ3shyX15dFZ9cJJkZ6ypAPeDIzPcrhPKSe/pZ3C7motznfcqZ9nJWn5ezc7xWIW0coqZ444eqWe7b4afsXML5UHMt6m+MRHnZ1BxjK95tIbH6/HD/PHZji38IViPcj6azfI9NAa4XR4Pa+U2YXHGUx4q3dvWJiLV59YwDMMwDOMZyFq5TVic7mYq7EuXHPPEfhF5A4A3rFH6hmEYhmEYTxtrNaFafP9Z6S1UVB0DAHDOfQPANwBARMrf/xmGYRiGYZwgrNWEanGpr7fCvl4AM865Sm+vVkRxjNdzG9vU+vQ2Do6MCV5PbuxgV1hqSR/5gnJK1s/6oJpZvt5Zp59dlsfv/PJGsjs3svM5jPKae2eQ7TvyPKdsWODiSmdZ25Gq4/XgunbeX5NTa9wJnut2hTi9e8f5HjcoZ6aN53BTyUywRiypHM+Fm5Xzv27WVWxr4/Mztezg8IWX6jJ+P5anRVmi9vM9lwVH1pKqerUhcWyBztkXcsDplm7WPQQLXKYRsNYjl+PuE6thnV32Me4HdcIr7K6D6+iiHtZNjL6WA++mcTnZDz3AWpm2AGuyAOC1v/4msvffwwFKDw6z48+Y8lPZEuN2kYRy4qhYWFhGFBVbXcBqp/pdpIntUJzvOR9hbWJdlNtQJMfawiNRdlgYSnMdNs+xk8lck9JkTZerJm4cYv1Or+N23ahG8e1n8Njz65eyg9ltg+eQ/Z7PfZzsc88/k+zWTm7Ho0ceJrvYwZU89eAtZCeTynlrI5dBapI1sQnlQLe5lss81MX3X1vP9+viXKY1ydU/evaO8TlbHbeD5DjrvLb1s8aoMMYapdki9/1DC1xpOeUIenMf64keuOd+snvbWOM1Mc4apf5TuV+ElZPgPvUOI1HD+qPZedadNSv9aLSOx/vWVtXRp5T7yQbu9w3t/DwAgLtu53ZQ71grON/NY8XEw5yHnf3cLsIqsHnTLPfFuRTXUVGeAseeK8E5dxTAJIBzK+x+PoD7K2w3DMMwDMN4VrCWsfz+G8ArReSJf4tF5HIAO+Av7RmGYRiGYTwbWdGSn4i8G96CyOL77at8z+gA8CnnXBzAR+GJzG8UkU8CaIC3TvMQgC+saa4NwzAMwzCeQaxUQ/UnAEodVfya/wMAXwEQd84dEZFLAfwjgI8DyAK4DsD7qtFPAcCb/4iDbc7Hef33xmEO9Nukgnd2HWE/LV07eS12ZoHXTvtSnN04S7ZQt7E8ks5pZ7DuIK6CG8818nrvXcPDZPcXef23tZ41RUMN7Jdpto31N2fUs84h3Mv3cMfjvJ7ckOaqv3QXB2CdHGJN1eRuvl6hgevAdfOafWOBNV3zBb6/hhb2u3Ka8pUzX3s8TSZ2DAto0Xod7YeqzI/VsfU47YPsu6WrRfnLAQfrbMuxLmGmn18QL9SxDqF/knUND46yXqm3j9vA+Bi3S+llXccVb34t2fkEt5lTuln7snkHB80FgIUM3+OFr95F9sF/YQ2VLsLyEuW+Vwet22InQ0m1t0z3tozkKhhlzVNEBbEtbmBNVW6B7Qnlb6czwvkLB7jd1s6oIOa1XEd5FQx5f7w8IHVciT4fUIHEz0xzHjqV36rr7mBdVv/RH5M9tJe1K+cOsB/nui72eTdQx+3uUEL5fa5jv4C1NewTSca5TKWT+35kmstwtsj6pdQRvt7zeliv49RYGq9f/UJMupZb2ngd62/6lP+wzUpHdjjF59/009vIHjt8O9mpg+w7rMOxzqy7mRv26BA/4846/SSyB5QG1ikNmAR5bIpPq6DhHay56qhhfWc2o3w1HuaxtWsD53/0MLeJm+/7FTSJeR4L+nc+j+xcnMvgBWeyDqs/wlq6tkZupzMNSjOVUn4G09rv4OpZ0YTKOTe4wuMeAfDSajJkGIZhGIZxorGWGirDMAzDMIznJDahMgzDMAzDqJK18kO1rjz2IK83j8zz+i5UnLpW5WtmJMEaq35hHUj34CDZ87V8/vAjrGHo6yl3t9VVz2vILsK6goLy+xTL8vGNzUfJDhU4je4I62HCrXz+y97NTudzCS6To4e+RvZjBdZNNCV5/XnDNtYE5CbZ78q88Jp7fpjjvg2q+Ir1yk/VzBivV7f1cp2mH2edxEpoUYKaWIvSVMWUAkfbCCk7h2PRkuY1/xaliYoUWHcx0836HdSyXqg+z+0sk1DivQiX6QMHHiR7cz1rSSIqdlVniH3bLKjAedErzuINM8qXDICBDi6jKzbyNb/Wx3VQw9IQ5KHrlfUyZRopTZlmahnRlCJSwz6KxlPcbtvjXGfJNk6wLcN13lVk/zyjLNNAXGkHWxy386nkfWTXTZXH8pua577jHI8l/RtZ4xQNcxr/8smPkh2bvYrztJHr9Fe3fofsV1z4u2SPhrkdHglwnW4PsA4sv8BlkAuzdq8hyXqkOFScvFnuJ+NNXMguxenVtfD56WC5P7XlaI5yGfYrv0yFKI8NLsya0Owh9h32y8c/S3YowWXuAnz9++e4J/ROcly6Ld3cBh68j2P/JRPcr87cyI/60Qint6B0ag1H+P4KIR7/GzZym9yfOkx2Ps7nj05yGyii3OfT2WdtIbsmyvc8NsdawKYo983DUdZtxef5/Nw830Ogidtt1OnBZfXYGyrDMAzDMIwqsQmVYRiGYRhGldiEyjAMwzAMo0pOCA1VcJjFHi+5fBfZiSZeGz0pzOu7weBOso+Osm6ia5bXq3959x1kPzS+h+zNG9lHFAA87+LzyL7hMPtx6omwX5DcDl6/bZzj9dtohrUhNSr23Vt+4wqy+1t4DXx8mO9p23bW78w+wn6wAlkVB0nFHJttYG1JMcnpFZTPo6hTflrOYJ9NTUrn0K18MCVqWJOwEmLKCVHLsj6QWE/TAm5HMaj4VFH+/2OmlfUzIRXnbUr5M+vUvl3GuIyc8lM1BPY1czTPWpGaWdYh3H2A2+l2Fcpwupav3xtiXzQNQ9wGQ31KZAXATXAZpFXIys4G1vZNQomowLqwFhVfsahqSSuu3OokU2Wktd4yxVrFDY1cpmkV7n1YxRdDkfU8rTNcxjnh8jg0xTHX0rPcBrKt5dqSng4uheQk+87KKp9Hmxu4Xf/TX36V7Pv23Ep25EaOP7hz5wvJfuWLX0H2V3/+b2QvzLPWLtDDZVCjfN65Vu5nceXzKRRTvrpauB0W4rVqP6cXiHM/awms/r1BQ5KvEVDxYNtDPP7NK21eVxvrtmb3sV/C4QSXSTHP7SQhSsfbxP2kpshjSyDBZVRs5Pi2iThff76Hx/NUH+st5+eVP7VRHlsww/dbN6Xyk+XnTUeYx5r2/nJ95kITd7bhB1mHFg5w559WMSUb67hf5DexVi+R4GdyWM0bUlkez48He0NlGIZhGIZRJTahMgzDMAzDqBKbUBmGYRiGYVTJCaGhOu8lu8iOpXj9dXaK15fv3cNrrRL+LtnZFOsWvvI4r9XKJK/hD9XwenZ7Da/FAsB5Oziu0MXvuIzs6fseInvLEV5jj6u4Q7GU8iF01mlk1/ZyHn9620/IvudeXrOfPMJ+sYrKB1JzH69ft6hYgo0tSh8U56ZTy0vk6G7hNfT6MJ9/Ug/r2nJR5Ztm0yBWjfr3IFasfNgiLS1KM1UW/E9fkA/obOIYkbUFLtOC8gWDHJdBrp11CG6WdW3ze1gzdfSu68ieGGWNVW89a6Bazub8TcWGyG7uZL8vgSZug4V51vsAwFSO28mmHr6nhgK3C1biAQDfky7yCFgPE1U6Mu6JKwjHqMg7zt9ClP2xZZSWZWye+3owxfuLKh6i9HB+a+a4jh88+nOym5S/ts6gvkNgwxbWw6Cdyzw+yRqrhUmO+XjmOaxb+8XnWUP1yN5HyD7npLdynifuJPv07TwWjU1yGaYOcjtONLIWMaz0l2PTXKYNk4fITge5HeZquMxckdtUoYl1aPE864VWQmeE76k9zM+ccB3ruBJKI+T6WM/TcQaPh9EDrNFqauMykru4DMenuc4zO1mz2h1R9zjKfqnGm1UZFdjnXFLpi5xwu27ZyvfTNMVtbvCcc8l+8J4HyE5NsI+99m72EQUA7Sok5IYebhf3J3js6Y7wAH8wyeN5S4jvub6ZfWOFhvkZOVbkMj0e7A2VYRiGYRhGldiEyjAMwzAMo0psQmUYhmEYhlElJ4SG6q67hsi+6Zc3kz2uYqB1xFkL0lnPuoZQF6/fDif4+PgYr58/7ySO/ef6dcw3YO+QWiNWsfuSLSeRnUuwziFUqzREKV6jbw/w+vC+4b1s38nikRtuuZ/sLuF7Hujh6we7lU5MxSB7OK3i4g2zBqBhQWm2Wnj9u3uGxS6TBVVHvex3qj5Vrr5ZjpYmTqOoBDWsnEO5gEfpcYIxLvOC2t/Vymv4uVEuw8IG1lC5BbWGz0WESDPrh5JH7yH7kYeHyN5/hH3DnBbiMjzn8kvIDvSyzmP4AOscZFT5b2tVsQQBnHk2awVv+Dm32+GUEkJolGuvBuX6ZSHCfRWZsuB9ZBVX6ZcqVGDHWYUsl8l4I/fbbIzrbGQ3xxW94Az22dQQ4iH1X7/NPqAmpjn+4uWdrye7LlfWSrHnhlvIrgHrcZo3ckNqn+S+eehuHs+wg8ei1ORdZA/P7CM7m2B9TriJ2+llZ+4i+8Y7uV3Npdnv33xyK9n5IJf5RLhN7ecyaXbscymv/L0VurkOIrPlvr2WIzPF7SKY4/Et0M96yWxaxSYN8D2FO08he3s339ORe1jzNDrGZTbgeKxpyXPfLCr/ZbE27qdtSfb1VTfNPpzqWgfJTjrWrXW2sY6vqMp8zwG+Xq6X22hrq/JVFijXIbe08FiyN85l1KW6RqaT29ELGzmNuRyX2ZEabtfzBa4jaVCO+44De0NlGIZhGIZRJTahMgzDMAzDqBKbUBmGYRiGYVTJCaGh+swPOHZUfcN2sk/dxBqnN53FPo4ibQNk1wZZP3Sw8SjZD9yl/FPsu5uvF+X1aQCYHWYxR/sOXkPuHuH121HH670tbpTsfIT91xyYZ21J4B5eUB4Z4fP7CzxXPr2XdRaul/2ItASUxmmUm0a9io81XKNiPQVY9+ASrD05nGKN2dAY51eKrEnoauc6XQkxpZlqAtdzFLxGHgPrCrSmqoDcMfc3NPOaf6CJdReZHOsa6h1rsiZH2JdYfy0nUJtjgdHhR3eTPTrH2phzz2KtSwKs69jSxn6pRg+z1qWpkdtMUw2fDwAHlS+Y+/c8xueMcBmXuQJTmqlZvT+jRFaq0BvU3tX+R5gqct9OO76fxiT3y59f9598/PAQ2R1R1lMeVv3k1h//kOxQA7fJ+V2s43C13O8BAP2sl9k/xVq9jlqu9/p61hglHmDdV4/jdnfVBa8je7BFxXlT2r9wC9/D8Aj37bZTuZ394j+4ndVt4OsPdPF4mkpzu3ZJrvVmdkOFuiC3gvo098sFYZ9OK6FxI8cePRrndrMzwpnYWOQymo/w+LmjcZDsSJ7bXYFde2H7EI+n84dZmxhUjv821fH4mYjwM6/ouOMllC/G6SmOudnXx/nNzR4me0HFDmwMcR2dvUO12XHWTOV0RwaQnuZ2HXB8z5E6jrvZXc9+o2pruS9mA1xn3Qn2vRVo42eqzCzjuHAF2BsqwzAMwzCMKrEJlWEYhmEYRpXYhMowDMMwDKNKTggNVWo3r61e/mtnk33lK9gXzLZG5bumQ/u84DX77BTrPlou5Hnmvnr2IYIo6x4AYCrBfpP2388ao/Ze9gOVmGI9TCrCGqfQJK/npiO8Rp4c5zXwjSoO0cZNfL1kHdvZBb7HIRUPLK5ucXJCxRhLsLblSCPrKrDATSvQzz6SBsJ8/P486yY6RsvUNSuA6z0Q4Dxm1RL5sT0cAWhS3SPAFyiAy3SulXURzQ1cJyPj3EbOauT9wUbWbfSfz+320qkryHbzrIXp3cy6hb5W1vE1OdYMFDpYyDA6xiXQdzL7KwKApihrhEJHWSc2DRW/UNGsJEJl4RLnlchKsaA3NCs7rg9gIgHWhWWUP7Ss8t8TTykdWQuPFZ/52hfInosp32M5pcNLcT8aiz1K9klKmwMAE0dZS5IP81hw5B7uuwMnczuQw5yGTHChb9mmCjHOddBYx31za4iPn9/IlVo/xMdPLbCPot3XsabqzCCX0eadHEd0aE6Nzws8lvYPXEh2Pqj8w+VZu7gSYg3cl7e3D5I9McKao0g/63v66lRsvTyXeUq5MgzMcbsopnh0itez5jQyxPeYaWBNVWue63D3PI+3oTTri7aextrFzAH2k7jnKD9vmsIbya6r4zrM7WNdspvj/NQEVfBXAAObOY9OONZoSxuPPTWOx55kM2tiO7Pcbg7PcjvNHuU8xITr9HiwN1SGYRiGYRhVYhMqwzAMwzCMKrEJlWEYhmEYRpWcEBqqvk3sk2NkH/vEEBWXDnOsVckc5DX0dJT9puQCHLdo8vAhsoMqClwyXa4TaW3vIfv2W9j3y+YE57GrEebwJQAAIABJREFUgbUaxRHWy6TreX04N8Vr1F0dvJ4c6FTxtiZU3LYpFpcUgmzPhvj4DrWmP1mr4mfNsf4nmuUyzndxfvvqOL+d7ezvZ2sDl1/s2FKaJVDxBsvcirBmKNyiFDlaRKX0Ng1K99DdznqXUA3rCgop/n8l63hNPwMWqjU0cndM9LJ277JLWCMwdoTzf+HFF5E9nuBCTCtfORk3SHbrxdwG6p0KNghgaoivGerlej3pJNZdPf44azHiSs5SwevSsall3Rji5VqMY5LlOmnNcw5CWRWPMcnalb0H2J9cLsD9ur6T6zCb5/xOh/l68/eyH6/spexjDwCip24me1MHl3FTJ/t9yh65ieyiGi+bOpRPohpuh+kEa0lO28Dph5XwrSnG91i/g8euCzdzHLipu39M9nye+2W8izva6F7WkPUX+XkQauL8SIbb+fgs65tWwgaVp/F5jq23aZD1jtPD/IwIKh3XvBo8Ah1qfBzg8bRO+aBrS7MGNdrM95xhSRSOTnF+G5u5nw4Msk6tOKZiEzbx8fUtPF7XB/l+csJtbM8cD6aJYc7ghk1cfgDw4N3c7rp38mBRk+Fz0inOc7DIZR7Mc9/sT/L4O93IdkOmPHbpajnmGyoROU9EPi0ij4hIQkQOi8h/iciOCseeLCI/FpEFEZkRkS+LSGel6xqGYRiGYTybWO4N1QcAXATgGwAeBNAD4N0A7hWRC5xzDwOAiPQDuBneNzYfgvcq4E8AnC4iz3fOrfLfSMMwDMMwjBOH5SZU/wjgzaUTIhH5OoCHAPwvAL/pb/4QvLf35zjnDvvH3QngZwCuBvDZtc22YRiGYRjGM4djTqicc7dW2LZXRB4BcHLJ5tcB+MHiZMo/7noR2QPgjahyQnXli9jPyO138Hrt9d/7HtlNr3gL2ZFuXk+OgvVGyXHef2ic/agsJFljlRk+WJbHYIzX3INqzXr3I3vJdlEVo2yAV0eTak07rPyiNMzyenMqx/uH85x+rsD3gCneXxtkfU4xymv2G5T/ofrz2EfIydsGyc7GOb36mpPJbtnBGoGBGtY5TDar1eIPYXmWcSwVUF6MYmWaKWUrHdcCSzcQjrA/no561lQFc6yraFValcjAqWTv0+9xD7NurbmLdQ/9Pazlm1rg9PKO7eF93GYCyonThiBrwIaz5U6dgi3czq569WvJjoHb7cKPv0P2/B2sQQq0sD6mieU3mONmB8RUIenQf8to70T4goUWrpNHhjh/M/vZX1xdiNtlo/IJNaL8AelYgeEi6zZuGmbtZGiK9ZsA8OoLziQ7kOM4lxOzfM7YFGv/iqpMG1W8QMlxHdQFWMdVW899t6AkpIUu1lAV5lhfs/PUF5D90F2PkH10iB9DsUPsw6gtymU6N8P3O/pt1que8rYXk52bVb7AVkB9I/edyQbWE06Osu+vQJjzWIirOKF8OWQLXGZx1fDrNrCeZ/e9HI8wJNyOXC+nt0X4eZTu5PRSaW53majyK5XjSj46yv0mkeJnZn0/P1N7JlQsWMf9bPoAtzkAQC+3y/xB1tUORXj83hDie6iPcZksBLnvuUa2N8xtJTvVye3ueFj1V34iIgC6AUz59gYAXQDurnD4nQDOqrDdMAzDMAzjWcPxfOX3FgAbAPyFby++WhitcOwogDYRiTjnaMorIm8A8IbjSN8wDMMwDOMZxaomVCKyE8C/ALgNwBf9zYvfJlaKOZEuOYb2O+e+AU/sDhEpj+ViGIZhGIZxgrDiCZWI9AC4Dt6XfK937olAOouOWyIVTouqY46LLeeeRvbGDvbhdMNd95F9aMN1ZJ968ZVkjx/k2FYZpcvIxzi72TleGQ3P8FotACwIr3EXC7xu3xRkjdC+CV6D7g1yGs2d7JkiWWT9y2yE15tr1CL9nHLClI8ofzhR1jk01fN6daHAa/INvUovtIPPL0Y4/ed1DnJ6ab5eYpaP39/D69eNsdXrHhDTIiqmCB3MTwluyoL5Me0qrFt0hP2khKD88QS4e7Vv5C4yMsxtIJfi/MSEz68Z4fiG+W4Vy6+bRV533sVav+tuvJHzO8d18oIkx8TcuYm1MwCwoZNFS/EJ7guv3Ml6n54d7DvmyKG7yB5SesS997IPuebbef8RKI3R3LIRGYmNm/h/t0cnWS9ZmGHd2KGEipm5ja+fVv6BQlEeO5qU77F28PHD4HY/dvihsjxf/0NuN5EEt+OxCLeT/jbWzzQP8Ngyn+Qya1fjXVcz266N2120ifWTyQPs1+/uIuttVNg7DGzfRXZtE5fpwX33kx1Js07N1XM/+dIvv072hhez/qg1wlrFlZARzlNyhDWhuU7W0nXNc5llUqy5SoS4jBryPJiEEpzn+gjHK+xs40J0fXz81mbWMAXBcTxlnNOLnqTinqrxf3Q365Xq27lfpBt47Ek+xs+/iZB6nqlwkdLMfrcAYHKUr5kLs54x28p5HFOa1HphHVi4ljWttbVcBy7Pz/3GMJf58bCiCZWINAP4ETzZ7yXOudLaXVzqq+Q9rRfAjF7uMwzDMAzDeDax7IRKRKIAvg9gB4AXO+fo9Y5z7qiITAI4t8Lpzwdwf4XthmEYhmEYzxqW85QeBPB1ABcCeINz7rYlDv1vAK8UkYGScy+HNwn7xhrl1TAMwzAM4xnJcm+o/gHAq+C9oWoTkd8s3emc+4r/50fhfbF3o4h8Ep6n9PfDcwD6hWoz6Q7x+nFNG/thOWML641uvJ61Ij1q/baj9Wyy7917A9l7DvAK5eghjrfVEeQ1fQDIOV6jbguwjmA+obQhHXyNaWFtRSKijo+ydmXhIH9UGQ5ojRJrrJK1fHxHnteX5w4MkT2ufNeEWUaBzDyvX9+vYgXu6+Y6y86zVqW3fYDswjSvZze3Hs8HqMuIoBQtynFVrEWdr0zl1gSJgvKJlGadRU0f10FmhlfFg0oXFy9yHW5oYE1UbQtrBGYirGMbHuJ4Wf29XMZ7bmddxPwcvzzu7eV+dfk5l0MzPcX16ELcUDoH+J4vSqjYe70vJXM2wu1o8gL2LXPnhayP/OEPvk32kf2ss9AyOc3QAT5gcxf3gwNJ1lkcVnrJniG+33CA+1VIDamZ/Vxe7f2sfQkHeHAqKK0hACTTnIfaAdbHBOb5nJ5mFSExzdoQ18hjldSrsaeRx6b2LNdhNsaDQaqN+1Hufs7PzdexO8NX/g5rWrumuM6PjnM7Ts0on0Vb2U9WzVHW2X3rP39F9mt2lcekXI7JKaUbcyrG4xxr66aVbixYy3XW5VgTeiDGY8UpzdzXw11cpoUH2O9VrdLkJue5r4fBY1Ot0kA1CLeJ5jjrOXdP3kJ2doT7Tc8OPj+k7j+f4LFpspHbUHOK+wUANMS5jCYa1DMgxffUtI2P7+5lHdlsmMfXzAjXWTjMx4cWqpJ6A1h+QrWoML3K/9F8BQCcc0dE5FJ4ntU/DiALT8D+PtNPGYZhGIbxbGc5T+m7Vnoh59wjAF667IGGYRiGYRjPMlbtKd0wDMMwDMNgjkeo8pTTydIOHBrlWH6pfl7z7wudQ/Y93+H14NMvZ83A2RdfSnbd/ONk/6rA68uPPa4cqwCIdLG+JRVkbUizY12ADmXU3806gtQCr++m8nzPrexqBo8P8xp3Jsdr1KmM8p01zj6UEj3sFyQD1hAUsrz+vPcevp/Zbk7vyCHWhrRMcXozUbZzfaxzaHXlMc2WRf97oKUTLCFCTAfzO7YbKyjXMgg1cIKJNJdR7SzXIWpVHXcrfz8TrD05fJD97dR2sT+2phzrMDZsYf9s+x9n3cWlL+ZYgNd9mb8xOfscvv7IUa5jAJif53YxuJW1H5nZWrKb6lj3EKzjdhGKcj/Zchb3o7PPYm3Izi2s67rt1q+S/V//8eWyPJfSv5G1HkMqbufIfu770s6aqgU5iWwX5H7VluM2MbaBG80tY1ynl2xl/WewvtzHcX2G9ZPReW7IO1q43jMN3I5qlc+f8PR+spsjXGfZKNuzeVZttLfu5PSD3M73uuvJ7u9k3dnh29jX1isvu5js9OnsJ/And/L4uzDNY2Esr8YeVV4TAT5+JRTCLJhMDnNfirYrvWS/0gzFeIDvbGBfWJtUvMNChjVGoYLS+zSxbq4/y2UebuR2J53cRsKpzWQvCOe/UOB+0dbK6c0kuJ3nj7AmywV4/3yUn8mtszzeZ5q4PAFgfFrpjidVDMcu1ktG9vLYs2Ur67oaMjy9CTVwmaYz3K5Tzcfh+1Bhb6gMwzAMwzCqxCZUhmEYhmEYVWITKsMwDMMwjCo5ITRU9z3CWpdQLWsEZIzXXhdqlY5jcJDsew7dTXbyEdayLMR4fbcwxterq2ffNQAwcXAP2fEir+cmGniNeXaS17CL87zGXJtlXyvNfTz3PTzNvlemlNylSXi9OLHAOovZDl5jz2d5/TicYV1CfIjLOBdlHVnE8Rp9UMW77tjMWpLcGK/Bp+JcB/nDx9E0tQ+ihYpHlaBEU1pTpQjUcBmIcrGUDvOa/kQNV0ogyGWc2KNiRsZYY7XztDPITrI0BC0xzsD4JOs28n0qXtcgx2D79Te/iuyOEOsmQh3KGRmAfhXDa86xpkpC3O47VAzI4Qk+vq+X+0X2CFdioY61Ijtewpqqrp3vIXs5DVVyP/eLQie3s/EA10kwxD6dpsF+p2oCHF9MAqzL2BlhXdr46exf7oaH2cfdRVsuKMvz+S/jmIrFiVp1BI8l00oPE4ixHjEwzw0p7/ieWmv4nltb+XrJw9y385EDZJ9z9iDZv/whx1bdXMvn7zjtbWTPzJ1F9sERHitaklyG+zPczpMN3IaGR4ewWsYXWL/TGlVx4YT7ckuN0hImWYsXm+JnSvtW1mjVxdg3WEOMtXZNzTz+N2zkOhxTmqU2JVE6GuE2svAY2xuDPD6PNHE/7ergfjwX53EgPcntvqmF8xub4za1kFWBUQFEwc+c5gznoZjgsWAkwM/MsX084Pdt4fEuMMdlVHR8fCKh+9XqsTdUhmEYhmEYVWITKsMwDMMwjCqxCZVhGIZhGEaVnBAaqtE463XSMfbhkQ3wmnxuN+sk5tpZA9B6GsdBgvAa/cKD7LfkgVnWkuSGee0WAIJBXnN3kWGyJ1rYmVZXhIt+fmqI7GSC19CDRfYlMzLOeU7m+PrJOhUTrMjaEFngMgkpfU/TFDu6ijv2x9PeyDHJOlq4jkJ5LuPRg1xmyQbWK4WHufwiiXJ/PMuiW3NeiY7AZYomzkPTHGsx5lQwv2ye8zSW5nZ4an6I7FSik+xDB7gM+0/aRHZjlDVUh4+yHV3g/LS1s24jmmINwNF7uQ22hrmO6/o4/WCA97skpw8A8YLSzxRZO9fcztdIpbndbdvMuoacSmMmxO22q5U1VnUpbve5KOu+lmO4wPqaXqWBOnrPbk5/nP/nnN/A/bBZ6ZfG5llLEiuyluYUpTELNbOuY2SC9UEAEE/yNd0Cj28uwoKZhhiPV7NKE9qZ4n6w0Mv9JHiYj4/XcJ3uKHA7libW3xRaWec2eDa3kRrHdSoqPmRdM5dRpI77WWaGyyM8zmXW2cR1fN+D5Xqd5ZA8962FOh4vox2qbwxzmdY3n8J5VBqo5jy38yNFrtOJMGuO6hq43S2EuB+6DKefbeA6rUtyPywG2Bdj8yDnp6GGdWwp4TJOZFhT1duo/BqqNjfTxG0o4Lg8ASAVZF1WUvlHK+T5HuoX+BkTquEyLiodmYS5zMJNfA/R4HE8cxT2hsowDMMwDKNKbEJlGIZhGIZRJTahMgzDMAzDqJITQkOVG+X142wNrxfXtPF6caiDY/50q9hTp52kYl/V85p/eoDXr1Ofv5PsXwhrCAAgVMtFubGRtRFzc7ygOx1hjVJLlH1sHJ5mvUz6dl5fDmxgXUBblstksol1Bq11nOd0hnUPC/WsYZpXvm4awX5TWppYfzM2yvG2OvvYh0isne93PsYarJY8+1DKhMvX2JelTCqhNFNoVbs5j1kVlw3cDCCqDAIxLuPxI6y/6e3mOgvmWP+Tn+N2m4ipuHat/P9OLsk3OJ7hMhvOc/7aN/P9djWcTXZgnHUgoYzSQYxxmwCAxi7WLNUOsDYiGmTdWCO4XY9Nsa4rEOYyCIN1ErNzrAcKNnGlOKfr+Nj09XG7nk5zHSLGOrrGXvbR1DPPep7aGtZhSB1fbw5c5/PT3OYG6ljLki+Ut/tNY9wu75tlvc1ME7eD/gLXe2iK29Uk2D9adp7TDOf4HptHuA6G+7jdRKZYhxZVsU8njvLY2LSN77nYyZop7aNvYprH0qMNStN15iDZ81M8dg6Psq+vldCc4zSKEa7Xmnl+ZvTu4DKsySs9z5TSzKa5jnINSuOkYmSGdrPOLFnDZd7cxn0/O8NlkKrnsWNTp/LJJNyOW7OqX6jYhq3C1xtVviFzSrMVnOE2FWoob+djyldWTvmFqhfWCUe3cRk1tXA7r6lhLV0xz/3mwOz/b+/Moxu76jv+/UmyLC+SZXlfZuJZ7MlkI3tIytI0CZSSA+VAaGlaGgokhENbCgHaoU3LUgrllCWkLCkpS2nI3kNCAg0UQiAJCUlmMpnJMqtnxtuMbY0kW7Isy7794z6f6Hstj+0RGdnx73OOjn3fu5Lu+937fu/q3e/7/fgYpkedYz4O9A6VoiiKoihKieiESlEURVEUpUR0QqUoiqIoilIiK0JDFW7h9dnJI9zs+BSv1xonqdv6EL+/d4jz4KXirG/qrOT189a1vF59gZPXCAA2ruui8hNbe6nckuL126kxnstm21hr0tzAepapXRzPxn+UNUkzknP285p1JsM6jKCzBl4Lrp+N8ho7Yk6+xH5eo6/O8ffnnLhSOUcb4/M58XaSrF3JR+fqdxbCUUhhHE7+QLB+JRrh70w4mio4qexi9RupXLOB1/iTWSeuiZNLsGbcyZc1tJvKa9r486ebOM5K7+5nqFzn43EeCPGYgI8PoNrHNp328Zjry7O+adPGU+EyPsHHEBhkXUNrN2ui+EwCAiyTQH2U649keVzVO1oUmeFxesQZpgtxoJc1TVknF2HvOOvgImN8vAcquc9aK1knFxP2FTUtrCmLi5OfLM6DpLmIRx40bLQBZ5zGD3C582zWgjSezOPime3sW8xePhf3VPE4aWlfS+XNadY85bp4HI7H2WZvuvw1VJZa3r9vP8f9ywxwuaqNfWf2MSfP3MUcM8mf2kHlvhon6eYi2D/I+QlPa+Nzcf161vIF02zj0Ti3MdLMGqTgCPd7xTD758YA5w5M1vMxzID7KJ3kczeXdOK1OXnxjsS5PaOt3J5I+HQqn97JOSklz3EHEzP8+T4n5p0vzWNkIOcEiQIQPsp1EgHu9wo3v2AF21ycc3Wqgn1Hb5CvoZNptnl7Nevijge9Q6UoiqIoilIiOqFSFEVRFEUpEZ1QKYqiKIqilIhOqBRFURRFUUpkRYjSAyFHTNzA4rFJJwhZxM9K1akxFoam+1mQHc9wQLDpPIt15WwWG1+2noWnANDfxyLA5v0HqOyLcRCyfeMswEsPcwDBRJAFz5On9HD9vSyw62li0XdVltsYDPEx+yIs+POlWQQZH3ESTLPeD+1RDg7oiioHJnmunmt31Mh5tqkvyiJFv88RWC+Co3O28MMIUdagI5FgAfQcnBzYFzqJbTtnOEChv55t6E87ARQrWCQ+YZwEp4bHRIXhPms7qYvKhydZGNrhjJlgLcv0A1P8YEE2wkLXte1OEM4jHEwQAF5x1gVUPnCIx20gx+9JJTkZcvQkLuccMW1TlM+TvBPkMTXA5boWp1MXoGMNB/X9/Be+TuXWdvYVzz7PfdgS5TEwOs0nRp8TKDTXt5PKYeeBmei082DEBAfFBICKhzmw8NVv4j5o6DmfylVOcuIKw+fyMy98lsov9PJDOhvP4z7aVMHn6uEMn9uNOfZ9/mYWTDdO8oMEM90cHDbUz+8/r4Ft0h/kBx8Cr34llTe088Mc2/Zto3JVkIMQA31YiJ5utsHMIe6Xg9vZt3TXsYi7fS2L1keG2L8FnMCW+T52NnVrncTnp7MNn93JD0/s3O6Ms0n+fr/hhz8GmthbBg+wb8rnORD00yn+vLVn8rhv6ufrzUzYeZhj1HlCZ2LuvZy033nQKs/jojrF/nJmhOv7O50Hmcb5wYLJSvZv4Rh/3pFDc/3dUtE7VIqiKIqiKCWiEypFURRFUZQS0QmVoiiKoihKiRxTQyUipwL4JwDnAGiFFaU8C+Dzxph7nbqbAXwRwKsA5ADcB+BDxhgnguPSqc3wGnhbtRPNr4XXPqd38Vpqxxmsi9g9wQlVJw9wMuVxw1qUmRHWiTz88Fx9z+GoEywvwVqLmcReKkfzvIY+Ve139vMa9niW17Cb2llXIH4nGF+zk7TVCZJ2dJq1HPER1uNEW/h4Jp3Ew/kkr7mnnVyXsSgHjjswzhqvQJg1BdkgD8Xg2NKTI0fh6GlYNoBEgnUPdVEeF8lcP78hw9qMmQvOpbI0OhqlCR439Rs5qXfNAJ8Kg2le029McB9nmlhDMDrF+8MZbv+4n48Pcdb/REJOstFOtnGtnEHlaIDrA8DUiNMvzawbS2RZ4xTzs/bD5wSUHa1l7UWql4M61vpY5xBq5+8fSi5tnDw1vJXKu/Y/TeVmJxCnk+sZaWHd2UTOSchqnHFdzeelz8m/2phmbUmone0JAKNHuF93DvG5dGkXj6OOTRfN+YxCzu38OZX3DX2fyoM7WHuSbWL9pq+C25h2gq1WTLF+JlPJvqhyhH3PZJg1VSnHJg3r2N9fXPcGKgfXOsFhH+A+CEa7sFTGB9kftXfwNaiilo85P8YJow8Msf9PD7M/3TrGgS+7TmEN6snTThLvEJ8nB/OPUTkz6gT5DfDnheu4vZX9vP8VpzrHV83tPzzMGrLwGGsRI9N8byboBMmsq+U+fbjP8bUAojPONdDR6o06YYJbAnwNGoiyb6nOO4nU+1k3lknx963pYt3c8bCQKP0kAGEA3wEwAKAawFsB3CMi1xhjbgIAEekE8BCAJIAtAGoBXAfgdBE53xiz9LDXiqIoiqIoK4RjTqiMMfcDuL9wm4jcCOBJAB8CcJO3eQuAGgDnGGMOevUeB/ATAFcV1FMURVEURXnZsWQNlTFmGsAhgNZX3grgh7OTKa/eTwHsAvD2UhupKIqiKIqynFlUHCoRqQFQBaAOwJsAvAHAbd6+DgDNAJ4o8tbHAfxBqY185jDre07rceJJjPGK4t4Ir+E3drDeaPRxJ7FjiM2QPsSahecTvD6dT7DGAAAmx3lNuXa4l8rVQdYw7RnncsSJsRH2cxySyhpe8zZR1nKsb9hM5d1Pb+f2NLK+KHmY15eb1/D+iRQfc2jKiTcUYh0Hppy4Wn28Zr7uFaxHGu51kiW38/r3eAVrsBZDwok7hRTrEJrWsI2GD7HuIRbheD6BV3Eb2mtYE1XXwXGoMkdYsxRPs3akdtMp/Hk51lUEJlj7MTXIa/x1VawVaetiLUsux5qCypATa6yOz5u6MbZXpoHPm/Y2J/gYgPE0n2uBYe638DrWTqTirHtIJFnv2BDncRNudzRKw2yD/kHWP3Y0z9V5HYutD7NeMhbj82p3gm2GBH9fpIr7rKaVtSZNGfYltVOcuDfVyMeTaxyi8lQj64kAICn8nf/91Zup/KNf30flVz79AJXPO+11VG7bzBor369voXK2gr/v8UfZl2w8hcfZ2lYnBlEVC89qYiwcm8w62kUnsW6Vc1lad8Y5XG7lcf/ko6zHiVWxb8uOOAHlFsG0eY7KBhuoXDnO4zrZzHrK8GHWS/YOclzC0CbWLEX9/P7xWj4369n9IuxnX1A5wefhVID9a7iK21MX5fa31bJvy0acuFNOEvPpJj7PR9Ksfaxr4vpHnRh5G4r4997tvVRuiPF7asdYU5qYYl/UNMrXzKgT6zEcYH+Wjzo2n+JxfTwsNrDnvwG4xvt/BsDdAD7glWfTcA+6b/K2xUSk0hgzWWS/oiiKoijKimexE6ovAbgTQDvsEp4fwOxPydlpZLEJU7agDu0XkSsAXLGUxiqKoiiKoixHFjWhMsY8D2A21v13ReQBAPeKyAUAZu8tFlujmX3edcLdYYy5A8AdACAixt2vKIqiKIqyUjjeXH53AvgGgB68uNTXVqReG4B4qct9DU6sm51P8drptJMzrWs9r08fTbNuY2SadQzjhzh2Tm+a16MPjPL6c+XA3LVWXxXH6ahq4TVmM8Vr1A0t/B0j+3iN/VCeTdaS4PXjyTRrtnpDjn4mxvqe/QOcr6t1LdtoeBevqaeE62cDHHvLV8WxYWKGtSiH67g9gVGOq9XU0kVl5JxcV04ep8UQCzif2cX9PLzfye23njVV8YPch29Zx9qTgJNbLxJincC6Hh6nfQf485IDrOXoqeU1/aF2/ryWONt0rJr1RUeq+P3VET7e8BHWpuSm+fuzNRybJhbhMTAD3g8AbWEel4k0a472x9nGnRn+zBpwG00H68wgrIc53MjftzbHv9v2HHByRC7A1m2PUnkoyee2BPm3X9B5bKepms+rhHG0NDG2WdLxfFOVXD/ouM3+/rkZKaWC2xRq5zaEnXhm9//gZ1T+zS/ZptFu1pTunuRz2xzYzfXPYu3f5AS3uSLOfdpR62imjvL+wAz32ZiTY635JNbv+Pr5MjXQy7q2vj2/oHKTEzxsYJp9z2IYyPF3jh3iOIJnh1kTWhPh79gWZ33OUSd24msN+4rQEPuWg84tho4Yj5uOKtZ01W9gf51+gX3P0aOsL0o3sL89cwPbvKV2LZWH93D+wwbHN/i6WIM7PsG+LD/KY7Cn2c2vCIw38TH6Jt1x4+Ta83H92kn2PS+keZxNOclZ/U6sruAe1jMeD8cbKX12ma/OGNMPYBjAuUXqnQ9gW5HtiqIoiqIoLxuOOaESkTlhe0WkAsA7YZfxnvU23wXgchF+z6pLAAAOvElEQVRZU1DvEtg7WHf81lqrKIqiKIqyDFloye8bIhKBjYLeD5t+5koAJwP4sDFm9tn4z8AKzH8uIl+GjZT+EQDPAPjWS9FwRVEURVGU5cJCE6rbALwbwLUAGgCMwUZJ/5gx5p7ZSsaYQyLyWgBfAPBZvJjL78O/jXAJ+3ipFNkJXhs1IV7Pvfy8k6l8cB/rJOIJjkPl695E5dy2p/j78rx+HI1x7BkAGI8nqDxa58RCcWJgzBzlNuQcXQKy/J2+Nl6D7zasMUqmHDPneY18Q4C7+oiTWyojbKNoNd+8NGOss8iHeY08FuM1+rXt/P5DWzmqRmQDaww2dLAmYfujz2KpxB05Tt1eHjjRLtYFJPZxrJlKJ1ZW2s82C61n7Uk+xTZMtLDwYePZHINoOs06h1yS+7RNeMzUNHOfmizb3D/D3ydVrLEKxdjG+Qjrc9JZPr4pHpIIObFqACDp5Fhsd9JqTtTw/kFHM9XuY91Cxo0tc5THTZ2TNnN/ytH7yNJ0D+kca0syTnycDsM2iZ7OMZb8jg4jZ1gbmZvizwuFWNfR5Of2Dw+xXqm9em4uP9+E46ZT3FHbx1hTtKGS/eGeDOd9mx7iE2XEyUcYcZ4heuCXv6TyVe9i/1q9/rVUHnXyL1ZXcp9P9bMv8G1kXVxqmLUyzQEeQz96hPMx9o5zXMBcljW1gSI5KReiq4L76bAj1UvxqYaJJPvb8Wo+hmCE88TF29hGZoiPyR9nm+QqeJwFutmmjb08TtPNfP2QSj6A9etZg4UUxz/LOPluEy18/co6MfeMn31Frok1wQ3CY7hyxEkOCyDm6CUzwhrXzBEnhl2QdVsjPu7nYCXrOyeM4w/HnGflxNFoHQcLpZ65FcCti/kgY8xOAK8vuUWKoiiKoigrjOMVpSuKoiiKoigeOqFSFEVRFEUpkeONQ3VCmdnPmoANtbzeu+a0M6mcDHBck+xYL5Vra1j40ZvhtdZcH+seuvy8Pl1fMTc+T6yV404lJzkOU3baWWOvYNPHOjmOiUmyJmlwnLUibR3cpu4Q62/6wqw7GJjiufOaTn5/9QCvgY9M8zFOR7gcCbKNk/W85j6RZRuHT2H9Ut8e1j2cd8ap3L6LnKF5LxZmgr8zuZ7j0WCvE4+mhnUM5zbw++vGWKvSPsKRQXouOo3KwxWsW8ik2CbGz2NE1rIuISCsAaiZYL1OZYTX/FNV3Ke+DI+B6pijN3JMWjXBfZhy8kXOmLk50GoaWNsxkGcbZ50cZr4cj6u91RxDbmaMbRDxc/6t4SnHRjysgUZ3w7Hx57nP2wIshjF+tvHwJOs6Zma4fRU13Ae1We7zunrug6lh/rxAG/e55LkPAGDa+dlb4WcNZzjA/ZQaYN8hm7ifo0Nss4ST8zFkeFxP51kv8xMnztWmC/ncrnfiSlWNsc2yjs1TB1m7sivF7Znq5RhGTx38DZVzac7JaSZZKzM5wWNyMZgEa46anbh5qUnev87pV0nyONsQZd8yMMG5TiuGeJy0ruHrR/wA26zaORFa29nmYxNO7sEJR/NUxRqm6Vr279Vj3CeVTi5am773RfLVPI5jh7n9o/V83ld3z/Ut/l85sRQ383cGxnqo7AvzddoX4HE2GGSbNbi6sDHWpYnrII8DvUOlKIqiKIpSIjqhUhRFURRFKRGdUCmKoiiKopSIGFP+vMSaHFlRFEVRlBXEHcaYtxdu0DtUiqIoiqIoJaITKkVRFEVRlBLRCZWiKIqiKEqJ6IRKURRFURSlRHRCpSiKoiiKUiI6oVIURVEURSkRnVApiqIoiqKUyHLJ5fcUgL0AOgH0LVBXOTZqw9JRG5aO2rA01H6lozYsHbXh/GxwNyyLwJ6ziMjtbqAsZWmoDUtHbVg6asPSUPuVjtqwdNSGS0OX/BRFURRFUUpkuU2o7ih3A14GqA1LR21YOmrD0lD7lY7asHTUhktgWS35KYqiKIqirESW2x0qRVEURVGUFYdOqBRFURRFUUqk7BMqEakUkc+JyICITIjIYyJyWbnbtRwRkfNE5EYR2SkiaRE5KCK3i0hPkbqbReTHIjIuInER+S8RaSpHu5czIvJxETEisqPIvotE5FcikhGRIRG5QURqy9HO5YaInC0i93hjKyMiO0Tkr5w6ar95EJFuEblVRPo8+zwvIteLSLVTb9XbUERqReQTnj+Le+frVfPUXZTfExGfiHxURPaLSFZEtovIO17ygykTi7GhZ5OrvPP6kHeN2SEify8ioXk+990i8pxnw90i8pcn5ICWKcshDtW3AbwNwJcA7AZwFYD7ReRiY8yvytiu5cjHAPwOrFBwO4BWAB8A8JSIvNIYswMARKQTwEMAkgC2AKgFcB2A00XkfGNMrhyNX254dtoCIF1k35kA/g/AcwA+BBuP5ToA3QDecAKbuewQkdcBuBfAVgCfAjAOG5Ols6CO2m8eRGQNgMdhz88bAcQBXAjgEwDOAfBmr57a0NII4HoABwE8DeB3i1Vaot/7ZwB/C+A/APwG1ua3iIgxxtz6Eh1HOVmMDasBfAvArwF8HcARvDguLxGR3zMFomsRucardxeALwB4NYAbRKTaGPO5l+5QljHGmLK9AJwPwAC4rmBbCMAeAI+Us23L8QXgIgBBZ1s3gCyA7xVs+yqADIC1Bdsu9Wx9dbmPY7m8ANwKe8F6EMAOZ9/9AAYARAq2vcez4evK3fYy2iwCYAjA3QB8x6in9pvfNls8O5zqbP+Ot71ebUh2qQTQ6v1/rnf8VxWptyi/B6ADQA7AjQXbBHYydgiAv9zHXA4bAggCuKjIe6/36l9asK0KwAiAHzp1vwf7A6u+3Mdcjle5l/zeBmAawE2zG4wxWQA3A7jQ+yWneBhjHjHO3SVjzG4AOwFsLtj8VtiBfrCg3k8B7AKgQdoAiMhrYMffB4vsiwC4DHaSmirY9V1YZ7GabfgnAFoAfNwYMyMiNSJCfkTttyAR7+9hZ/sggBkAObXhixhjJo0xQ4uouli/92YAFbATsNl6BsDXYO8CXvjbaPdyYjE2NMbkjDGPFNn1P97fwmvMxQAaUGBDj38HUAPgjcfb1pVMuSdUZwHY5TgMwN4OB4AzT3B7VhwiIrAXuBGv3AGgGcATRao/DmvzVY2I+AF8BcA3jTHPFKlyOuxyONnQm8xuw+q24aUAUgA6ROQF2It7SkS+VqCzUPsdmwe9vzeLyJkiskZE/gjAtQBuMMakoTZcEkv0e2fBLvM/V6QeoLZ1afX+jhRsm7WRa+8nYX8UrEoblntC1Qb7q8xldlv7CWzLSuVK2FvYt3nlNu/vfHaNiUjliWjYMuZ9AE4C8A/z7F/Ihqt5XHbDXuh/AOB/Ye8K/CesTb/l1VH7HQNjzI9hx95lsDq0g7DLz18xxvyNV01tuDSW4vfaABz27kq59QC1rctHYX9E/ahgWxuAaWPMkcKK3oR/FKvUhuUWpVcBmCyyPVuwX5kHETkZ9hbro7D6C+BFmy1k12L7X/aISAOATwL4lDFmeJ5qC9lwNY/LWljx6teNMbNP9d0tIkEA14jI9VD7LYZeWM3OXbAXoDcC2CIiQ8aYG6E2XCpL8Xt63VkkIrIF9q70+40xiYJdVbA6tGKs2vFZ7gnVBKxYziVUsF8pgoi0ArgP9omWtxljpr1dszZTuxbn07BPVX3lGHUWsuFqtt/ssX/f2X4LgGtg9ScZb5varwgi8sewutEeY0yft/luT4v2ORH5PnQMLpWl+D297iwCbxn60wBuNsZ8zdk9AStiL8aqHZ/lXvIbxIu3aguZ3TZwAtuyYhCROtjbr1EAv2+MKbTT7G3r+ewaN8as1rtT3QCuBnADgHYR6RKRLlgHUOGVY1jYhqt5XM4euyuonr31Xw+130K8H8DWgsnULPfA3v07C2rDpbIUvzcIoNXTn7r1ALUtxMaC/C7sj/b3FakyCMAvIs3O+4KwYvVVacNyT6i2Aejxnmgp5IKC/UoBnvD3XgA9AC43xjxbuN8Y0w9gGPbRWJfzsbpt2gE75m8AsL/gdQGsPffDPiK8A0Aejg09Z3EmVrcNn/T+djjbZzUTw1D7LUQLAH+R7RXe3wDUhktiiX5vG+zEdbNTT687AETkAtgn+54A8HZjTL5ItVkbufY+F9bHrkoblntCdSesY7l6doMnHHwXgMeMMYfK1bDliPd02m2wyypXGGMenafqXQAuLww7ISKXwE4aVnP28B0A3lLktRNWGPwW2NvbSQA/BfCnIhIueP+fwWqIVrMNb/f+vtvZ/h7YCcCDar8F2QXgLJmb4eAdsE9IbVcbHheL9Xs/ADAFe6dwtp7A3onpB1AsdMCqQEQ2w96V6oX9wT7f0t3PYKUT1zrbr4Vd8r/vpWrjckbmPuhwghsgcjvsheyLsAE9/xz2F8UlxpiHytm25YaIfAnAX8Peobrd3W+M+Z5Xbw3s00MJAF+GdcAfAdAH4LzVuuQ3HyLyIIBGY8xpBdvOhnWsz8LqXToBfBjAQ8aY15ejncsFEbkZwF/AjsFfwEZdvgLAvxhjtnh11H7z4MVA+xmsGP1G7+/lsNHPv2mMea9XT23oISIfgJU4tMNetO+G9XGAfToyuRS/JyL/6u27CTZS+h/CPhhwpTHmlhNyUCeYhWwIO5nfCXv3eQvs5LKQvYU/4kXk/bAPRd0J+8TvqwG8EzZG3WdeuiNZxpQ7siisfuXzsGuyWdhYIK8vd7uW4ws2fo2Z7+XUPRV2kKcBHIWNYNtS7mNYji8UiZTubX8VgIdhBZZHYC9+4XK3t9wv2KWpf4T9FZuDTRn1QbXfkmx4Pmwk9EHPhi/AXsQCasOi9uo9hu/rKqi3KL8Huzrzd97nTsLevb6y3MdZTht6r3mvLwC+XeQz3wvgec+Ge2ADJUu5j7Vcr7LfoVIURVEURVnplFtDpSiKoiiKsuLRCZWiKIqiKEqJ6IRKURRFURSlRHRCpSiKoiiKUiI6oVIURVEURSkRnVApiqIoiqKUiE6oFEVRFEVRSkQnVIqiKIqiKCWiEypFURRFUZQS+X+sck1qxffwCgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: cat (3)\n",
"Image #1: dog (5)\n",
"Image #1: cat (3)\n",
"Image #1: horse (7)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: bird (2)\n",
"Image #1: cat (3)\n",
"Image #1: dog (5)\n",
"Image #1: deer (4)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: automobile (1)\n",
"Image #1: dog (5)\n",
"Image #1: ship (8)\n",
"Image #1: ship (8)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: frog (6)\n",
"Image #1: cat (3)\n",
"Image #1: deer (4)\n",
"Image #1: deer (4)\n",
"[Step #0] Loss: 1.1130 Accuracy: 0.0000% Time elapsed: 0.8761s (total 64 images)\n",
"[Step #10] Loss: 1.1064 Accuracy: 0.0000% Time elapsed: 5.8331s (total 704 images)\n",
"[Step #20] Loss: 1.1037 Accuracy: 0.0000% Time elapsed: 10.8326s (total 1344 images)\n",
"[Step #30] Loss: 1.1035 Accuracy: 0.0000% Time elapsed: 15.8694s (total 1984 images)\n",
"[Step #40] Loss: 1.1056 Accuracy: 0.0000% Time elapsed: 20.9180s (total 2624 images)\n",
"[Step #50] Loss: 1.1048 Accuracy: 0.0000% Time elapsed: 25.9474s (total 3264 images)\n",
"[Step #60] Loss: 1.1056 Accuracy: 0.0000% Time elapsed: 30.9362s (total 3904 images)\n",
"[Step #70] Loss: 1.1065 Accuracy: 0.0000% Time elapsed: 35.8943s (total 4544 images)\n",
"[Step #80] Loss: 1.1068 Accuracy: 0.0000% Time elapsed: 40.8272s (total 5184 images)\n",
"[Step #90] Loss: 1.1067 Accuracy: 0.0000% Time elapsed: 45.7350s (total 5824 images)\n",
"[Step #100] Loss: 1.1062 Accuracy: 0.0000% Time elapsed: 50.6297s (total 6464 images)\n",
"[Step #110] Loss: 1.1051 Accuracy: 0.0000% Time elapsed: 55.5172s (total 7104 images)\n",
"[Step #120] Loss: 1.1054 Accuracy: 0.0000% Time elapsed: 60.3971s (total 7744 images)\n",
"[Step #130] Loss: 1.1049 Accuracy: 0.0000% Time elapsed: 65.2678s (total 8384 images)\n",
"[Step #140] Loss: 1.1054 Accuracy: 0.0000% Time elapsed: 70.1505s (total 9024 images)\n",
"[Step #150] Loss: 1.1052 Accuracy: 0.0000% Time elapsed: 75.0328s (total 9664 images)\n",
"[Validation] Loss: 1.1102 Accuracy: 0.0000% Time elapsed: 77.7038s (total 10000 images)\n",
"[Size of Perturbation]\n",
"Average L0 distance (the number of changed parameters): 3030.032\n",
"Average L2 distance: 2.4227688247680663\n",
"Average MSE: 0.0019131853790953755\n",
"Average Linf distance (the maximum changed values): 0.0627451241016388\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CGuOdNuM6MJS"
},
"source": [
"* Black-box attack"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "AjKZWIpS6Llg",
"outputId": "1e4baf73-8052-4f6f-c493-3477cec85070"
},
"source": [
"running_loss = 0.\n",
"running_corrects = 0\n",
"running_length = 0\n",
"\n",
"running_l0 = 0\n",
"running_l2 = 0\n",
"running_mse = 0\n",
"running_linf = 0\n",
"\n",
"start_time = time.time()\n",
"\n",
"for i, (inputs, labels) in enumerate(test_dataloader):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" adv_untargeted, perturbation = pgd_attack(model, inputs, labels, False, eps, alpha, iters) # adversarial attack\n",
"\n",
" outputs = black_box_model(adv_untargeted)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" running_loss += loss.item()\n",
" running_corrects += torch.sum(preds == labels.data)\n",
" running_length += labels.shape[0]\n",
"\n",
" l0, l2, mse, linf = get_distance(adv_untargeted, inputs)\n",
" running_l0 += l0.sum().item()\n",
" running_l2 += l2.sum().item()\n",
" running_mse += mse.sum().item()\n",
" running_linf += linf.sum().item()\n",
"\n",
" if i == 0:\n",
" print('The dimension of an image tensor:', inputs.shape[1:])\n",
" print('[Prediction Result Examples]')\n",
" images = torchvision.utils.make_grid(adv_untargeted[:4])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[:4]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[:4]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" images = torchvision.utils.make_grid(adv_untargeted[4:8])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[4:8]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[4:8]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
"\n",
" if i % 10 == 0:\n",
" cur_running_loss = running_loss / running_length\n",
" running_acc = running_corrects / running_length * 100.\n",
" print('[Step #{}] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(i, cur_running_loss, running_acc, time.time() - start_time, running_length))\n",
"\n",
"epoch_loss = running_loss / len(test_dataset)\n",
"epoch_acc = running_corrects / len(test_dataset) * 100.\n",
"print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, time.time() - start_time, len(test_dataset)))\n",
"\n",
"print('[Size of Perturbation]')\n",
"print('Average L0 distance (the number of changed parameters):', running_l0 / len(test_dataset))\n",
"print('Average L2 distance:', running_l2 / len(test_dataset))\n",
"print('Average MSE:', running_mse / len(test_dataset))\n",
"print('Average Linf distance (the maximum changed values):', running_linf / len(test_dataset))"
],
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"text": [
"The dimension of an image tensor: torch.Size([3, 32, 32])\n",
"[Prediction Result Examples]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: automobile (1)\n",
"Image #1: bird (2)\n",
"Image #1: truck (9)\n",
"Image #1: ship (8)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: automobile (1)\n",
"Image #1: bird (2)\n",
"Image #1: airplane (0)\n",
"Image #1: airplane (0)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: frog (6)\n",
"Image #1: cat (3)\n",
"Image #1: frog (6)\n",
"Image #1: horse (7)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: frog (6)\n",
"Image #1: cat (3)\n",
"Image #1: frog (6)\n",
"Image #1: deer (4)\n",
"[Step #0] Loss: 0.0241 Accuracy: 68.7500% Time elapsed: 0.8825s (total 64 images)\n",
"[Step #10] Loss: 0.0329 Accuracy: 62.3580% Time elapsed: 5.8430s (total 704 images)\n",
"[Step #20] Loss: 0.0330 Accuracy: 61.6815% Time elapsed: 10.8252s (total 1344 images)\n",
"[Step #30] Loss: 0.0320 Accuracy: 63.3064% Time elapsed: 15.8189s (total 1984 images)\n",
"[Step #40] Loss: 0.0319 Accuracy: 63.6433% Time elapsed: 20.8353s (total 2624 images)\n",
"[Step #50] Loss: 0.0320 Accuracy: 63.6642% Time elapsed: 25.8391s (total 3264 images)\n",
"[Step #60] Loss: 0.0325 Accuracy: 63.3709% Time elapsed: 30.8374s (total 3904 images)\n",
"[Step #70] Loss: 0.0329 Accuracy: 63.3583% Time elapsed: 35.8385s (total 4544 images)\n",
"[Step #80] Loss: 0.0329 Accuracy: 63.3295% Time elapsed: 40.8405s (total 5184 images)\n",
"[Step #90] Loss: 0.0333 Accuracy: 62.8949% Time elapsed: 45.8427s (total 5824 images)\n",
"[Step #100] Loss: 0.0336 Accuracy: 62.3917% Time elapsed: 50.8407s (total 6464 images)\n",
"[Step #110] Loss: 0.0334 Accuracy: 62.3592% Time elapsed: 55.8328s (total 7104 images)\n",
"[Step #120] Loss: 0.0332 Accuracy: 62.4096% Time elapsed: 60.8195s (total 7744 images)\n",
"[Step #130] Loss: 0.0334 Accuracy: 62.2615% Time elapsed: 65.8033s (total 8384 images)\n",
"[Step #140] Loss: 0.0337 Accuracy: 61.9016% Time elapsed: 70.7765s (total 9024 images)\n",
"[Step #150] Loss: 0.0335 Accuracy: 61.8791% Time elapsed: 75.7443s (total 9664 images)\n",
"[Validation] Loss: 0.0337 Accuracy: 61.8100% Time elapsed: 78.4536s (total 10000 images)\n",
"[Size of Perturbation]\n",
"Average L0 distance (the number of changed parameters): 3030.1533\n",
"Average L2 distance: 2.423288898849487\n",
"Average MSE: 0.0019140023371204734\n",
"Average Linf distance (the maximum changed values): 0.0627451241016388\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_7iMQYbKyKiU"
},
"source": [
"#### MI-FGSM Attack\n",
"\n",
"* White-box attack"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "STmJnh880gXQ",
"outputId": "6a69dac1-2319-481c-d0e9-1486901504e8"
},
"source": [
"running_loss = 0.\n",
"running_corrects = 0\n",
"running_length = 0\n",
"\n",
"running_l0 = 0\n",
"running_l2 = 0\n",
"running_mse = 0\n",
"running_linf = 0\n",
"\n",
"start_time = time.time()\n",
"\n",
"for i, (inputs, labels) in enumerate(test_dataloader):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" adv_untargeted, perturbation = mi_fgsm_attack(model, inputs, labels, False, eps, alpha, iters, decay=1.0) # adversarial attack\n",
"\n",
" outputs = model(adv_untargeted)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" running_loss += loss.item()\n",
" running_corrects += torch.sum(preds == labels.data)\n",
" running_length += labels.shape[0]\n",
"\n",
" l0, l2, mse, linf = get_distance(adv_untargeted, inputs)\n",
" running_l0 += l0.sum().item()\n",
" running_l2 += l2.sum().item()\n",
" running_mse += mse.sum().item()\n",
" running_linf += linf.sum().item()\n",
"\n",
" if i == 0:\n",
" print('The dimension of an image tensor:', inputs.shape[1:])\n",
" print('[Prediction Result Examples]')\n",
" images = torchvision.utils.make_grid(adv_untargeted[:4])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[:4]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[:4]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" images = torchvision.utils.make_grid(adv_untargeted[4:8])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[4:8]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[4:8]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
"\n",
" if i % 10 == 0:\n",
" cur_running_loss = running_loss / running_length\n",
" running_acc = running_corrects / running_length * 100.\n",
" print('[Step #{}] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(i, cur_running_loss, running_acc, time.time() - start_time, running_length))\n",
"\n",
"epoch_loss = running_loss / len(test_dataset)\n",
"epoch_acc = running_corrects / len(test_dataset) * 100.\n",
"print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, time.time() - start_time, len(test_dataset)))\n",
"\n",
"print('[Size of Perturbation]')\n",
"print('Average L0 distance (the number of changed parameters):', running_l0 / len(test_dataset))\n",
"print('Average L2 distance:', running_l2 / len(test_dataset))\n",
"print('Average MSE:', running_mse / len(test_dataset))\n",
"print('Average Linf distance (the maximum changed values):', running_linf / len(test_dataset))"
],
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"text": [
"The dimension of an image tensor: torch.Size([3, 32, 32])\n",
"[Prediction Result Examples]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: deer (4)\n",
"Image #1: bird (2)\n",
"Image #1: automobile (1)\n",
"Image #1: automobile (1)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: dog (5)\n",
"Image #1: deer (4)\n",
"Image #1: ship (8)\n",
"Image #1: ship (8)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: truck (9)\n",
"Image #1: horse (7)\n",
"Image #1: cat (3)\n",
"Image #1: airplane (0)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: automobile (1)\n",
"Image #1: deer (4)\n",
"Image #1: bird (2)\n",
"Image #1: bird (2)\n",
"[Step #0] Loss: 1.1839 Accuracy: 0.0000% Time elapsed: 0.9554s (total 64 images)\n",
"[Step #10] Loss: 1.1868 Accuracy: 0.0000% Time elapsed: 5.8784s (total 704 images)\n",
"[Step #20] Loss: 1.1833 Accuracy: 0.0000% Time elapsed: 10.7975s (total 1344 images)\n",
"[Step #30] Loss: 1.1849 Accuracy: 0.0000% Time elapsed: 15.7160s (total 1984 images)\n",
"[Step #40] Loss: 1.1831 Accuracy: 0.0000% Time elapsed: 20.6380s (total 2624 images)\n",
"[Step #50] Loss: 1.1840 Accuracy: 0.0000% Time elapsed: 25.5607s (total 3264 images)\n",
"[Step #60] Loss: 1.1842 Accuracy: 0.0000% Time elapsed: 30.4954s (total 3904 images)\n",
"[Step #70] Loss: 1.1845 Accuracy: 0.0000% Time elapsed: 35.4290s (total 4544 images)\n",
"[Step #80] Loss: 1.1853 Accuracy: 0.0000% Time elapsed: 40.3608s (total 5184 images)\n",
"[Step #90] Loss: 1.1848 Accuracy: 0.0000% Time elapsed: 45.2943s (total 5824 images)\n",
"[Step #100] Loss: 1.1856 Accuracy: 0.0000% Time elapsed: 50.2288s (total 6464 images)\n",
"[Step #110] Loss: 1.1851 Accuracy: 0.0000% Time elapsed: 55.1659s (total 7104 images)\n",
"[Step #120] Loss: 1.1849 Accuracy: 0.0000% Time elapsed: 60.0931s (total 7744 images)\n",
"[Step #130] Loss: 1.1853 Accuracy: 0.0000% Time elapsed: 65.0266s (total 8384 images)\n",
"[Step #140] Loss: 1.1857 Accuracy: 0.0000% Time elapsed: 69.9528s (total 9024 images)\n",
"[Step #150] Loss: 1.1853 Accuracy: 0.0000% Time elapsed: 74.8816s (total 9664 images)\n",
"[Validation] Loss: 1.1905 Accuracy: 0.0000% Time elapsed: 77.5587s (total 10000 images)\n",
"[Size of Perturbation]\n",
"Average L0 distance (the number of changed parameters): 3001.5342\n",
"Average L2 distance: 3.0243445865631102\n",
"Average MSE: 0.0029820813957601787\n",
"Average Linf distance (the maximum changed values): 0.0627451241016388\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vkq3_S_B6bRC"
},
"source": [
"* Black-box attack"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "SXAljmM9x2Ts",
"outputId": "31c10c97-05a2-4bc0-b661-f524e5b2f0fc"
},
"source": [
"running_loss = 0.\n",
"running_corrects = 0\n",
"running_length = 0\n",
"\n",
"running_l0 = 0\n",
"running_l2 = 0\n",
"running_mse = 0\n",
"running_linf = 0\n",
"\n",
"start_time = time.time()\n",
"\n",
"for i, (inputs, labels) in enumerate(test_dataloader):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" adv_untargeted, perturbation = mi_fgsm_attack(model, inputs, labels, False, eps, alpha, iters, decay=1.0) # adversarial attack\n",
"\n",
" outputs = black_box_model(adv_untargeted)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" running_loss += loss.item()\n",
" running_corrects += torch.sum(preds == labels.data)\n",
" running_length += labels.shape[0]\n",
"\n",
" l0, l2, mse, linf = get_distance(adv_untargeted, inputs)\n",
" running_l0 += l0.sum().item()\n",
" running_l2 += l2.sum().item()\n",
" running_mse += mse.sum().item()\n",
" running_linf += linf.sum().item()\n",
"\n",
" if i == 0:\n",
" print('The dimension of an image tensor:', inputs.shape[1:])\n",
" print('[Prediction Result Examples]')\n",
" images = torchvision.utils.make_grid(adv_untargeted[:4])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[:4]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[:4]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[:4]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" images = torchvision.utils.make_grid(adv_untargeted[4:8])\n",
" imshow_batch(images.cpu(), title='original labels:' + str([int(x) for x in labels[4:8]]) +\n",
" '\\npredicted labels:' + str([int(x) for x in preds[4:8]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[4:8]):\n",
" print(f'Image #{i + 1}: {class_names[label]} ({label})')\n",
"\n",
" if i % 10 == 0:\n",
" cur_running_loss = running_loss / running_length\n",
" running_acc = running_corrects / running_length * 100.\n",
" print('[Step #{}] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(i, cur_running_loss, running_acc, time.time() - start_time, running_length))\n",
"\n",
"epoch_loss = running_loss / len(test_dataset)\n",
"epoch_acc = running_corrects / len(test_dataset) * 100.\n",
"print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, time.time() - start_time, len(test_dataset)))\n",
"\n",
"print('[Size of Perturbation]')\n",
"print('Average L0 distance (the number of changed parameters):', running_l0 / len(test_dataset))\n",
"print('Average L2 distance:', running_l2 / len(test_dataset))\n",
"print('Average MSE:', running_mse / len(test_dataset))\n",
"print('Average Linf distance (the maximum changed values):', running_linf / len(test_dataset))"
],
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"text": [
"The dimension of an image tensor: torch.Size([3, 32, 32])\n",
"[Prediction Result Examples]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: deer (4)\n",
"Image #1: cat (3)\n",
"Image #1: horse (7)\n",
"Image #1: deer (4)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: deer (4)\n",
"Image #1: horse (7)\n",
"Image #1: deer (4)\n",
"Image #1: bird (2)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: bird (2)\n",
"Image #1: airplane (0)\n",
"Image #1: bird (2)\n",
"Image #1: truck (9)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: deer (4)\n",
"Image #1: bird (2)\n",
"Image #1: cat (3)\n",
"Image #1: truck (9)\n",
"[Step #0] Loss: 0.0692 Accuracy: 42.1875% Time elapsed: 0.8748s (total 64 images)\n",
"[Step #10] Loss: 0.0562 Accuracy: 45.8807% Time elapsed: 5.8483s (total 704 images)\n",
"[Step #20] Loss: 0.0545 Accuracy: 48.2887% Time elapsed: 10.8243s (total 1344 images)\n",
"[Step #30] Loss: 0.0518 Accuracy: 49.6472% Time elapsed: 15.7997s (total 1984 images)\n",
"[Step #40] Loss: 0.0508 Accuracy: 50.4192% Time elapsed: 20.7719s (total 2624 images)\n",
"[Step #50] Loss: 0.0507 Accuracy: 50.3064% Time elapsed: 25.7451s (total 3264 images)\n",
"[Step #60] Loss: 0.0511 Accuracy: 50.2561% Time elapsed: 30.7187s (total 3904 images)\n",
"[Step #70] Loss: 0.0507 Accuracy: 49.8900% Time elapsed: 35.6910s (total 4544 images)\n",
"[Step #80] Loss: 0.0501 Accuracy: 50.1736% Time elapsed: 40.6741s (total 5184 images)\n",
"[Step #90] Loss: 0.0501 Accuracy: 50.2060% Time elapsed: 45.6554s (total 5824 images)\n",
"[Step #100] Loss: 0.0499 Accuracy: 50.1856% Time elapsed: 50.6352s (total 6464 images)\n",
"[Step #110] Loss: 0.0495 Accuracy: 50.0282% Time elapsed: 55.6239s (total 7104 images)\n",
"[Step #120] Loss: 0.0493 Accuracy: 50.1291% Time elapsed: 60.6153s (total 7744 images)\n",
"[Step #130] Loss: 0.0494 Accuracy: 50.0358% Time elapsed: 65.6027s (total 8384 images)\n",
"[Step #140] Loss: 0.0495 Accuracy: 50.0332% Time elapsed: 70.5909s (total 9024 images)\n",
"[Step #150] Loss: 0.0496 Accuracy: 49.9897% Time elapsed: 75.5741s (total 9664 images)\n",
"[Validation] Loss: 0.0498 Accuracy: 49.9400% Time elapsed: 78.2865s (total 10000 images)\n",
"[Size of Perturbation]\n",
"Average L0 distance (the number of changed parameters): 3001.4756\n",
"Average L2 distance: 3.0242431716918947\n",
"Average MSE: 0.002981893261149526\n",
"Average Linf distance (the maximum changed values): 0.0627451241016388\n"
],
"name": "stdout"
}
]
}
]
}