Computer_Vision/Augmentation/CutMix data augmentation for image classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69e124e8",
   "metadata": {},
   "source": [
    "Albumentations: fast and flexible image augmentations\n",
    "\n",
    "- [Demo](https://albumentations-demo.herokuapp.com/)\n",
    "- [Paper](https://arxiv.org/abs/1809.06839)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b6108c09",
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import torch\n",
    "import json\n",
    "import torchvision\n",
    "import numpy as np\n",
    "from models.resnet import ResNet18\n",
    "\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4820e2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Config:\n",
    "    datadir            = '../data/'\n",
    "    gpu_id             = 0\n",
    "    lr                 = 0.1\n",
    "    epochs             = 100\n",
    "    batch_size         = 128\n",
    "    accumulation_steps = 1\n",
    "    num_workers        = 8\n",
    "    seed               = 223\n",
    "    # CutMix \n",
    "    beta               = 1\n",
    "    cutmix_prob        = 0.5\n",
    "    \n",
    "config = Config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce79718d",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = f'cuda:{config.gpu_id}' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fe7a19f",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9946b16f",
   "metadata": {},
   "outputs": [],
   "source": [
    "transform_train = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.RandomCrop(32, padding=4),\n",
    "    torchvision.transforms.RandomHorizontalFlip(),\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "])\n",
    "\n",
    "transform_test = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "631fac48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "trainset = torchvision.datasets.CIFAR10(root=config.datadir, \n",
    "                                         train=True, \n",
    "                                         download=True,\n",
    "                                         transform=transform_train)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root=config.datadir, \n",
    "                                        train=False, \n",
    "                                        download=True,\n",
    "                                        transform=transform_test)\n",
    "\n",
    "trainloader = torch.utils.data.DataLoader(trainset, \n",
    "                                          batch_size=config.batch_size, \n",
    "                                          shuffle=True, \n",
    "                                          num_workers=config.num_workers)\n",
    "\n",
    "testloader = torch.utils.data.DataLoader(testset, \n",
    "                                         batch_size=config.batch_size, \n",
    "                                         shuffle=False, \n",
    "                                         num_workers=config.num_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d7d7cb1",
   "metadata": {},
   "source": [
    "## CutMix Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce245e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs, targets = next(iter(trainloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c86700df",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rand_bbox(size, lam):\n",
    "    W = size[2]\n",
    "    H = size[3]\n",
    "    cut_rat = np.sqrt(1. - lam)\n",
    "    cut_w = np.int(W * cut_rat)\n",
    "    cut_h = np.int(H * cut_rat)\n",
    "\n",
    "    # uniform\n",
    "    cx = np.random.randint(W)\n",
    "    cy = np.random.randint(H)\n",
    "\n",
    "    bbx1 = np.clip(cx - cut_w // 2, 0, W)\n",
    "    bby1 = np.clip(cy - cut_h // 2, 0, H)\n",
    "    bbx2 = np.clip(cx + cut_w // 2, 0, W)\n",
    "    bby2 = np.clip(cy + cut_h // 2, 0, H)\n",
    "\n",
    "    return bbx1, bby1, bbx2, bby2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd6d49aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cutmix(inputs, targets, beta, device):\n",
    "    # generate mixed sample\n",
    "    lam = np.random.beta(config.beta, config.beta)\n",
    "    rand_index = torch.randperm(inputs.size()[0]).to(device)\n",
    "    \n",
    "    targets_a = targets\n",
    "    targets_b = targets[rand_index]\n",
    "    \n",
    "    bbx1, bby1, bbx2, bby2 = rand_bbox(inputs.size(), lam)\n",
    "    inputs[:, :, bbx1:bbx2, bby1:bby2] = inputs[rand_index, :, bbx1:bbx2, bby1:bby2]\n",
    "    \n",
    "    # adjust lambda to exactly match pixel ratio\n",
    "    lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (inputs.size()[-1] * inputs.size()[-2]))\n",
    "    \n",
    "    return inputs, targets_a, targets_b, lam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ebf72f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp, targets_a, targets_b, lam = cutmix(inputs, \n",
    "                                         targets, \n",
    "                                         config.beta, \n",
    "                                         device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "20dd8c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = np.transpose(inputs.numpy(), axes=[0,2,3,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95ac0c54",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd8407c0910>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.imshow(np.hstack(temp[:4]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a0cd01",
   "metadata": {},
   "source": [
    "# Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d568132",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ResNet18().to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0073583",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "777ff046",
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=config.lr)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bced5195",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(\n",
    "    model, dataloader, criterion, optimizer, accumulation_steps, beta, cutmix_prob, device='cpu'\n",
    "):\n",
    "    correct = 0 \n",
    "    total = 0\n",
    "    total_loss = 0\n",
    "    \n",
    "    optimizer.zero_grad()\n",
    "    for idx, (inputs, targets) in enumerate(dataloader):\n",
    "        inputs, targets = inputs.to(device), targets.to(device)\n",
    "        \n",
    "        \n",
    "        r = np.random.rand(1)\n",
    "        if beta > 0 and r < cutmix_prob:\n",
    "            inputs, targets_a, targets_b, lam = cutmix(inputs,\n",
    "                                                       targets,\n",
    "                                                       beta,\n",
    "                                                       device)\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, targets_a) * lam + criterion(outputs, targets_b) * (1. - lam)\n",
    "        else:\n",
    "            # predict\n",
    "            outputs = model(inputs)\n",
    "\n",
    "            # loss and update\n",
    "            loss = criterion(outputs, targets)\n",
    "            \n",
    "        # auccumulation steps\n",
    "        (loss / accumulation_steps).backward()\n",
    "        \n",
    "        if (idx + 1) % accumulation_steps == 0:\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "        \n",
    "        # total loss and acc\n",
    "        total_loss += loss.item()\n",
    "        \n",
    "        preds = outputs.argmax(dim=1) \n",
    "        correct += targets.eq(preds).sum().item()\n",
    "        total += targets.size(0)\n",
    "        \n",
    "        if idx == (len(dataloader)-1):\n",
    "            print('[%d/%d]: Loss: %.3f | Acc: %.3f%% [%d/%d]' % \n",
    "                  (idx+1, len(dataloader), total_loss/(idx+1), 100.*correct/total, correct, total),end='\\n')\n",
    "        else:\n",
    "            print('[%d/%d]: Loss: %.3f | Acc: %.3f%% [%d/%d]' % \n",
    "                  (idx+1, len(dataloader), total_loss/(idx+1), 100.*correct/total, correct, total),end='\\r')\n",
    "        \n",
    "    return 100.*correct/total, total_loss/len(dataloader)\n",
    "        \n",
    "def test(model, dataloader, criterion, device='cpu'):\n",
    "    correct = 0\n",
    "    correct_top5 = 0\n",
    "    total = 0\n",
    "    total_loss = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for idx, (inputs, targets) in enumerate(dataloader):\n",
    "            inputs, targets = inputs.to(device), targets.to(device)\n",
    "            \n",
    "            # predict\n",
    "            outputs = model(inputs)\n",
    "            \n",
    "            # loss \n",
    "            loss = criterion(outputs, targets)\n",
    "            \n",
    "            # total loss and acc\n",
    "            total_loss += loss.item()\n",
    "            preds = outputs.argmax(dim=1)\n",
    "            _, preds_top5 = outputs.topk(5, dim=1)\n",
    "            \n",
    "            correct += targets.eq(preds).sum().item()\n",
    "            correct_top5 += preds_top5.eq(targets.view(-1, 1).expand_as(preds_top5)).sum().item()\n",
    "            total += targets.size(0)\n",
    "                        \n",
    "            if idx == (len(dataloader)-1):\n",
    "                print('[%d/%d]: Loss: %.3f | Top-1 Acc: %.3f%% [%d/%d] | Top-5 Acc: %.3f%% [%d/%d]' % \n",
    "                      (idx+1, len(dataloader), total_loss/(idx+1), \n",
    "                       100.*correct/total, correct, total,\n",
    "                       100.*correct_top5/total, correct_top5, total),end='\\n')\n",
    "            else:\n",
    "                print('[%d/%d]: Loss: %.3f | Top-1 Acc: %.3f%% [%d/%d] | Top-5 Acc: %.3f%% [%d/%d]' % \n",
    "                      (idx+1, len(dataloader), total_loss/(idx+1), \n",
    "                       100.*correct/total, correct, total,\n",
    "                       100.*correct_top5/total, correct_top5, total),end='\\r')\n",
    "                \n",
    "        return 100.*correct/total, 100.*correct_top5/total, total_loss/len(dataloader)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70197c3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/100\n",
      "[391/391]: Loss: 1.823 | Acc: 35.196% [17598/50000]\n",
      "[79/79]: Loss: 1.588 | Top-1 Acc: 45.000% [4500/10000] | Top-5 Acc: 91.150% [9115/10000]\n",
      "Epoch: 2/100\n",
      "[391/391]: Loss: 1.451 | Acc: 50.594% [25297/50000]\n",
      "[79/79]: Loss: 1.200 | Top-1 Acc: 57.710% [5771/10000] | Top-5 Acc: 95.450% [9545/10000]\n",
      "Epoch: 3/100\n",
      "[391/391]: Loss: 1.289 | Acc: 57.110% [28555/50000]\n",
      "[79/79]: Loss: 0.979 | Top-1 Acc: 66.720% [6672/10000] | Top-5 Acc: 96.800% [9680/10000]\n",
      "Epoch: 4/100\n",
      "[391/391]: Loss: 1.169 | Acc: 62.012% [31006/50000]\n",
      "[79/79]: Loss: 0.810 | Top-1 Acc: 73.520% [7352/10000] | Top-5 Acc: 98.070% [9807/10000]\n",
      "Epoch: 5/100\n",
      "[391/391]: Loss: 1.102 | Acc: 64.766% [32383/50000]\n",
      "[79/79]: Loss: 0.732 | Top-1 Acc: 76.030% [7603/10000] | Top-5 Acc: 98.240% [9824/10000]\n",
      "Epoch: 6/100\n",
      "[391/391]: Loss: 1.042 | Acc: 67.508% [33754/50000]\n",
      "[79/79]: Loss: 0.763 | Top-1 Acc: 74.480% [7448/10000] | Top-5 Acc: 98.100% [9810/10000]\n",
      "Epoch: 7/100\n",
      "[391/391]: Loss: 0.959 | Acc: 69.654% [34827/50000]\n",
      "[79/79]: Loss: 0.641 | Top-1 Acc: 78.900% [7890/10000] | Top-5 Acc: 98.480% [9848/10000]\n",
      "Epoch: 8/100\n",
      "[391/391]: Loss: 0.974 | Acc: 69.752% [34876/50000]\n",
      "[79/79]: Loss: 0.647 | Top-1 Acc: 79.810% [7981/10000] | Top-5 Acc: 98.710% [9871/10000]\n",
      "Epoch: 9/100\n",
      "[391/391]: Loss: 0.911 | Acc: 71.430% [35715/50000]\n",
      "[79/79]: Loss: 0.783 | Top-1 Acc: 74.880% [7488/10000] | Top-5 Acc: 98.120% [9812/10000]\n",
      "Epoch: 10/100\n",
      "[391/391]: Loss: 0.874 | Acc: 72.842% [36421/50000]\n",
      "[79/79]: Loss: 0.620 | Top-1 Acc: 80.380% [8038/10000] | Top-5 Acc: 98.820% [9882/10000]\n",
      "Epoch: 11/100\n",
      "[391/391]: Loss: 0.868 | Acc: 74.118% [37059/50000]\n",
      "[79/79]: Loss: 0.520 | Top-1 Acc: 83.190% [8319/10000] | Top-5 Acc: 99.050% [9905/10000]\n",
      "Epoch: 12/100\n",
      "[391/391]: Loss: 0.849 | Acc: 73.830% [36915/50000]\n",
      "[79/79]: Loss: 0.484 | Top-1 Acc: 84.340% [8434/10000] | Top-5 Acc: 99.180% [9918/10000]\n",
      "Epoch: 13/100\n",
      "[391/391]: Loss: 0.796 | Acc: 76.030% [38015/50000]\n",
      "[79/79]: Loss: 0.471 | Top-1 Acc: 84.600% [8460/10000] | Top-5 Acc: 99.250% [9925/10000]\n",
      "Epoch: 14/100\n",
      "[391/391]: Loss: 0.791 | Acc: 75.408% [37704/50000]\n",
      "[79/79]: Loss: 0.459 | Top-1 Acc: 84.840% [8484/10000] | Top-5 Acc: 99.230% [9923/10000]\n",
      "Epoch: 15/100\n",
      "[391/391]: Loss: 0.792 | Acc: 77.034% [38517/50000]\n",
      "[79/79]: Loss: 0.461 | Top-1 Acc: 85.050% [8505/10000] | Top-5 Acc: 99.130% [9913/10000]\n",
      "Epoch: 16/100\n",
      "[391/391]: Loss: 0.733 | Acc: 78.596% [39298/50000]\n",
      "[79/79]: Loss: 0.426 | Top-1 Acc: 85.960% [8596/10000] | Top-5 Acc: 99.270% [9927/10000]\n",
      "Epoch: 17/100\n",
      "[391/391]: Loss: 0.759 | Acc: 77.326% [38663/50000]\n",
      "[79/79]: Loss: 0.431 | Top-1 Acc: 86.370% [8637/10000] | Top-5 Acc: 99.420% [9942/10000]\n",
      "Epoch: 18/100\n",
      "[391/391]: Loss: 0.776 | Acc: 76.604% [38302/50000]\n",
      "[79/79]: Loss: 0.418 | Top-1 Acc: 86.570% [8657/10000] | Top-5 Acc: 99.350% [9935/10000]\n",
      "Epoch: 19/100\n",
      "[391/391]: Loss: 0.745 | Acc: 77.242% [38621/50000]\n",
      "[79/79]: Loss: 0.440 | Top-1 Acc: 85.540% [8554/10000] | Top-5 Acc: 99.370% [9937/10000]\n",
      "Epoch: 20/100\n",
      "[391/391]: Loss: 0.747 | Acc: 77.324% [38662/50000]\n",
      "[79/79]: Loss: 0.422 | Top-1 Acc: 85.920% [8592/10000] | Top-5 Acc: 99.270% [9927/10000]\n",
      "Epoch: 21/100\n",
      "[391/391]: Loss: 0.693 | Acc: 79.842% [39921/50000]\n",
      "[79/79]: Loss: 0.452 | Top-1 Acc: 86.040% [8604/10000] | Top-5 Acc: 99.430% [9943/10000]\n",
      "Epoch: 22/100\n",
      "[391/391]: Loss: 0.701 | Acc: 78.284% [39142/50000]\n",
      "[79/79]: Loss: 0.423 | Top-1 Acc: 86.590% [8659/10000] | Top-5 Acc: 99.460% [9946/10000]\n",
      "Epoch: 23/100\n",
      "[391/391]: Loss: 0.679 | Acc: 79.652% [39826/50000]\n",
      "[79/79]: Loss: 0.401 | Top-1 Acc: 87.270% [8727/10000] | Top-5 Acc: 99.240% [9924/10000]\n",
      "Epoch: 24/100\n",
      "[391/391]: Loss: 0.738 | Acc: 77.662% [38831/50000]\n",
      "[79/79]: Loss: 0.468 | Top-1 Acc: 86.260% [8626/10000] | Top-5 Acc: 99.390% [9939/10000]\n",
      "Epoch: 25/100\n",
      "[391/391]: Loss: 0.704 | Acc: 79.960% [39980/50000]\n",
      "[79/79]: Loss: 0.399 | Top-1 Acc: 87.730% [8773/10000] | Top-5 Acc: 99.440% [9944/10000]\n",
      "Epoch: 26/100\n",
      "[391/391]: Loss: 0.712 | Acc: 79.588% [39794/50000]\n",
      "[79/79]: Loss: 0.393 | Top-1 Acc: 87.150% [8715/10000] | Top-5 Acc: 99.500% [9950/10000]\n",
      "Epoch: 27/100\n",
      "[391/391]: Loss: 0.661 | Acc: 80.500% [40250/50000]\n",
      "[79/79]: Loss: 0.404 | Top-1 Acc: 87.610% [8761/10000] | Top-5 Acc: 99.500% [9950/10000]\n",
      "Epoch: 28/100\n",
      "[391/391]: Loss: 0.633 | Acc: 81.420% [40710/50000]\n",
      "[79/79]: Loss: 0.374 | Top-1 Acc: 88.350% [8835/10000] | Top-5 Acc: 99.580% [9958/10000]\n",
      "Epoch: 29/100\n",
      "[391/391]: Loss: 0.677 | Acc: 79.362% [39681/50000]\n",
      "[79/79]: Loss: 0.352 | Top-1 Acc: 89.350% [8935/10000] | Top-5 Acc: 99.560% [9956/10000]\n",
      "Epoch: 30/100\n",
      "[391/391]: Loss: 0.588 | Acc: 82.116% [41058/50000]\n",
      "[79/79]: Loss: 0.365 | Top-1 Acc: 88.260% [8826/10000] | Top-5 Acc: 99.520% [9952/10000]\n",
      "Epoch: 31/100\n",
      "[391/391]: Loss: 0.692 | Acc: 80.002% [40001/50000]\n",
      "[79/79]: Loss: 0.362 | Top-1 Acc: 89.190% [8919/10000] | Top-5 Acc: 99.390% [9939/10000]\n",
      "Epoch: 32/100\n",
      "[391/391]: Loss: 0.638 | Acc: 81.466% [40733/50000]\n",
      "[79/79]: Loss: 0.369 | Top-1 Acc: 89.020% [8902/10000] | Top-5 Acc: 99.600% [9960/10000]\n",
      "Epoch: 33/100\n",
      "[391/391]: Loss: 0.607 | Acc: 82.318% [41159/50000]\n",
      "[79/79]: Loss: 0.348 | Top-1 Acc: 89.490% [8949/10000] | Top-5 Acc: 99.590% [9959/10000]\n",
      "Epoch: 34/100\n",
      "[391/391]: Loss: 0.625 | Acc: 80.198% [40099/50000]\n",
      "[79/79]: Loss: 0.358 | Top-1 Acc: 89.200% [8920/10000] | Top-5 Acc: 99.550% [9955/10000]\n",
      "Epoch: 35/100\n",
      "[391/391]: Loss: 0.666 | Acc: 80.296% [40148/50000]\n",
      "[79/79]: Loss: 0.345 | Top-1 Acc: 89.750% [8975/10000] | Top-5 Acc: 99.550% [9955/10000]\n",
      "Epoch: 36/100\n",
      "[391/391]: Loss: 0.591 | Acc: 82.166% [41083/50000]\n",
      "[79/79]: Loss: 0.351 | Top-1 Acc: 89.920% [8992/10000] | Top-5 Acc: 99.580% [9958/10000]\n",
      "Epoch: 37/100\n",
      "[391/391]: Loss: 0.555 | Acc: 83.232% [41616/50000]\n",
      "[79/79]: Loss: 0.368 | Top-1 Acc: 89.520% [8952/10000] | Top-5 Acc: 99.600% [9960/10000]\n",
      "Epoch: 38/100\n",
      "[391/391]: Loss: 0.617 | Acc: 82.610% [41305/50000]\n",
      "[79/79]: Loss: 0.390 | Top-1 Acc: 88.660% [8866/10000] | Top-5 Acc: 99.670% [9967/10000]\n",
      "Epoch: 39/100\n",
      "[391/391]: Loss: 0.550 | Acc: 83.188% [41594/50000]\n",
      "[79/79]: Loss: 0.378 | Top-1 Acc: 89.500% [8950/10000] | Top-5 Acc: 99.590% [9959/10000]\n",
      "Epoch: 40/100\n",
      "[391/391]: Loss: 0.561 | Acc: 83.446% [41723/50000]\n",
      "[79/79]: Loss: 0.362 | Top-1 Acc: 89.520% [8952/10000] | Top-5 Acc: 99.550% [9955/10000]\n",
      "Epoch: 41/100\n",
      "[391/391]: Loss: 0.571 | Acc: 81.440% [40720/50000]\n",
      "[79/79]: Loss: 0.345 | Top-1 Acc: 90.350% [9035/10000] | Top-5 Acc: 99.650% [9965/10000]\n",
      "Epoch: 42/100\n",
      "[391/391]: Loss: 0.542 | Acc: 84.680% [42340/50000]\n",
      "[79/79]: Loss: 0.335 | Top-1 Acc: 90.220% [9022/10000] | Top-5 Acc: 99.620% [9962/10000]\n",
      "Epoch: 43/100\n",
      "[391/391]: Loss: 0.588 | Acc: 82.590% [41295/50000]\n",
      "[79/79]: Loss: 0.362 | Top-1 Acc: 89.950% [8995/10000] | Top-5 Acc: 99.610% [9961/10000]\n",
      "Epoch: 44/100\n",
      "[391/391]: Loss: 0.556 | Acc: 83.102% [41551/50000]\n",
      "[79/79]: Loss: 0.346 | Top-1 Acc: 90.650% [9065/10000] | Top-5 Acc: 99.610% [9961/10000]\n",
      "Epoch: 45/100\n",
      "[391/391]: Loss: 0.580 | Acc: 82.872% [41436/50000]\n",
      "[79/79]: Loss: 0.326 | Top-1 Acc: 89.890% [8989/10000] | Top-5 Acc: 99.490% [9949/10000]\n",
      "Epoch: 46/100\n",
      "[391/391]: Loss: 0.588 | Acc: 83.356% [41678/50000]\n",
      "[79/79]: Loss: 0.312 | Top-1 Acc: 91.090% [9109/10000] | Top-5 Acc: 99.620% [9962/10000]\n",
      "Epoch: 47/100\n",
      "[391/391]: Loss: 0.538 | Acc: 83.420% [41710/50000]\n",
      "[79/79]: Loss: 0.340 | Top-1 Acc: 90.440% [9044/10000] | Top-5 Acc: 99.620% [9962/10000]\n",
      "Epoch: 48/100\n",
      "[391/391]: Loss: 0.556 | Acc: 83.918% [41959/50000]\n",
      "[79/79]: Loss: 0.323 | Top-1 Acc: 91.020% [9102/10000] | Top-5 Acc: 99.610% [9961/10000]\n",
      "Epoch: 49/100\n",
      "[391/391]: Loss: 0.564 | Acc: 82.588% [41294/50000]\n",
      "[79/79]: Loss: 0.344 | Top-1 Acc: 91.010% [9101/10000] | Top-5 Acc: 99.570% [9957/10000]\n",
      "Epoch: 50/100\n",
      "[391/391]: Loss: 0.535 | Acc: 84.768% [42384/50000]\n",
      "[79/79]: Loss: 0.341 | Top-1 Acc: 90.250% [9025/10000] | Top-5 Acc: 99.550% [9955/10000]\n",
      "Epoch: 51/100\n",
      "[391/391]: Loss: 0.538 | Acc: 84.032% [42016/50000]\n",
      "[79/79]: Loss: 0.315 | Top-1 Acc: 91.220% [9122/10000] | Top-5 Acc: 99.590% [9959/10000]\n",
      "Epoch: 52/100\n",
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      "[79/79]: Loss: 0.344 | Top-1 Acc: 90.110% [9011/10000] | Top-5 Acc: 99.610% [9961/10000]\n",
      "Epoch: 53/100\n",
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      "[79/79]: Loss: 0.347 | Top-1 Acc: 90.580% [9058/10000] | Top-5 Acc: 99.580% [9958/10000]\n",
      "Epoch: 54/100\n",
      "[391/391]: Loss: 0.520 | Acc: 85.814% [42907/50000]\n",
      "[79/79]: Loss: 0.338 | Top-1 Acc: 90.350% [9035/10000] | Top-5 Acc: 99.560% [9956/10000]\n",
      "Epoch: 55/100\n",
      "[391/391]: Loss: 0.484 | Acc: 85.780% [42890/50000]\n",
      "[79/79]: Loss: 0.357 | Top-1 Acc: 90.910% [9091/10000] | Top-5 Acc: 99.510% [9951/10000]\n",
      "Epoch: 56/100\n",
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      "[79/79]: Loss: 0.337 | Top-1 Acc: 91.210% [9121/10000] | Top-5 Acc: 99.650% [9965/10000]\n",
      "Epoch: 57/100\n",
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      "Epoch: 58/100\n",
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      "[79/79]: Loss: 0.372 | Top-1 Acc: 90.540% [9054/10000] | Top-5 Acc: 99.550% [9955/10000]\n",
      "Epoch: 59/100\n",
      "[391/391]: Loss: 0.501 | Acc: 85.394% [42697/50000]\n",
      "[79/79]: Loss: 0.347 | Top-1 Acc: 90.960% [9096/10000] | Top-5 Acc: 99.620% [9962/10000]\n",
      "Epoch: 60/100\n",
      "[391/391]: Loss: 0.501 | Acc: 85.734% [42867/50000]\n",
      "[79/79]: Loss: 0.351 | Top-1 Acc: 90.810% [9081/10000] | Top-5 Acc: 99.560% [9956/10000]\n",
      "Epoch: 61/100\n",
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      "[79/79]: Loss: 0.333 | Top-1 Acc: 91.130% [9113/10000] | Top-5 Acc: 99.610% [9961/10000]\n",
      "Epoch: 62/100\n",
      "[391/391]: Loss: 0.458 | Acc: 87.350% [43675/50000]\n",
      "[79/79]: Loss: 0.320 | Top-1 Acc: 91.270% [9127/10000] | Top-5 Acc: 99.590% [9959/10000]\n",
      "Epoch: 63/100\n",
      "[391/391]: Loss: 0.479 | Acc: 85.842% [42921/50000]\n",
      "[79/79]: Loss: 0.344 | Top-1 Acc: 91.130% [9113/10000] | Top-5 Acc: 99.600% [9960/10000]\n",
      "Epoch: 64/100\n",
      "[391/391]: Loss: 0.478 | Acc: 86.940% [43470/50000]\n",
      "[79/79]: Loss: 0.352 | Top-1 Acc: 90.420% [9042/10000] | Top-5 Acc: 99.640% [9964/10000]\n",
      "Epoch: 65/100\n",
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      "[79/79]: Loss: 0.321 | Top-1 Acc: 91.570% [9157/10000] | Top-5 Acc: 99.640% [9964/10000]\n",
      "Epoch: 66/100\n",
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      "[79/79]: Loss: 0.347 | Top-1 Acc: 91.360% [9136/10000] | Top-5 Acc: 99.570% [9957/10000]\n",
      "Epoch: 67/100\n",
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      "Epoch: 68/100\n",
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      "[79/79]: Loss: 0.334 | Top-1 Acc: 91.330% [9133/10000] | Top-5 Acc: 99.560% [9956/10000]\n",
      "Epoch: 69/100\n",
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      "Epoch: 70/100\n",
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      "[79/79]: Loss: 0.319 | Top-1 Acc: 91.770% [9177/10000] | Top-5 Acc: 99.560% [9956/10000]\n",
      "Epoch: 71/100\n",
      "[391/391]: Loss: 0.508 | Acc: 85.398% [42699/50000]\n",
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      "Epoch: 72/100\n",
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      "[79/79]: Loss: 0.321 | Top-1 Acc: 91.620% [9162/10000] | Top-5 Acc: 99.630% [9963/10000]\n",
      "Epoch: 73/100\n",
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      "Epoch: 74/100\n",
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      "Epoch: 75/100\n",
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      "Epoch: 76/100\n",
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      "Epoch: 77/100\n",
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      "Epoch: 78/100\n",
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      "Epoch: 79/100\n",
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      "Epoch: 80/100\n",
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      "Epoch: 81/100\n",
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      "Epoch: 82/100\n",
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      "Epoch: 83/100\n",
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      "Epoch: 84/100\n",
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      "Epoch: 85/100\n",
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      "[79/79]: Loss: 0.307 | Top-1 Acc: 92.200% [9220/10000] | Top-5 Acc: 99.650% [9965/10000]\n",
      "Epoch: 86/100\n",
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      "Epoch: 87/100\n",
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      "Epoch: 88/100\n",
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      "Epoch: 89/100\n",
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      "Epoch: 90/100\n",
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      "Epoch: 91/100\n",
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      "Epoch: 92/100\n",
      "[391/391]: Loss: 0.508 | Acc: 86.604% [43302/50000]\n",
      "[79/79]: Loss: 0.290 | Top-1 Acc: 92.510% [9251/10000] | Top-5 Acc: 99.680% [9968/10000]\n",
      "Epoch: 93/100\n",
      "[156/391]: Loss: 0.476 | Acc: 84.971% [16967/19968]\r"
     ]
    }
   ],
   "source": [
    "# history\n",
    "history = {\n",
    "    'train':{\n",
    "        'top1-acc':[],\n",
    "        'loss':[]\n",
    "    },\n",
    "    'test':{\n",
    "        'top1-acc':[],\n",
    "        'top5-acc':[],\n",
    "        'loss':[]\n",
    "    }\n",
    "}\n",
    "\n",
    "for epoch in range(config.epochs):\n",
    "    print(f'Epoch: {epoch+1}/{config.epochs}')\n",
    "    train_top1acc, train_loss = train(model, trainloader, criterion, optimizer, config.accumulation_steps, config.beta, config.cutmix_prob, device=device)\n",
    "    test_top1acc, test_top5acc, test_loss = test(model, testloader, criterion, device)\n",
    "    \n",
    "    scheduler.step()\n",
    "    \n",
    "    # history\n",
    "    history['train']['top1-acc'].append(train_top1acc)\n",
    "    history['train']['loss'].append(train_loss)\n",
    "    history['test']['top1-acc'].append(test_top1acc)\n",
    "    history['test']['top5-acc'].append(test_top5acc)\n",
    "    history['test']['loss'].append(test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c4528d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "weights = {'model':model.state_dict()}\n",
    "torch.save(weights, 'ResNet18_cutmix05_beta1_epochs100_lr01.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "83b574df",
   "metadata": {},
   "outputs": [],
   "source": [
    "json.dump(history,open('ResNet18_cutmix05_beta1_epochs100_lr01.json','w'),indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8653fd2a",
   "metadata": {},
   "source": [
    "# History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7317ea68",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd8425a3520>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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50vg9NAFm3A2TbzQCoK2fG10jZ/7OGNsLx0F9BbidgIZ+o43sWXiKUdqIMrb3nwFjLzOyW5nfw6L/Gq/vtH8aZZlvnQ8n/B+sexuCo+HSN43nDkuE856B5Akt4/v697DiRQj2ZDhu/AGiW+b0ArDkKSMTp0zQfzpc+3nb11i+x8jChfWDyP7GPLom9irPv/3R+Y66N1yf5NrUwuFy0+B0U213cMNrK9lTWsvfzxtFYZWdrzcWsCW/igGxIfz3snGMS40kq6SWEx+Zz71nDOOW4wdid7h4d8U+npu/qzkASYywcfHEFC6ckEJGbPvzPHcW1fDEj5ks213Km7+YwvDE8Hb3A6NR4AXPLmZUcgQbcir545nDuem4AQfs1+h08/n6PBbsKGbZ7lJiQgL49azBnDayX5sgoajaztJdpfycWcKna3OJDLZS0+DklBH9eOqK8c37Vdkd3PnuWspqG3np2kkkhNvYU1LLkz9mct74ZI4fEtfpf+e9pbUszyojOTKI4AAzq/eWsza7grEpEZw/PpkGh5sPV+dQUFnPOWOTOHZgbKfnzx2O4uoGvt2Uj9OtCQm0cO7YpOZkUGdsL6jm+y0F5JTXsz6nsrl756kjEnjxWt9cHg52bfJlgPccUKi1fkAplQCsAcZqrQ/MU7fS2YtUdlkdM/8zj0cuGcvFE1MO7wUIcZRIgOf/H6BAPkT5tcZaIygZfw2EeAKTmmLIW2METpHpEDvI2G6vhKcnw5DT4dwnO35OrY0s1Hd/hIZquGaOEWC1t59Sxu3mT+DnR6GwVQPq4BiY8Rs49g7j3I+NgoEnwtgrYdkzkLXQeF5rMGQvN45JOxaGnGpk0i55HeKGw+tnGsHR6f8yAsJ9S2HrF0Y2LuP4tpmt4h3wxa9h3xJj3lxYP7htBWz8ED6/HcKTjee6eUFLZq6JsxHeOAfy18P1X0HKxPZf84fXw+75cMvPENm56hxf6A3XJ7k2Gb7bXMAfPtpAZb0xNyvQYuKV6yYzY7DxntdaM3dLIQ98vhmb1cy3d83kqR938uz8nSy9bxYJ4S0NMLTWNH2EVopubyryr6+38sLC3QxJCOWrO2diNXdP4/steVXcN2cDmUU1fHfXcc1ZrNa01t3+enqbTbmVfL+lkEsnp5Lso0Y5B7s2ee3rL6XUu8AJQKxSKgf4C2AF0Fo/D/wdeF0ptRHjK8R7DhXcdUWgdNEUQgjRWateNYKhHXPh2s+McsVXToVqT/ttZTKClf7HwuInoKbQyGYd93tj/tf+HHb46AbY7imLNAfAa2fBVR9AmqfMuXAzfHgD1Jcbc8Wq82H3PEgYBSc/YARp5Vmw/l0j2xU3DIq2QEOVEfAljTeCsy2fwbf3gS6Cc540MoWf/soIzvpPhxHnG59AZ8835rjFDjbO3/9Y46c9cUOM17v6Vfj5MTjzEbDaYOzlsPBhqNhrZPH2D+4ALAHGv2F9mZH9a49SRuDZUA22jjMZQnRGdlkdH63OYe6WQv7vzGHMHNw209TodPPwd9t46ecsxqREcM4Y4//LaQNiGJ0S0byfUorTRvYj0GLi+tdW8uKC3cxZk8PMwXFtgrumfb0ZA/3mlCHUNbq4bHJqtwV3YHTV/uRX06lpdBJua78CQYK7QxuVHMGo5IhD7+gj3uyiecUhHs8DTvXW+ZubrEiAJ4Q4BKXUr4GbML5seklr/bhSKhp4H0gH9gCXaq3LfTZI0b1Wv25kkSbdAC4nLH/RKDfct8TIXOWsBEed0SwkIBQ+vQU+uQWu/hiWPmtkvPYuNuabnfFvI0jbNc9o9GEJNOa9bf8aTvkbTPuV0SHyrfONzNbgU43g7Of/GqWH6TNgxzdGKecZD8PkXxpZQ4C0qTDiPGM9t09uBpPV6P6Y5CmrUgpGnm/so3VLmWNoAvz0Dzjz4ZbMXFezZCaTkembfGPLNrPVCMxKdxrn7IjVBtYOgrsmSklwJw5qZ1E1z87fxW9PHdphlmTBjmJueG0FGggwm3hlUVabAG/13nLum7OBHYU1XHtMf/541vDmrpAdOWFoPKeP7Md/f9iB1nDvmUe/wsZmNfP38w8ogOsWJpPqMLgTvYMvm6x4lU0CPCFEJyilRmEEd1OARuBbpdSXGI2dftRaP6SUuhe4F7jHdyMVXVaVb7TST51szONqCnQyvzeCOJSRzaorhcp9cNnbRonjkifBYoNrPmnJcF3wArx2Brw8y5jPdu6TsOA/sPoNI6s1ZzaU7ABbBMQOhZwVRrA2dbZxfGQq/OI7I/u1+RPY9iWkTDGyYOGJRnCn3e3P6bMGwcWvwYvHg6PUmPO2P6NGrOX+gOONH29IntB2zp0QXvLXL7bwc2YJS3aW8vovJjMgNpRNeZWkRgUTFxaI1ppHvttOSlQw786extvL9vLCwt2U1DQQGxrI+yv3ce+cjfQLtzV3peys+88ZwYIdxVjMilNHdP44IXqCXhvgWc0mLCYl6+AJIQ5lOLBca10HoJRaAFwInIdRZg7wBjAfCfD8h9sNH17XMictIg2m3WqUNM6ZbZRBOuqMrFxInBEADj2jpStl+oy25Ytp02D6XUbzkam3QlS6cX/dO/DSSRAQZnR43PWTMa/tpPtbgrsmIbFGtu+0f0JJplHe2BTQmczAQbIKcUPg4leNLOGgWd337yRED9XUFOSqqWn8sLWQC59dgtbG+sYDYkP47PbprNpTzsbcSv590WiSI4M4f3wyz87fxZfr87hoYgoPfbONyenRvHr95C6vLZYcGcQTl4/D4dJdasYhRE/QawM8MMo06xu7bWk9IUTvtAl4UCkVA9QDZwKrgASttWcCFgWAfIXrayWZRnv/+OEw+JSD77v6NSO4O/kBo8Ryy2fw3X0w949GM5JL3jDKKl89FSqzjSUBmsoiZ/25/ec84T5jPt3ws437cUNgzGVGqeaVH0DCCJhwrdFkxHKQBX5NZogf1uWX3xyACtHLaW200O8XbuP+s0fwqxMH8a+vtxIdEkD/mBD++fVWfvfheoqqG0iODOKC8UYzvSEJYQxPDOfTdXlU1jspr3Pwp7OGH/bC0aeO7NedL0uIo6ZXB3i2ALOsgydEJ1RUVPDOO+/wq1/9qsvHPv7448yePZvg4E6sRdUDaa23KqX+DcwFajHW53Ttt49WSrXbcvhw1ukUXVSRDe9fZXRlbDL1VmN+W3uBVFW+0TAl43gjy6aUsQZa1kJj/tyEa1s6Yp74R1j7Foy/+tDjsATAuP2ml5//rHFrMrfdTwhx2OZtL2L13nL+ecFobFYzyZFBPH1lS1mw1pp/fLUVgAcvGEWApaUJyfnjkvjXN9vYXlDN6SP7MSYl8mgPXwif6762PD2QzWrC3igBnhCHUlFRwbPPPntYxz7++OPU1dV184iOLq31K1rriVrr44ByYAdQqJRKBPDcFnVw7Ita60la60lxcZ1fI6jPcjmMDpMHs+Yt+PJuI7CrLoA3z4WyPcZi2L9ebzQtWf4cvHWB0SAFjOecM9tYm+3FE4x1585+rO28tIzj4Mr3YNiZLduO+x3csfbwm32YzG2DOyFEp3y2LpdLnl/Cnz/bxHebC3C5je/QnC43D32zjf4xwVwyqf1lrn45I4OLJqQwKD70gKWwzh2XhFJgd7r47alDvP46hOiJenUGL8hqljl4QnTCvffey65duxg3bhynnHIK8fHxfPDBBzQ0NHDBBRfw17/+ldraWi699FJycnJwuVzcf//9FBYWkpeXx4knnkhsbCzz5s3z9Us5LEqpeK11kVIqDWP+3TQgA7gOeMhz+5kPh9h7fHwjFG+DWxYbC1U3rf0WMxD6jYGf/m6sAwew9n8QGg91ZXDtp5A6xdh++r+MJQO+uBNWvgzTbjGO2fA+DJwFURkw5tL2W/i3x9Srv+sUokfRWvPCwt089M020qKD2ZSbw5tL93L9sek8cO5IPliVw47CGp67akKHywMopXj00rG43PqAxbETI4K4aEIKkUFWBieEHY2XJESP06sDPJvVLF00heiEhx56iE2bNrFu3Trmzp3LRx99xIoVK9Bac+6557Jw4UKKi4tJSkriq6++AqCyspKIiAj++9//Mm/ePGJjY338Ko7Ix545eA7gNq11hVLqIeADpdQvgb3ApT4dYW+Qswq2fGr8vvkTGHMJbPwI5nja8AfHGB0tJ1xnrPM2/yFjofAr32sJ7ppMuNaYV/fTP4z5bIseM+bDXfjiUX1Jwj+s3lvO4z/s4KVrJ0nDDB978sedPPbDDs4Zm8Qjl4xBofjn11t5fckeBieE8tj3O5icHsXpow49/23/4K7JI5eM7e5hC+FXen2AJxk84Xe+uRcKNnbvc/YbDWc81Kld586dy9y5cxk/3lhnq6amhszMTGbOnMlvf/tb7rnnHs4++2xmzpzZvWP0Ia31AS9Ga10KSLvC7vTjXyE41gjkfn7UKJX84QEjczf5Rsica3SrPOZ2o7TywheMDF97i+4qZazx9uwx8NaFEBgGpz541F+S8L26Rie7i2sPuujwR6uz+TmzhI25lUxOjz6KoxOt7Sis5qmfMjlvXBKPXToOkydA++NZw9mSV8UfP9kEwMvXTZbFtoU4Ar06wAuymqmod/h6GEL4Fa019913HzfffPMBj61Zs4avv/6aP/3pT8yaNYs//7mDboNC7G/3fKPJyekPGUHenBvh7UuhKscI5NJnwMTrDjzuYB/yYgbCzLth/r/g1L9DqMyB7Gu2F1Rz69ur2V1cyy+mZ3DfmcPaLetbtLMEgPXZFRLg+YjWmvs/3URIoIW/nDOyObgDY2mrp68azwXPLGHGoFjGpUb6bqBC9AK9OsCzWU00VEkGT/iZTmbaulNYWBjV1dUAnHbaadx///1cddVVhIaGkpubi9Vqxel0Eh0dzdVXX01kZCQvv/xym2P9vERTeJPLCd//BcJTYOINYLLAvAdh7yIYdrYR3B2u434Pg0+FpPHdN15xxDblVrIiq4xrj+mPpYN5VEfq200F/Ob9dYQEWrhwfDKvLs5ic14lr+y35ll2WR3ZZfUAbMyt9MpYxKF9vj6P5VllPHjBKKJDDuw0Gx9mY97vTsBqlsydEEeqVwd40mRFiM6JiYlh+vTpjBo1ijPOOIMrr7ySY445BoDQ0FD+97//sXPnTn7/+99jMpmwWq0899xzAMyePZvTTz+dpKQkv22yIrzs50chfx1c9ApYbca2E/8IX91tLHVwJExmSJ5w6P3EUfXcgl18tSGfeduLePqKCUQEW7v1+UtrGvj9R+sZkhDKS9dOIj7cxvRBsfz2w/W8vzKbX87IaN53sSd7Nyg+lA05EuD5gsut+dfX2xiTEsHlkzteTqb1cgdCiMPXuwO8ADP1skyCEJ3yzjvvtLn/61//us39gQMHctpppx1w3B133MEdd9zh1bEJP+FsAFTbdeCyV8CCf8PoS2H0xS3bx1wCoy6UJQa8zO3WnPfMYq6amsblUw69TuPOomoq651M7B91ROfdXlBNcmQQy3aXcv6zi/nfjVNJjgw6ouds7dHvd1DX6OLRS8cRH258aXDRxBReWZTFVxvy2gZ4u0qJDwvkgvHJPPzddirrHUQEdW/AKQ5u+e5SCqrs/PmcER02RhFCdJ9e/VVJoEW6aAohxFEzZzY8PRGq8oz7NcUw5yaISIazHjlwfwnuvG5LfhUbcyuZu6WwU/vfN2cjv3p7NVrrwz5ng9NFVkktF05I5p2bplFS3cBVLy2jqOoQ6x920tb8Kt5bsY9rpvVnUHxom8fOGpPImn0V5FYYJZlut2bJzhKmD4plTIrRhGWjZPGOui825BEcYObEofG+HooQfUKvDvCCAszYHW5fD0MIIXqn+oqW32uKYesXULEP/neRsSTCy7OguhAufBlsHXc4FAfSWvPBqmwKjzAoWrLLKE/ckFNxyKCtss7B6r3lFFY1kF95+OfdWVSDy60ZkhDG5PRoXv/FZIqqG7j6leWU1zZ26jm01rjdB45Xa83fvthCeJCVu04efMDjZ49JBOCrDcaXDNsLqymtbeTYgTGMSY4EYH1OxeG9MHFYHC4332wq4OThCQQFyJc6QhwNvTvAs5ppdLlxtfNHQgghxBFY/x78JwM2f2rc3zwHtAvO+A+U7jSCu8ZauP4rSJvq06H6ow05lfzhow089VNmh/u43Uaws3ZfeYf7LN5ZCkBJTSN5hwjaFmYW0/Tncu2+CgDKaxs5+6mf2dCFoGh7gdGwaVg/Y5Hpif2jefnaSeworOGdFfua91uRVca/v93WbuB5+7trGfbnbzn98YX8+bNNzdU4767IZunuUn536lAigw9s1NE/JoTRyRF8tSHf8/qNAHf6oFgigq2kxwR36bWII7d4ZwkVdQ7OGZvk66EI0Wf06gDPZjVenpRpCn9wJCVR/qIvvMY+oWATfHEXaDf89HejS+aG9431FqfeDBe/CgNnwY0/QMpEX4/WL320OgeArzcW4HAZlSgLdhTzp083Nr+PlmWV8uriLF5fsqfd52h0ulm5p4yxnpbz67MrDnrOeduLiAy2EmAxsS7bCBp/3FbEptwqvvQETJ2xvaCaALOJ9NiQ5m3HDoolIzakzRjeWLqH5+bvYsmu0jbH2x0ufthSyPB+YSRG2Hhz6V5uf2ctu4tr+MdXW5gxKJYrDzKf8OwxiazPqeSJHzJ54sdMhiaEkeSZ/zcmJVIarRxlX27IJ8xm4bgh0mlZiKOlVwd4QVajFEA6aYqezmazUVpa2qsDIK01paWl2Gw2Xw9FdMTZAHVlB9/HXgkfXGOUXJ79mJGtm/cPyF1tNFIBGH4OXDMHojMO/lx9lMPl5i+fbWJXcU27j9sdLj5fn0dihI2y2kYW7SzB5dY88Plm/rdsH/N3FAPw/spswMiQNJUzaq2pqDPKINfnVFDX6OKXMzKwmtVBSxPdbs2C7cUcPySOUUnhzRm8eduLAFi6XxB2MNsKqhkYH3rAenSjkyPaLFOwznOOZ+fvbLPfmr3lNDjd3DlrMK/dMIW/nzeSH7YWcvZTizArxb8vHtNmDbX9nTnaKNN87IcdjE6O4MVrW75kGJMSQX6lnaLq7pkPKA6uweniu80FnDayH4EWKc8U4mjp1V00bU0BnnTSFD1cSkoKOTk5FBcX+3ooXmWz2UhJSfH1MMT+SjJh5ctGFs7thjvXQEg737Y76uG9q6B8r6f0chqs/R8segxQbbtkig6t3lvOG0v3UlHv4InLD1y/78etRVTWO/jvpWP5zfvr+GJdHjV2J1kltQSYTTw/fxcTUqP4ZlMB/cJtFFTZ2VpQxcikCJ74MZNn5+/iw5uPYfHOEpSC4wbHMjwxnA3ZHWeuNuVVUlrbyIlD49mYW8n/lu3F7nDx845iLCbF5rzKTnef3F5QzTEDYw7YPiYlgs/X51Fc3QBAbkU9/WOCWbyzlHXZFc2LWy/aWYLZpJiSYSxIfs0x6dQ1uvjXN9t45JKxh+zGmRodzJ/OGk50SAAXjE9GqZZgcEyKcY4N2ZWcPEK+bPK2zXlVVNudnDw8wddDEaJP6RMBXoNTAjzRs1mtVjIyJNshfGDdO/Dlb4xyy8GnwvavYfHjcOo/2u7nbIQProU9i+CCF6C/sU4iJ/0J3roAMmZCuMyx6YymeWHfbCzgL+c0HrDo80ers0mMsHHC0HjOGJXIlxvy2JxXxcC4EC6bnMo/v97GX7/YTKPTzb8uHM0Nr6/k58wShiSE8fbyfTQ63dz6v9VEBgcwKimCyOAAxqZE8snaXNxu3Zz92l1cw9++3MKs4QkUVtqNYHBIHGaT4pVFWby7Yh9VdifXHtOfN5fuZdWeMmYd4oN6ZZ2Dgio7Qz3z71oblWw02tmUW4nTk3H867kj+fV763h23k5evHaS8e+zq5RxqZGE2VqCyZuPH8hlk1PbnXfXnhtnDmh3+6jkcK6YkkZCuAR3R0Oep5tpemywj0ciRN/SN0o0G6WTphBCtKE1fPU7+PRWSJkMd22Ey982yixXvGx0v9QadsyFr/8AL8yEzLlwzuMw9rKW5xlwIpx0v/EjOuXnzBISI2w0utx8tDq7zWMbcypZsKOYCyckYzYpzh2XRG2ji+2F1fzqhEFcObU/EUFW5qzNZVRyOCcOi2dYvzB+zizmp21FFFc3cNfJgympbWRLfhXHejJpY1IiqGlwsrvEKAvNKqnlipeWsSizhPs/3cTT83YyLjWS6JAAxqdFAvDc/F2YTYrbTxpEgMXEst1GmeaGnAru/XgD17yynOtfW0FBq+Yt2wqqANoN8EYmhaMUbMytZF12OWaTYmpGDNcdm87cLYVsyjWyhBtzKpjeTgaws8HdwQQHWPjXhaMZnSJdXb2hrLaxTflr0/8bieHdtwaiEOLQenUGr6kdr8zBE0KI/Wz5DFa+BFNvgVMfBLPnz8Hxf4CNHxrNUxx1sOljsAZD8kSYcXfb4A4w0j6/O/rj76GKquxc/tIyHC434TYrNx8/kHNbdQ+srHewIaeC208cxNLdpbyzfB83zhjAwsxinvppJ6v3lhMaaOGySUYTkWkDYogLCyTQYuLccUlYzSauPaY/T/20k8smG/vMHBzLG0v24nZDfFggt584iKTIIP7w0QZOHGasO9bSaKWSRqfmhtdX4HBpvrpzJvmV9bz8cxaXTDLKp5Mjg4gLC6SouoEp6dHEh9mYkBbJst1lVNkd3PjGKuobXQyID2VHQTW3vr2a92ZPI9BiZnuh0UFzaMKBAV6YzcqA2BA25FRS73AyrF8YQQFmfjk9g3eW7+Oejzdw+4mDcGuj66XwP796ezUut+bDW44FIK/CTnCAmfCgXv1xU4gep1e/46SLphBCtMNRD3Pvh4RRcNo/2y44HjMQxl0Ba98CZTZKMKffBeZDz70S8MLC3ewtrePsMYks2VXK64uz2gR4S3eV4tYwY3AcA+JCuev9dVzx0jKWZ5WR5pk7dtGEFKI8ZZtmk+LFayYSaDE3Ny256bgBWM0mLp5gBGQzBsfx0s9ZLN1dym0nDsRiNnHppFROGBpHfJhRijgwLpTgADNP/ZRJdnk9UcFW3rlpKkP7hTG0XxgntFqAWinFuNRIvt9SyPFD4wAj0Hzyx0we+GwzxTUNfPKr6YxLjeSrDfnc9s4a/vbFFh68YDTbCqoJs1lIjGi/BHJ0cgRLdpVS3+ji3HHGv0tEsJW/nzeSW99ew/2fbSbIamZ8WlQ3/5cR3pZXUc+y3WVt5mnmV9aTGGFrMw9SCOF9vTzAkwyeEEIcYMnTULkPzv+ybXDX5MQ/GkHg1FsgdcrRH5+fKq5u4O3lezl/XDKPXjqWh77Zxss/76a+0dVcUbJ4ZwnBAWbGpUYyJiWCqC+srNlXzp2zBnPbiQPb7TS4f7ATbrNy56yWRb6npEcTYDHR6HQ3Z/6A5uAOjEBxfFoki3eWcsWUVP5w2rDmILI9E9Ki+H5LISd6Ar9pA2J4/IdM5qzN5dpj+jc3RDlrTCIbcgfwwoLd/Li1iJoGJ8MTwzr8QD86JZJP1xmLkDc9B8AZoxM5bWQC320u5PghcQRYevUMkl6pae3BynoHlXUOIoKt5FfaSYyQ8kwhjrZeHeA1zcGTDJ4QQgAuB2z/Bhb9F0acZzRGaU94krGWneiSl3/eTaPTzW0nDgRgakY0zy/Yxdp95RzrKTlcvLOEaQNimgOY92Yfg9WsGBAXetjnDQowc/LweBwuTVpMx80s/nPxWGrsznbnx+3v6mlppMcEMyIpHDCCsQCLiYggK787bWibfX9/6lBiQwLZXlhNUXUDF01I7vB5x7Sa+9Y016/J388bxbrsCs4c3e+Q4xM9z5cb8jApcGvILq8jIjiC/Mp6jhsc5+uhCdHneC3AU0q9CpwNFGmtR3WwzwnA44AVKNFaH9+dY7BJgCeEEFBbasy3W/Ua1BRARNqBXTLFIWmt+W5zAScMjW/++9KkrLaRt5bt5dyxSc3B2sT0KEwKlmWVceygWHIr6tldUstV0/o3H9eZYKsznrlyAodaRvNQywu0FmazcoZnPTkw/p7++6LRpEUHE25rW65rMZu46bj2u1bub0RiOCYFIYEWBsS2DWrjw20su2+WlPP5oX2ldazPqeS8cUl8ti6PfWV1DO0XRlF1A4ld+P9OCNE9vFkD8TpwekcPKqUigWeBc7XWI4FLunsAQbIOnhCiL6vMMTplPjYS5v8LEsfAFe/BnWshMu3Qx/cSOwqrGfPAd+wsan9h8c5akVXGLf9bw58/29Rme5Xdwc1vraLB6eb2kwY1bw+3WRmRFM6KLKP75DcbjRK24wZ3fwMRpdRBF//uDheMT2Fi/+gjeo6QQAvD+oUzsX9Uu+OV4M4/fbHBKLu99QQje72vrI6i6ga0psP5mEII7/FaBk9rvVAplX6QXa4E5mit93n2L+ruMbTMwZNlEoQQfYjbDXP/aCxert0w9nI45g6IH+brkfnEiqwyquxOftxayKD4wy+FXJ5VBsAHq3I4YWg8Z45OpLSmgetfW8nW/CqevHw8g+LbZuSmpMfw9vK91DQ4efnnLKZmRDO4nQ6TfcnL101qbhgjeocvN+QzIS2SYf3CiQq2sq+sjnzPGngS4Alx9PnyCjsEiFJKzVdKrVZKXdvRjkqp2UqpVUqpVcXFxZ0+QaBFumgKIfqg7V/Dsmdh1EVGtu68Z/pscAc0Z+6a1nE7XCuyyhgcH8rY1Eju/XgD983ZwHH/mceOwmpeunYSZ41JPOCYqQOiaXC6eeDzzRRU2bntxEHtPHPfkuRZhkH0DmW1jWzNr+LkEQkApEUHk11WR75nDbwkKdEU4qjzZYBnASYCZwGnAfcrpYa0t6PW+kWt9SSt9aS4uE5O1i3cgmnxf4mz1kuAJ4ToW5Y8aZRgnvt0nyrF7EhTgLdyTzlOV+cqOrTWPL9gFy8t3A2Aw+Vmzb5yjh0YwxOXjcPl1nyyNpczRyfy6W3Tm9eb29/kdKOk8aPVOYxKDmemF8ozhfClDTkVQEtX1NToYCODV2lk8PpJBk+Io86XXTRzgFKtdS1Qq5RaCIwFdnTLsxdugh//RqLlCVkmQQjRd+xbDtnL4Yz/tCxe3sdlFlUTEWSlst7BpryqNu352+N2a+7/bBNvL9+H1ay4YEIyOeX11DW6mJIRQ3psCD/89niCAyxt1vxqT3RIAEMTwtheWM1tJwySOWai19mQU4lSxhqHYGTwvt1UQE55PaGBlgOa8gghvM+XGbzPgBlKKYtSKhiYCmzttme3GN8YRVicksETQvQdS56EoCgYf7WvR+IT2WV1PPD5Zj5enQMYDVAKqxq4eKKxKPjSXe2Xabrcmm825vP0T5lc//pK3l6+jwvGJ+NwaT5endPcKGVyhrEmXWJE0CGDuyZnjUlkYv8oThsp7f9F77Mhp5IBsSGEeQK5tOhgnG7Nmn3lMv9OCB/x5jIJ7wInALFKqRzgLxjLIaC1fl5rvVUp9S2wAXADL2utN3X0fF1mNWq+IyxOabIihOgbSnfBtq/guN9BQIivR+NV32zM5/uthfzzgtHYrGYanW7+8vlmPliVjcutSYkK4qKJKc3lmdMGxLBwRzHLdpdy6wkDKaq243RpkiKDaHC6uOu9dXyzqQCA2NAA7j1jGDcfN4DssjreX5nNgLgQMmJD2iwe3ll3zhrcZmFyIXqTDTkVzBjUUnqcFm2sxbglr4rpg6QkWQhf8GYXzSs6sc/DwMNeGYAnwAs1OymRZRKEEH1B5veAhgnX+XokXmV3uLj/s82U1DTgcmv+e+k4fvvher5Yn8d1x/QnzGbl6Xk72Vda1xzgDY4PZdqAGD5ek8Om3Equf20FZbWNnDIigcp6B8t2l/F/Zw7jmmnpBAW0rHF3+ZQ0fvfhevaU1nLJxFRfvWQheqSCSjtF1Q2MbrWAfaonwHNrSIqQBitC+ELv7VNsMS4qYWYHDU4J8IQQfUDBRgiJg4gUX4/Eq95Zvo+SmgbOGp3IZ+vyOOepRXyxPo97zxjGX88bxfnjkwBYsquEnUU1BFhMpEYHc8zAGOoaXVz03BLMJsUvZ2SwPKuMlXvKefSSscw+bmCb4A7grNGJhNksuDVMzjiyNeCE6A1W7SnjL59twuXWrPc0WBmTEtn8eGKEDYtnjcPESCnRFMIXeu8MfKtxUQk1O2WhcyFE31CwAfqNhl7cyMPucPH8gl1MzYjm6SvHE/ihiTlrcrlxRgY3HzcAgIFxocSHBbJ4Vyk1dgcDYkMwmxRTM6JRCmJDA3nnpqn0jwnh7lOGUlLT0Jx12F9QgJkLxifz5tK9TJUAT/RxWmv++sUWNuZWMjI5gr2ltVhMipFJ4c37WMwmkqOC2FtaJ3PwhPCR3hvgeZqshJgasUsGTwjR27kcULwNBt7q65EcsZcW7mZLfhWPXTbugMc+WJVNUXUDj18+DqUU/75oDJdNSmVyenRzh0qlFNMHxfJzZjE2q7m5a2ZMaCDv3jSNAbEhxIcbfyOCAswdBndNfnvKUGYOjjvkfkL0dkt3l7Ixt5KQADOPfLedtOhghiSEYbO2zXynRQd7Ajwp0RTCF3pviabV+EMcYnJIBk8I0fuV7ABXI/Qb4+uRdMrCHcUc868fqahrbLN9R2E1//52G5+szaWstvGA415bvIeJ/aM4ZkAMAFaziakDYjCZ2mYtjx0YQ0lNIznl9QyOD2vePm1ATHNw11kRwVZO8SziLERf9sKC3cSGBvDStZMoqm5g1d5yxqZGHLBfU6OVJCnRFMInenGAZ1xUgk0O7NJFUwjR2xVsNG77jfbtODpp/vZi8ivtLNvdsmyB1po/fdrSTHlFVlmbY/aU1JJVUsu5Y5MOuZ5c6+59gxNCu2nUQhxdSqlfK6U2KaU2K6Xu8mwbp5RappRap5RapZSacjTGsjW/igU7irlhegbHDorlrDGJAIxOjjxg32GJ4QRZzZLBE8JHem+A52myEqQcsg6eEKL3K9hoXPdiBvl6JO3SWre5vyW/EoBlu1uCuI/X5LIiq4y/nDMCm9XUJvgDWJhZDMDxQ+IOeb6kyCAyYo2lIgbFS4An/I9SahRwEzAFGAucrZQaBPwH+KvWehzwZ899r3tlURbBAWauntofgPvOGMYJQ+M4aVj8AfteMTmVeb87gZDA3jsTSIierBcHeIGAIpgG6iXAE0L0dgUbIGEEmMyH3vcosztcnPHEzzwzbydgBHtb8qoAmoM4p8vNw99tY0JaJFdN7c/E/lEs3y+Dt2B7Mf1jgkmP7dwafzMHxxJgMZEe07vXBBS91nBguda6TmvtBBYAFwIaaOpqEgHkHY3B7CisZnJ6NBHBxoLmKVHBvH7DFPq100jFYja1u10IcXT03gBPKbAGYVMO6h2uA749FkKIXkNrI4PXQ8szX1mUxbaCar7akA9ATnk9VXYnKVFBbCuopry2kSW7SimsauCmmQMwmRTTMmLYVlDVPEevweliya7STmXvmtx9yhA+uPkYAiy990+d6NU2ATOVUjFKqWDgTCAVuAt4WCmVDTwC3Hc0BlNV7yAiyHo0TiWEOEK9+6+exUYgjWgNjS6ZhyeE6KWqcqG+HBJG+XokByiqsvPMvJ0EWkxsLaiivLaRzXlGeeb1x6YDsDyrjE/X5RJms3Cip9xr6oAYtG6Zh7dqTzn1DleXArzI4IDmDppC+But9Vbg38Bc4FtgHeACbgV+o7VOBX4DvNLe8Uqp2Z45equKi4uPeDxVdifhQVJyKYQ/6N0BnjWIQG18+2tvlABPCNE+pdRvPE0MNiml3lVK2ZRSGUqp5UqpnUqp95VSAb4eZ4eaG6z0vA6aD3+3HYfLzT8vGI3WsDyrlM15VZhNiksnp2Kzmpi3rYjvNhVw1ujE5nbrY1MjCLSYmufoLdhRTIDZxDRP90wh+gKt9Sta64la6+OAcmAHcB0wx7PLhxhz9No79kWt9SSt9aS4uM5/MdLBc1FV7yDcJhk8IfxB7w/waACQeXhCiHYppZKBO4FJWutRgBm4HOOb88e01oMwPlj90nejPISCjYAy5uD1IBtzKvloTQ6/mJ7BueOSCA4ws3SXEeANjAsh3GZlYv8oPlqTQ22ji/PHJzcfG2gxMyEtiuVZpTQ63czfXsTkjChp2iD6FKVUvOc2DWP+3TsYc+6O9+xyEpDp7XHUO1w43ZpwKdEUwi/07gDPEkSANgI86aQphDgICxCklLIAwUA+xgenjzyPvwGc75uhdUJJJkSkQmDYofc9SrTW/PWLzcSEBHDbSYOwmk1MSo9mya5StuRVMTLJWDtrWkYMLrcmKcLGlPToNs8xbUAMm/OqGPmXb9lRWMOJQw/s1idEL/exUmoL8AVwm9a6AqOz5qNKqfXAP4HZ3h5Etd0JQJhNvmARwh/07neq1Ya1UTJ4QoiOaa1zlVKPAPuAeoz5LquBCk/nOoAcILmDpzj6nA2eTsEeNYUQ1s+rp6yoM+YzR4V0XKn66dpctuZXceeswfy4rYhVe8t56MLRzWVdxw6M4aFvtgEwMsloAjhtYAx8D+eNTz5gsfILJySzo7Ca1OhgRiaFy2Ljos/RWs9sZ9siYOLRHEdVvQNASjSF8BO9O8Cz2LA01AMS4Akh2qeUigLOAzKACow5Lad34fjZeL5BT0tL88II91O4GV4+GS55A4acamyrKfT6+nd3vLuWaruTT2+bDoDbrflucwGnjEjAYjaKQZ6Zt5PMohq+21yA3eFmZFI4l0xKbX6OY1rNnxuRaAR4E9Oi+NNZw7lwQsoB50yNDuaZqyZ482UJITqhyu4J8KREUwi/0LtLNK3BWFx2QEo0hRAdOhnI0loXa60dGM0LpgORnpJNgBQgt72Du7ORwSFpDd/cA446KNzUst3LGTyny82qPeWsy65gT0ktAF9tzOfWt9fw1UZj6YOKukYyi2o4c3Q/HC5NQZWdv5wzEnOrrNzIpHDCPHPoRngyeCaT4saZA4g+SGZQCOFbVfVGMUO4lGgK4Rd6eYBnw+KWAE8IcVD7gGlKqWCllAJmAVuAecDFnn2uAz7z0fhabPsS9vxs/F7lWdvY2WAskRDqvfLFncU1zVUQTQHdB6uyAViUWQLA2n0VAFwzLZ1v7prJZ7dNZ0pG2zl1FrOJYwfFkBEbQmSwBHRC+AvJ4AnhX3r3VzGWIEwuzxw8WSZBCNEOrfVypdRHwBrACawFXgS+At5TSv3Ds63dtaaOGocdvvsjxA0H7YJqI9Cipsi49WKAtyHbWLcuMcLGF+vzOG9cEot2lmA2KRbvLEFrzaq9ZZhNirGpEQQHWBjbwfpz/7xgNHWN8oWbEP6kSpqsCOFXenkGLwiz05iDJxk8IURHtNZ/0VoP01qP0lpfo7Vu0Frv1lpP0VoP0lpforWnJa+vbPkMKvbCaQ8aHTObMng1hcatFwO89TkVhAVauHHmALYVVPOfb7cDMPu4AeRV2skqqWX13nJGJoUTHHDwD4AxoYGkRgd7baxCiO4nTVaE8C+9PsBTnjl4NQ3OQ+wshBA9WNkuUCbIOA7CEw8M8MK6FuA1OF1c+dIy5m0rOuS+63MqGJMawVmjE1EKPl+fx/SBsVzmaaCyYEcx67IrmJAW1aUxCCH8Q5XdQYDFhM1q9vVQhBCd0LsDPIsN5bSjFJTWNvp6NEIIcfgqsiEsEcxWCEuC2iJwOaG6wHi8ixm8RZklLNlVyuM/HnyNZLvDxbb8asakRNIvwsbk/sa8uksmpdA/JpjkyCBeXZyF3eFmUroEeEL0RlX1TsneCeFHeneAZw1CuZ3EB5korfFtdZUQQhyRymyjNBMgPAm028je1RQBCkK61sHzyw3GHL712RVsyKnocL8t+VU43ZqxKcbC5Ncdm87o5AhOG9kPpRQzBsWSXWaUwk/sLwGeEL1Rld1BeJDMvxPCX/T6AA8gMRRKaySDJ4TwY5XZEOFZKy48ybitzoeaAgiOMTJ7nWR3uPh+SyFnju5HkNXM/5bt7XDfDdkVAM1NU84ak8gXd8xoLtU6dpCxtl1yZBCJEUFde01CCL9QVe8gTDJ4QviN3h3gWWwA9AuCEsngCSH8ldsNlbkQ6cnghSUat1W5Rgavi+WZC3cUU9Pg5PLJaZw/PpnP1+dRWedod98NOZXEhQXSL9zW7uPHDowFYIJk74TotartTlkDTwg/4rUATyn1qlKqSCm16RD7TVZKOZVSFx9sv8PiyeDFB2uZgyeE8F81heB2tMrgJRu3VfnGHLwuNlj5amM+UcFWjhkYw9XT0rA73Hy4OrvNPhV1jazcU8aKPWWMTYnEWCLwQHFhgfztvJHcfNyALr8sIYR/MEo0JYMnhL/wZgbvdeD0g+2glDID/wbmemUEngxevE1LBk8I4b8qc4zbpjl4wdFgDoTqvC5n8OwOFz9sKeT0Uf2wmk2MTIpgSkY0z8zbSUGl0XX4qw35TPrHD1zy/FJyyuuZ4SnD7Mi1x6QzKjnisF6aEKLnkyYrQvgXrwV4WuuFQNkhdrsD+Bg4dJ/uw2E11lqKs7motjtpcMpaeEIIP1S5z7htCvCUgrB+RtlmTWGXArz524upbXRx1uik5m0PXTiaBqebX7+3lvXZFfz2w3WMSYng9Rsm8/MfTuS6Y9O78cUIIfyNNFkRwr/4bA6eUioZuAB4zmsnsRoZvOhANwBlUqYphPBHzRm8lJZt4UlQtMUo3exCgPftJqM8c9qA6OZtA+JC+eu5I1meVcYlLywlJiSQF6+dxAlD40mNDu6wPFMI0fvZHS4anW7J4AnhR3zZZOVx4B6ttftQOyqlZiulVimlVhUXF3f+DBZjDl5UgJG5k06aQgi/VJENtgiwhbdsC0+C4m3G752cg9fgdPHj1iJOGZGAxdz28n/xxBQuHJ+M1aR48dqJxIYGdtfohRB+rNruBJAmK0L4EV++WycB73m+GY4FzlRKObXWn+6/o9b6ReBFgEmTJulOn8HTZCXSagR4Mg9PCOGXKnNayjObhCUaa+FBpzN4S3aWUt3g5IxRiQc8ppTi0UvH8hf7SCKkmYIQwqPKbnTYlSYrQvgPn2XwtNYZWut0rXU68BHwq/aCuyPiCfAiLMbFqUQyeEIIf/H5HbDuXeP31oucNwlvmUNHaL8On+bvX27h0bnbAfh2UwFhgZbmtev2p5SS4E4I0UZVvSfAkxJNIfyG1zJ4Sql3gROAWKVUDvAXwAqgtX7eW+dtw9NFM8xilBeUSgZPCOEP6itgzZuwbxmMvdwI8NKOabtPmwAvvt2naXC6eGvZXhqdbiKDA5i7pYCThscTaDF7b+xCiF6lylOiGSYlmkL4Da+9W7XWV3Rh3+u9MghPBi9QNxJoMclaeEII/5C/zrgt2WEEefbKlkXOm4R5AjxrMASGtfs0G3MqaXS6SYkK4u9fbgHgjFEdZ/uEEGJ/zRk8ye4L4Td82WTF+zwBnnLaiQ0NlDl4Qgj/kLvauFUmWPKk8XvrDpoA4Z55dKHxxrIJ7Vixx1ip5r3Z0xiRGE5ooIXjh7Sf7RNCiPa0NFmRAE8If9G78+2eLpo46okNDZAumkII/5C7BqIHQGR/2P61sS0ire0+TfPuDjL/bmVWGYPiQ0mJCubDW46hpKaBoAApzxRCdF5Lk5Xe/ZFRiN6kd2fwzBYwWcBRT0xoIKW1ksETQviB3DWQPBFGXtCybf8MniUAQuI7nH/ncmtW7S1ncrqx3l1IoIX+MSHeGrEQopeqqndgMSmCrPLlkBD+oncHeGBk8Zx2YkIkgyeE8ANV+VCdZwR4w88xvqQyWdtfCuGsR2HGb9p9mu0F1VTbnUzJiPLygIUQvVmV3UGYzYLqoBRcCNHz9P58uzWoJYNX04jWWi5SQoieK2+NcZs0AYKjYdApUJ4Fpna+jxtxbodPsyKrFKA5gyeEEIejqt4pDVaE8DN9IMCzGXPwIgNodLmpsjtlnSchRM+VuxqUGRLHGPfPfxYaqjt1aFGVnZcXZXHppBRW7iknKcJGSlSwFwcrhOjtquwOabAihJ/p/QGeJQic9cSEBgDGWngS4Akheqzc1ZAwsrkLMMHRxk8nPDt/F68v2cPLP+/GajbJkghCiCNWbXdKgxUh/Ezvn4NnDQKHsUwCIGvhCSF6Lrcb8tZC8oQuH1rX6OTjNTmcPDyB645Nx601J49oZ96eEEJ0QVW9ZPCE8De9/ysZaxA46ogJ8QR4shaeEKKnqthjLGqeNL7Lh36+Lo9qu5Objx/A5PRo/njmcCzm3v8dnhDCu5qarAgh/Efv/+tvsYHTTqynRLNEOmkKIXqq6kLjNiK1S4dprXlr2V6G9QtjUn+ja6YEd0KI7lBV75QMnhB+pvd/AvCUaEaFNM3BkwBPCNFD1RmdLwmO6dJh67Ir2JxXxdXT+kuXYCFEt3G43NQ7XIRJgCeEX+kbAZ6zHqvZRGSwlRIp0RRC9FSHGeC9s3wfIQFmzh+f7IVBCSH6qmq7E0CarAjhZ3p/gGcx1sEDiA0NpLhaAjwhRA9VX2bcdiHAq2t08vXGfM4ek0RooHwIE0J0n2q7A0CuLUL4md4f4HnWwQNIjLCRX1nv4wEJIbxBKWX29RiOWF2p8aVUQOfXrvt2UwG1jS4umpjixYEJIQ6lV1yD9tOUwZMSTSH8S+8P8DxNVgCSI4PIrbD7eEBCCC/JVEo9rJQa4euBHLa6si6XZ360Ooe06GAmp0d5aVBCiE7y/2vQfppLNKWLphB+pfcHeNZgI4OnNUmRQZTUNGB3uHw9KiFE9xsL7ABeVkotU0rNVkqF+3pQXVJXCsGdD9RyyutYuruUCyckS3MVIXzP/69B+2kq0ZQMnhD+pQ8EeDZAg7OBpMggAAoqJYsnRG+jta7WWr+ktT4WuAf4C5CvlHpDKTXIx8PrnLrSLmXwPlmTi9Zw0QQpzxTC13rFNWg/LSWaksETwp/0/gDPYgR1OOtJirQBkFch8/CE6G2UUmal1LlKqU+Ax4FHgQHAF8DXvhxbp3WhRFNrzSdrc5mSEU1qdOfn7AkhvKNXXIP205LBkwBPCH/S+9+xVk+A57CTHGlUSuRKgCdEb5QJzAMe1lovabX9I6XUcT4aU9d0IYO3s6iG3SW13DA93btjEkJ0lv9fg/YjTVaE8E99J8Bz1tMvIh6APGm0IkRvNEZrXdPeA1rrO4/2YLrM5QR7RacDvLlbCgE4ZUQ/Lw5KCNEF/n0Nakd1g5NAi4kAS+8v+BKiN+n971iLUZaJo55Ai5m4sEAp0RSid3pGKRXZdEcpFaWUevVQBymlhiql1rX6qVJK3aWUilZKfa+UyvTcerdNZX25cRsU3and524uYGxqJP0ibF4clBCiCw7rGtSTVdsdkr0Twg/1/gCvVYkmQFJkEHmyFp4QvdEYrXVF0x2tdTkw/lAHaa23a63Haa3HAROBOuAT4F7gR631YOBHz33vqSs1boMPHeDlV9azPqeS00YmeHVIQoguOaxrUE9WZXfK/Dsh/FDfCfCcRlCXHGmTOXhC9E6m1lk2pVQ0XS9DnwXs0lrvBc4D3vBsfwM4vzsG2aH6MuO2EyWa33vKM0+V8kwhepLuuAb1KDUS4Anhl7z2rvWUJZwNFGmtR7Xz+FUYbYQVUA3cqrVe3+0DseyXwYsI4qdtRWitZd0oIXqXR4GlSqkPMa4rFwMPdvE5Lgfe9fyeoLXO9/xeAHg3XdacwTt0gDd3cyED4kIYFB/q1SEJIbqkO65BPYpRoikBnhD+xpvv2teBp4E3O3g8Czhea12ulDoDeBGY2u2jsDbNwasDjBJNu8NNeZ2D6JCAbj+dEMI3tNZvKqVWAyd6Nl2otd7S2eOVUgHAucB97Ty3VkrpDo6bDcwGSEtL6/K4mx0iwKusd/DW0j1syq1i6e5SZh834PDPJYTodkd6DeqJqu1O4sNknq8Q/sZrAZ7WeqFSKv0gj7duIbwM8M5Kvc3r4LXMwQNjLTwJ8IToXbTWm5VSxYANQCmVprXe18nDzwDWaK0LPfcLlVKJWut8pVQiUNTBOV/E+IKKSZMmtRsEdsoh5uC9v3Ifj8zdQXpMMKeP7MdVU48gmBRCeMURXoN6nGop0RTCL/WUd+0vgW+88szNTVaMDF6yJ8DLrahnVHKEV04phDj6lFLnYpRIJWEEY/2BrcDITj7FFbSUZwJ8DlwHPOS5/azbBtueujKwBrdcs/azbHcZA+JC+Om3J3h1GEKIw9MN16AeR7poCuGffN5kRSl1IkaAd89B9pmtlFqllFpVXFzctRMEhhm3DdUAJEUapQayVIIQvc7fgWnADq11BkbDlGWdOVApFQKcAsxptfkh4BSlVCZwsue+99SVdVie6XJrVmaVMTWjc2vkCSF84rCvQT2Ry62pbXRJBk8IP+TTAE8pNQZ4GThPa13a0X5a6xe11pO01pPi4uK6dpLAMFAmsFcCEB0SQKDFJAGeEL2Pw3MdMSmlTFrrecCkzhyota7VWsdorStbbSvVWs/SWg/WWp+stS7z1sABo0Szg/LMrflVVDc4mTagc2vkCSF84rCvQT1Rjd0JIAGeEH7IZ+9apVQaxrfl12itd3jxRGCLgPqKpvOSHBlEXoXda6cUQvhEhVIqFFgIvK2UKgJqfTymzqsr7TCDtzzLiC2nZEiAJ0QP5t/XoP1UNzgACfCE8Edey+Appd4FlgJDlVI5SqlfKqVuUUrd4tnlz0AM8KxSap1SapW3xoItojmDB0ajFVkLT4he5zyMRcp/A3wL7ALO8emIuqKuFILaD+CW7y4lLTqYxIj25+cJIXqEbr8GKaV+rZTapJTarJS6q9X2O5RS2zzb/3Mk5+hIdXMGT+bgCeFvvNlF84pDPH4jcKO3zt/GAQGejZ+2dXEunxCix1JKmYEvtdYnAm5aFij3Hx3MwXO7NSv2lHHKcO8uwyeEOHzeuAYppUYBNwFTgEbgW6XUl0AqRjA5VmvdoJSKP9JztadaSjSF8Fs+b7JyVNgi2wR4QxLCKKlpoKhKyjSF6A201i7ArZTyz9a4Lgc0VLYb4GUW1VBR52DqAGmwIkRP5aVr0HBguda6TmvtBBYAFwK3Ag9prRs85253CZcjVW1vKtGUDJ4Q/qZvfC1ji4CSlml+Y1MjAVifU8kpI2QBTyF6iRpgo1Lqe1rNe9Fa3+m7IXVSfblx206TleVZRv+pqTL/ToierruvQZuAB5VSMUA9cCawChgCzFRKPQjYgd9prVce0cjbIRk8IfxX33jX7leiOTIpHJOCDTkVnDJCyp6E6CXm0HaZA//RvMj5gVm6lXvKSYywkRIl8++E6OG69Rqktd6qlPo3MBcjYFwHuDA+u0VjLMkwGfhAKTVAa61bH6+Umg3MBkhLS+vy+VsyeH3jo6IQvUnfeNfuF+AFB1gYkhDG+pzKgxwkhPAnWmv/m3fX5CAB3ubcSsakRKCUOsqDEkJ0hTeuQVrrV4BXAJRS/wRygGHAHE9At0Ip5QZigeL9jn0ReBFg0qRJbYK/zqjyZPDCpURTCL/TNwK8oEhw1IGzESwBAIxJiWDulkK01vLBSYheQCmVBRzwIUZrPcAHw+ma5gCvbRlmTYOT3SW1nD8+2QeDEkJ0hTeuQUqpeK11kWdpqQsxsnZu4ERgnlJqCBAAlBzuOTpSbXdiNSsCLX2jXYMQvUnfCPBskcatvRJCjYXSx6RE8sGqHLLL6kmLCfbd2IQQ3aX1gsI24BKMMqaer86zhvp+yyRsza8CjLJyIUSP541r0MeeOXgO4DatdYVS6lXgVaXUJozumtftX57ZHWoaHITZrPIluBB+qI8EeJ6mVq0CvHHNjVYqJMATohfQWpfut+lxpdRqjDU3ezZ7hXEbFNVm8+Zco4x8ZJJ/NgcVoi/xxjVIaz2znW2NwNWH+5ydVW13EhrYNz4mCtHb9I13busMnsfQfmEEWExsyKngnLFJvhmXEKLbKKUmtLprwvg23T+ucfYqMFnA2raRyua8KmJCAkgID/TRwIQQneXX16B2VNud0mBFCD/VN965zRm8iuZNVrOJEYnh0mhFiN7j0Va/O4Es4FIfjaVrGqogMBz2K4XanFfFiKRwKZESwj/47zWoHdV2hwR4QvipTr1zlVIhQL3W2u2Z0DsM+EZr7fDq6LpLOwEewNiUCD5cnYPLrTGb5AOUEP5Ma32ir8dw2OxVYGs7z67R6SazqJrjhvT8HjFCCD+/BrWj2u4kNVqmsAjhjzrbGmkhYFNKJWOsx3IN8Lq3BtXtWs/Ba2VMSiR1jS52FtX4YFBCiO6klPqnUiqy1f0opdQ/fDikzmvK4LWyo7Aah0tLgxUh/IRfX4PaISWaQvivzgZ4Smtdh9Gi91mt9SXASO8Nq5sFRRq3+wV4E/sbDQ1W7S07ygMSQnjBGVrriqY7Wuty4EzfDacL7FUtX0R5bMmTDppC+Bn/vQa1o8rukDXwhPBTnQ7wlFLHAFcBX3m2mb0zJC+w2MAcAPUVbTb3jwkmLiyQVXvKfTMuIUR3MiulmruRKKWCAP/oTtJOBm9zXiUhAWbSY0J8NCghRBf57zVoP263pqZBMnhC+KvOvnPvAu4DPtFab1ZKDQDmeW1U3U0p49vx/TJ4Sikmp0exIksyeEL0Am8DPyqlXvPcvwF4w4fj6Tx7FQSGtdm0Ka+K4YnhmGR+sBD+wn+vQfupc7jQGgnwhPBTnXrnaq0XAAsAlFImoERrfac3B9bt2gnwACb1j+brjQXkVdSTFBnUzoFCCH+gtf63Umo9cLJn09+11t/5ckyd1lDZpsmK263Zml/FJRNTfDgoIURX+PU1aD/VdqOHXmiglGgK4Y8620XzHeAWwAWsBMKVUk9orR/25uC6lS2y3QBvcno0AKv2lnOuBHhC+C2lVAYwX2v9red+kFIqXWu9x7cjOwStoaG6TYnmntJa6hpdssC5EH7Eb69B7ai2OwHJ4Anhrzo7B2+E1roKOB/4BsjA6KTpP2wRByyTADA8MYyQADMrpUxTCH/3IeBudd/l2dazNdaAdrfJ4G32NFgZIQ1WhPAn/nkNakdTBk8CPCH8U2cDPKtSyooR4H3uWf9Oe21U3tBBiabFbGJC/yhW7pEATwg/Z9FaNzbd8fwe4MPxdI7dCOZaZ/A251VhNSuGJIR1cJAQogfyz2tQO2oaXIAEeEL4q84GeC8Ae4AQYKFSqj9Q5a1BeUUHAR4Y8/C2F1ZTWe8f67YLIdpVrJQ6t+mOUuo8oMSH4+mcBs+ltE0Gr5LB8WEEWDp7iRZC9AD+eQ1qh8NpJCKtZrkGCeGPOvXO1Vo/qbVO1lqfqQ17gRO9PLbuFRRpBHj6wMTj5PQotIY1+2S5BCH82C3A/yml9imlsoF7gNk+HtOhNWfwjPl2Wmu25FXJ+ndC+B//vAa1w+k2PitZTBLgCeGPOvXOVUpFKKX+q5Ra5fl5FCOb5z9sEeBqBEf9AQ+NS4tEKVifXXH0xyWE6BZa611a62nACGC41vpYINrHwzq0/TJ4hVUNlNY2SoAnhJ/x22tQO5xuI4NnMcsyLUL4o85+NfMqUA1c6vmpAl476BE9jc3Tja6dMs3gAAtp0cFkFtYc5UEJIbwgDbhHKZUJPOfrwRxS0zXJMwdvc55xf2SydNAUwk/51zWoHU5XUwZPAjwh/FFnZ88O1Fpf1Or+X5VS67wwHu9pHeCFJx7w8OD4MHYUVh/lQQkhuoNSKh24wvPjAPoDk/yiPXmD57pjawrwjIze8ETJ4AnhL/z6GtSOphJNmYMnhH/q7Du3Xik1o+mOUmo6cGCtYytKqVeVUkVKqU0dPK6UUk8qpXYqpTYopSZ0ftiHwRZp3LazVALAkIRQskpqaXS6231cCNEzKaWWAl9hfGF1kdZ6IlDtNx+sGtp20dySV0V6TDChgdK9Tgh/4PfXoHY4XcZnIbNk8ITwS50N8G4BnlFK7VFK7QGeBm4+xDGvA6cf5PEzgMGen9l4u4yhOcBrv5PmkIQwnG5NVkmtV4chhOh2hUAYkADEebb5zzIu9ipQZggwpjVvzq+UBc6F8C/+fQ1qh6OpyYrMwRPCL3W2i+Z6rfVYYAwwRms9HjjpEMcsBA62uNx5wJuerpzLgEil1IG1k93lIHPwAAYnhAJImaYQfkZrfT4wGlgNPKCUygKilFJTfDqwzmqogsAwUIrKegfZZfWywLkQfsTvr0HtcHkyeFbpoimEX+rSO1drXaW1blr/7u4jPHcykN3qfo5nm3ccIsAbGBeKSUGmBHhC+B2tdaXW+jWt9anAVOB+4DFPq/KezV7VXJ7ZVEEgC5wL4V/8+hrUjqY5eGbJ4Anhl47kq5mj9q5XSs1uWqKhuLj48J6kKcCrr2j/YauZ9JgQdkgnTSH8mta6SGv9tNZ6OjDjkAf4WkNVc4OVgkpjanNihM2XIxJCHAG/uwa1w+HpoikZPCH805G8c4+0vjwXSG11P8Wz7cATaf2i1nqS1npSXFxce7scmiUArMEdNlkBo0xzR5Fk8IToLbTWe309hkNqlcErqLQD0E8CPCF6Bb+4BrXDJevgCeHXDhrgKaWqlVJV7fxUA0lHeO7PgWs93TSnAZVa6/wjfM6Ds0V0WKIJRlnU3tI67A6XV4chhBDNGiqbM3j5VXYCzCaigwN8PCghRF/mkHXwhPBrB+3DrbU+7IkgSql3gROAWKVUDvAXwOp53ueBr4EzgZ1AHXDD4Z6r02yRUF/e4cODE8JwuTW7i2txa83mvEoum5zm9WEJIfowexXEDQegsNJOfHggJvlQJYTwIafbjdmkUEquRUL4I68ttKS1vuIQj2vgNm+dv10hsVBX2uHDQzydNL/amMf/lu2jyu7g9FGJRARZj9YIhRDdSCn1Z6313zqxXyTwMjAKo/z8F8B24H0gHdgDXKq17vgbosPVag5efqVd5t8J4WeUUrFa65JW968GpgCbgJc8n3f8itOtJXsnhB/rW7Nng2OgtqTDhwfEhmIxKZ6Zt4t6hwutYX12xdEbnxCiu93Yyf2eAL7VWg8DxgJbgXuBH7XWg4EfPfe7l9Zt5uAVVtlJCJcATwg/M7fpF6XUn4BrMJZMOAX4r68GdSScLgnwhPBnfSvAC4mFuo4DvACLiYFxoYTZLLx941SUgjX7uv8LeyFE9+lgnnCn5worpSKA44BXALTWjVrrCoy1Ot/w7PYGcH63D95RB9oFtnC01hRUSQZPCD/UOhK6ELhQa/0GcCVwsm+GdGScLjcWc9/6iChEb+K1Es0eKSTOmIPncoC5/bLL/142lgCzicEJYQyJD2PNvoqjO0YhRFdVAJO11oX7P9DJNagygGLgNaXUWIxv3n8NJLRq/FQAJHTPcFuxe5YVDQynst6B3eGWDJ4Q/idIKTUe40tzs9a6FkBr7VBK+WXXNqdbY5UOmkL4rb719UxwjHFbV9bhLiOTIhjsWWR4Qv9I1u4rx+32u/J5IfqSN4H+HTz2TieOtwATgOe01uOBWvYrx/TMoWn3QnBE63Q2eAI8WwQFVbJEghB+Kh+jFPMRoEwplQiglIoBnL4c2OFyujRmKdEUwm/1rQAvJNa4PUiZZmvj06KotjvZXSKLnwvRU2mt/6S1XtHBY/d04ilygByt9XLP/Y8wAr7CVh/UEoGiDs5x+Ot0tsrg5XvWwJMSTSH8i9b6xP1+mjL/FRjl337H4XZjkUXOhfBbfevdG+wJ8A7SaKW1CWlRAKzZW+GlAQkhvEEp9UBn99VaFwDZSqmhnk2zgC0Ya3Ve59l2HfBZd44RMNbAA7CFU+gJ8KREUwj/p5R6QGvt0lrX+Xosh8MlJZpC+LW+FeB1MYM3IDaEiCCrNFoRwv+c28X97wDeVkptAMYB/wQeAk5RSmViNEp4qFtHCAdk8JSC+DAJ8IToBbp6DepRpERTCP/Wt5qsdDGDZzIpxqdFSoAnhP/p0icTrfU6YFI7D83qltF0pHkOXjiFVWXEhAQSYOlb37sJ0Uv5dXTkcLmxShdNIfxW33r3BkcDqtMBHsD41Cgyi2qosju8Ny4hRHeb4OsBdEpDtXEbGCaLnAvRu/jHNagDLrfGIiWaQvitvhXgmcxGkNfJEk2AyRlRaA2LMjt/jBDi6FNKDVBKfaGUKsFokPKZUmqAr8d1UPYqQEFAmCxyLoSf88trUAccbo1ZmqwI4bf63rs3OLZLGbypGTH0C7fx0eocLw5KCNEN3gE+APphLHD+IfCuT0d0KA1VEBgGJhP5lXb6RQT6ekRCiMPnf9egDjhdbqwyB08Iv9X3AryQWKgr7fTuZpPiwgnJzN9eRJFnnSohRI8UrLV+S2vt9Pz8D+jZKTF7FQSGU9/oorLeQWJEkK9HJIQ4fP53DeqAU0o0hfBrfS/AC46B2q4tRnzxxBTcGuaszfXSoIQQ3eAbpdS9Sql0pVR/pdQfgK+VUtFKqWhfD65dDVVgC29e5FxKNIXwa/53DeqA0yXr4Anhz/pWF02AkDjYs6hLhwyIC2Vi/yg+XJXNzccNQCn5VkuIHuhSz+3N+22/HNBAz5sL01gLASEUyCLnQvQG/ncN6oBk8ITwb30wwIuF+nJwu4ymK510ycQU7p2zkcU7S5kxONaLAxRCHA6tdYavx9BljjqwBlFQVQ9IBk8If+aX16AOOF1aMnhC+LG+9+4NjgU01JV16bCzxiQSExLA1a8s55pXlsvaeEL0MEopq1LqTqXUR56f25VSVl+P66AcdWANoaiqAYD4cGmyIoS/8strUAecbjcWabIihN/qewFeSIxx24WlEgDCbFa+ves4fnvKELYXVHPdKyvILqvzwgCFEIfpOWAi8KznZ6JnW8/VWAcBwZTVNRJgNhEW2PeKKoToRfzvGtQBp0tKNIXwZ30vwAv2lFd2sdEKQFxYIHfMGszHtx4LwJ3vrcXhcnfn6IQQXaSUaoqKJmutr9Na/+T5uQGY7MuxHZKnRLOsppHokACZ3yuEH/Lra1AHnG6N1dz3PiIK0Vv0vXdvSJxx24W18PaXGh3MPy8czdp9FTz+w45uGpgQ4jCt8Ny6lFIDmzZ6Fhh2+WZIneQp0SyrbSQqJMDXoxFCHB7/vQZ1wOlyY5YSTSH8Vt+rBwrxZPC6sBZee84Zm8S87UU8v2A3N80cQGSwfDgTwkeaPoX8DpinlNrtuZ8O3OCTEXVWoyeDV9dIjAR4Qvgr/70GdcDh1lilRFMIv9X3Arwgz1I0R5DBa3LFlDTmrMll6a5SzhideMTPJ4Q4LHFKqbs9v78ANLXHdQHjgXk+GdWhuBzgdkCAkcFLiQr29YiEEIfHP69BB+FySxdNIfxZ33v3mi0QFNXlJivtGZcaSWighUU7j/y5hBCHzQyEAmEYX1opz4/Fs61ncniaNFmDKauRDJ4Qfsw/r0EH4ZASTSH8Wt/L4IHRaOUwmqzsz2o2MW1AtAR4QvhWvtb6b74eRJc1GgGe02yjusFJtAR4Qvgr/7wGHYTTJSWaQvgzr2bwlFKnK6W2K6V2KqXubefxNKXUPKXUWqXUBqXUmd4cT7OQOKg9sjl4TWYMimVvaZ0smSCE7/jnpxBPBq9WG2vfSZMVIfyW165BSqlfK6U2KaU2K6Xu2u+x3yqltFIqtrvP63JrLNJFUwi/5bV3r1LKDDwDnAGMAK5QSo3Yb7c/AR9orccDl2OsG+N9ITHdUqIJMGOwcV2VLJ4QPjPL1wM4LJ4Ar8pprIMsJZpC+C2vXIOUUqOAm4ApwFjgbKXUIM9jqcCpwD5vnNshC50L4de8+fXMFGCn1nq31roReA84b799NBDu+T0CyPPieFqExEFNYbc81cC4UPqF21iUKQGeEL6gtS7z9RgOi6MegCq3EeBJiaYQ/smL16DhwHKtdZ3W2gksAC70PPYY8AeMz1HdyuXWaI00WRHCj3nz3ZsMZLe6n+PZ1toDwNVKqRzga+AOL46nRcxgqC+HmqIjfiqlFDMGx7J4Vwkud7dfZ4UQvVVjLQDlDgnwhBDt2gTMVErFKKWCgTOBVKXUeUCu1nq9N07qdLsBsMgcPCH8lq+/nrkCeF1rnYJx4XpLKXXAmJRSs5VSq5RSq4qLj7w5Cv1GGbcFG4/8uYCZg2OpqHNw78cbWJddQUlNA3tLa6lrdHbL8wsheiFPBq+80eioLgGeEKI1rfVW4N/AXOBbYB0QCPwf8OdDHX+4n52cLuPLainRFMJ/ebOLZi6Q2up+imdba78ETgfQWi9VStmAWKBNak1r/SLwIsCkSZOOPE0WP9K4LdwMg468dP60kf24dFIKn6/P48PVOc3bp2ZE8/7Nxxzx8wsheiHPHLySRitKOYgMsvp4QEKInkZr/QrwCoBS6p9AIXA+sF4pBcZnqzVKqSla64L9jj2sz07NAZ40WRHCb3nz3bsSGKyUylBKBWA0Ufl8v3324ZmcrJQaDtiAbkjRHUJIDIQlGgHeodSVwed3QEN1h7vYrGb+c/FYVvzxZB6+eAx/PXckp4/sx+q95dQ2SBZPCNEOT4lmSYMiIsgqH6aEEAdQSsV7btMw5t+9obWO11qna63TMaa/TNg/uDsSTSWaskyCEP7La58oPBOCbwe+A7ZidMvcrJT6m1LqXM9uvwVuUkqtB94FrtdaH52JbAmjoHDToffLWgBr3oSclYfcNdxm5ZJJqVx3bDqXT0nF6das3VfRZh+tNS//vJsNORXtPocQoo/wlGgW2c1SnimE6MjHSqktwBfAbVrrCm+f0OnpJyALnQvhv7y60LnW+muM5imtt/251e9bgOneHEOHEkbC7vngbATLQT5cVeUbt7Vd65I5sX8UJgUr9pQ1L6UA8NyCXfzn2+0M6xfGN7+eiafEQgjR1ziMDF5BvZmYECnPFEIcSGs98xCPp3f3OR0uTwZPumgK4bf67ru332hwO6A0E7SGjR+1H8RVNwV4XascDbNZGZEUzsqslu7JP2wp5OHvttM/JphtBdUs3tk9i60LIfyQox5QFNZqooIlgyeE6BmaOoJLF00h/FffDfASPI1WCjbBnkXw8S+NUsz9HWaABzA5PZo1+8ppdLrJKa/jrvfXMTIpnM9vm0FsaCAv/bz7CF6AEMKvNdZBQAhldU5iQiXAE0L0DA6XlGgK4e/6boAXMwjMAcY8vMWPG9sqsw/cr9ozb/kwArwp6dE0ON1szK3k0bk7cLjcPHfVRCKCrVx3TH8W7ChmR2HHzVuEEL2YoxZtDaK8rlHm4AkheoyWJit99yOiEP7Oq3PwejSzFeKGwdbPoXyPsa0y58D9qvKM2y7OwQOYnBENwBtL9vDFhjxuPm4gqdHBAFw1rT/PzN/JLW+tRimjSvTzO2YQGth3/5MI0ac46nFbgnG5pURTCNFzyDp4Qvi/vv31TMIoI7gLCIX+06Fyv2X6tD6iDF5saCAD4kL4fH0eEUFWbj1hYPNj0SEB3HbCIMwmRVxYILtLalm2q+M5eRV1jby+OKu5Nl4I4ecaa3GZgwCkRFMI0WM4ZQ6eEH6vjwd4nnl4E683ft8/g9dQ1dzp7nACPDDKNAFuP3EQEfstZHzHrMF8f/fxvH7DFAItJpYcJMB7b2U2D3yxhR+2Fh7WOIQQPYyjnkZTIADRIYE+HowQQhicni6aFumiKYTf6tvv3iGnQdoxcMztEJECDZVgr2p5vCl7F9rvsEo0AS6dnMp545K45pj+He5js5qZlB7Fkl0dn6OpG+c7y/cd1jiEED2Mo44GZQMgWko0hRA9hGTwhPB/fTvAix0Mv/gWwhMhPNnYVtWqTLNp/l2/0eCog8baLp9iQloUT1w+nkCL+aD7HTswlm0F1ZTUNBzwmNutWbW3nECLiYWZxewrrevyOIQQPUxjLXY8GTwp0RRC9BAtc/D69kdEIfyZvHubRKQat63LNJsyeIljjNvDLNPsjOmDjMXQl7ZTpplZVENlvYM7Zw3GpBTvrjSyeHtKaimvbfTamIQQXuSop64pwJMMnhCih3B4umhKBk8I/yUBXpOIFOO29VIJTWvg9Rtt3B5mmWZnjEoKJ8xmabdMc8UeozzznDFJzBoWzwcrs7lvzgZOfHQ+983Z6LUxCSG8yFFHrTuAIKuZoICDZ/iFEOJocXkyeFbJ4Anht+Td2ySsHyhz206a1fkQGAGRnvlzNUVeO73FbGJqRgyLd5bS4HTx3op9bMqtBGDVnjLiwwJJjQ7iyqlplNY28uGqHPpHB/NzZjGNTrfXxiVEX6CU2qOU2qiUWqeUWuXZFq2U+l4plem5jerWkzrqqHEHyBp4QogepWkdPFnoXAj/JQFeE5MZwpP2K9HMN+bnhcQZ971YogkwfVAM+8rqOOmRBdw7ZyM3vbmKmgYnK7PKmJwRjVKK4wbH8dCFo/nm1zP5vzOHU9voYvXe8kM+t9ZallgQ4uBO1FqP01pP8ty/F/hRaz0Y+NFzv/s01lHtCjigu64QQviSoymDJyWaQvgtCfBaC09uG+BV5RuZvRBjfpy3A7zjh8RhNinCbBb+fPYICqrs3P3+OvIq7UzubyQPTCbF5VPSGJwQxjEDY7CYFAszDz2uVxZlccIj8yTIE6LzzgPe8Pz+BnB+tz2z2wWuBuoIkPJMIUSP4mruoikfEYXwVxZfD6BHiUiB3FUt96sLIPY4sAZBQJhX5+ABDIgLZdE9JxIfZsNsUuwrq+P1JXsAmJwRfcD+YTYrE/pHsXBHMfecPuygz71mXznZZfVsyatidEqEN4YvhD/TwFyllAZe0Fq/CCRorT0TcSkAErrtbA6jE26NO4BAi3yIEkL0HI7mdfAkgyeEv5JPFq1FpBhz8Nxu46emwMjggZHF83IGDyAxIqi57v13pw0lOTKIsEALw/qFt7v/8UPi2JxXRXF1y/IKbrfm6Z8yyS5rWU5hn+f3pbu9G6QK4admaK0nAGcAtymljmv9oNZaYwSBB1BKzVZKrVJKrSou7uQ1orEpwAvEZpUMnhCi55B18ITwfxLgtRaRAm4H1BZBXQm4nca8PDDm4R2FAK+10EALr1w/iaevmtDhZOfjBhvzAxftbBnb2uxyHpm7gw9XG+WmWmv2etbOW9LOMgxC9HVa61zPbRHwCTAFKFRKJQJ4btvtsqS1flFrPUlrPSkuLq5zJ2zO4FmxWeUyLIToOZoDPOmiKYTfkndva81LJeS2LHLenMGL83qJZnuG9Qvn+CEdf2gcmRROTEgAC3e0jO3bTcb6fbuKagCorHdQbXdis5pYkVXWXH6xv8/X53Hp80txyzw90YcopUKUUmFNvwOnApuAz4HrPLtdB3zWbSf1BHjV7gBsFsngCSF6DqeUaArh9yTAay082bitzG5Z5DysKYN3dEo0u8pkUswcHMu87UXUNTrRWvPtZk+AV2wEeE3ZuzNHJ1LX6GJDTkW7z/XqoixW7ClrLucUoo9IABYppdYDK4CvtNbfAg8BpyilMoGTPfe7h6dEs9ppJVAyeEKIHsTpkhJNIfydfLJorTmDlwPV7WTw6kqMuXk9zNXT+lNR5+DtZfvYnFdFdlk98WGB7C6pxeXW7PUEbJdOSkUpWLLzwDLN7LI61mVXALAlv+poDl8In9Ja79Zaj/X8jNRaP+jZXqq1nqW1Hqy1PllrXdZtJ/Vk8KpcAQRKBk8I0YM0lWhapYumEH5L3r2tBUWBNQR2z4f17wEKQj2N80LjQbuh/tBrzh1tk9KjmT4ohhcW7uLTtbmYFPxiRgaNTjc55XXNzVbGpEQwIjG83Xl4X24wmgWaFGzJkwBPCK/yBHiVLqs0WRFC9ChNJZqy0LkQ/ksCvNaUMrJ4O7+H4u1w2oNg9qwkcZTWwjtcv541hJKaRl5ZnMXUjBgmpxvLKuwsqmFvaS1xYYEEB1g4dmAMq/eVY3e42hz/xfo8xqdFMjg+TDJ4QnhbcwZPmqwIIXoWR3OTFQnwhPBX8slif2c9Ahe8AL/dBsfc1rI9xNPopIcGeFMyojl2YAxaw+mj+jEoLhQw5uHtLa0jLToYgBmD42h0uvlpW0tDwF3FNWzJr+LsMUmMSAqXDJ4Q3uaZg1evA6VEUwjRo7jcbiwmhVIS4AnhryTA21/GcTD2cmNx89aaA7x2O6X3CPecPowxKRGcOTqRiGArsaGB7CyqIbusjv5NAd6gWPrHBPPiwt0YS3vBl+vzUQrOGp3IiMRwCqrslNY0HOxUQogj4cng1RMoGTwhRI/idGkpzxTCz8kni85qDvB67kLhY1Mj+fz2GcSFBQIwKD6ErfnV5FfZSfUEeGaT4sYZGazLrmDlnnIKKu28uXQPUzOi6RdhY0SSsaD61vxqn70OIXo9T4BXhyx0LoToWRwuLQ1WhPBzXn0HK6VOV0ptV0rtVErd28E+lyqltiilNiul3vHmeI5IUBSYrFCV6+uRdNrAuFA25laiNfSPCW7efvHEVKJDAnh2/k5ue2cN9Q4X/zh/NADDE40Ab0t+ZZvn+vNnm7j7/XVHbexC9GqeEs0GZA6eEKJncbndskSCEH7O4q0nVkqZgWeAU4AcYKVS6nOt9ZZW+wwG7gOma63LlVLx3hrPETOZISodyrJ8PZJOGxQf2vx70xw8gKAAM9dM688TP2YC8OQV45v3jQ4JIDHC1mYentaarzfmU9/owuWW0g0hjpijDrclCI1J5uAJIXoUh1tLgxUh/Jw3vzqeAuz0rDHVCLwHnLffPjcBz2itywG01j13ghtAdIZfBXgD41oFeK0yeADXHtOf6JAAfjkjg3PHJrV5bERieJsSzX1ldZTUNFLb6GpePF0IcQQcdbgsxntSMnhCiJ7E6XJjMcl1SQh/5s13cDKQ3ep+jmdba0OAIUqpxUqpZUqp09t7IqXUbKXUKqXUquJiH3axjB4A5VngaU7S0zVl5YKsZuJCA9s8FhMayNL7TuL+s0cccNyIpHB2Ftc0L6Wwem/L2n9Ni6ELIY5AYx1usw0Am2TwhBA9iNOtpURTCD/n669oLMBg4ATgCuAlpVTk/jtprV/UWk/SWk+Ki4s7uiNsLXoANNb02KUS9pcYYSM4wExadHC77Y47Kg0bkRiOy63Z6lkPb/XecsICLYQFWljfiQDP7nCxck9Zu4+V1DSQV1Hf+RchRG/kqMNpNjr1BkqTFSFED+J0SYmmEP7OmwFeLpDa6n6KZ1trOcDnWmuH1joL2IER8PVM0QOM27Ldvh1HJymlmNg/irGpEV06bkpGNBaT4qsN+YAR4I1Li2RMagTrcyoOefzLP+/mkueXsqHVvhV1jfzrm63M+PdPXPzcEtxu/8iCCuEVjjqcngxeoMXX37MJIUQLp9uNRbpoCuHXvPkOXgkMVkplKKUCgMuBz/fb51OM7B1KqViMks2eGz35WYAH8Mp1k/nnBaO7dExMaCAnD09gztpcymsb2V5YbQSKKZFsy69uLt3syNwthQC8vngPAPWNLs55ehEvLtzNoPhQ8irtbC2QxdRFH+aox2EyMniyTIIQoieRDJ4Q/s9rAZ7W2gncDnwHbAU+0FpvVkr9TSl1rme374BSpdQWYB7we611qbfGdMQiUkGZ/SrAC7CYDuubuMsmp1JW28ij329Ha5iQFsXY1Eicbs2W/JbgzOFy88Gq7ObmKwWVdjbkVBIZbOWLDXkUVdt5YeEussvqeeOGKbx2/RQA5m/3jzJXIbyisZZGk2cOnjRZEUL0IDIHTwj/59VPFlrrr7XWQ7TWA7XWD3q2/Vlr/bnnd621vltrPUJrPVpr/Z43x3PELAEQmepXAd7hOm5IHP3Cbby9fB9Kwbi0SMalRgI0z8PblFvJ+c8s5g8fbeDuD9ajteaHrUb27uGLx+JwaR79bgfPL9jFWaMTOW5IHHFhgYxKDmfBfgGe2635YUshO4ukS6foAxx1rQI8yeAJIXoOh3TRFMLvyTu4q6IH9IkAz2xSXDQxGa1haEIY4TYrCeE2+oXbWJddwSuLsjjvmcUUVjVwycQU1mdXMH9HMd9vKSQ9JpiTh8dz0rB43l+VjVvDvWcMa37u44fEsXpfOZX1DgAW7ijm7KcWceObq7jt7TUyP0/0fo56GpTMwRNC9Dwut8YqGTwh/Jp8suiqKP9aC+9IXDLR6JEzPi2qedvY1Ai+WJ/H37/cwqxh8fx49/E8eMFokiODePjb7SzdVcopIxJQSvGL6RkAzJ45gNRWC62fMDQel1uzeGcJ32zM59pXV1Dd4OCqqWlsL6xunsMnRK/VWEuDMpYukQyeEKIncbo0ZpmDJ4Rfs/h6AH4negDYK6CuDIKjfT0ar0qPDeG5qyYwOqWlC+cxA2KYu6WQ3582lFuPH4jJ80fg9pMGcd+cjQCcPDwBgBmDY3l/9jQm9I9q87zjUyMJt1n437K9bMipZGxqJO/PnobFpFi8s4Sn52Vy2siEdpd26IyiajtmpYjZb+0/IXoMRz12AjGbFFbpVieE6EEcbjehVvl4KIQ/k08WXdXcSbNvZPHOGJ1ISlRL9u2aY9JZft8sbjtxUHNwB3DRhBSSI4OIDLYysVVAN3VAzAEfYC1mEzMHx7FkVykBFhPPXTUBm9WMxWziVycOYlNuFfO3F7OnpJZlu0vRXVhYvqbByQXPLGH2W6uP4FUL4WX3ZPFj4o1SnimE6HFcbumiKYS/k69ouqr1UgkpE307Fh8wmxTx4bYDtgdYTDx39QSq7c5Ode08bVQ/vttcwJOXjycpMqh5+wXjk3nih0x++cZKmqbiPX/1BE4fldip8T387TZyK+rJragnq6SWjNiQzr0wIY4maxA1LquUZwohehyHS2OWJitC+DV5B3dVVDqg+kSjla4akxLJ9EGxndr3nDGJrP7TKcwY3HZ/q9nEgxeM4qIJKfzj/FEMjg/loW+20eh0H/I5V+0p481lezlrdCJKwWfrcg/rdQhxNDQ43dgkgyeE6GGcLrc0WRHCz8mni66y2iA8WQK8I6SUIiLY2u5jJwyN5+FLxnL1tP7831nD2VNax1vL9rbZp7SmoXm5BjBKSu6ds5GkiCD+c/EYpmZE89m6vC6VdwpxNNkdLsngCSF6HJdbH9b6uUKInkPewYcjbihkLwcJHrzuhCFxzBwcy5M/ZlJR1wgYH4yvfGk5Fz+/hOLqBgAW7yxhZ1ENfzh9KCGBFs4fl0xWSS0bciq7dTx7S2u7NWgsrm5oXi5C9C12h5sAyeAJIXoYh9stc/CE8HPy6eJwjL4EyrNg72Jfj6TXU0rxf2cOp9ru4PrXVlJUbeevX2xme2E1Dpfmk7U5AMxZk0O4zcJpI/sBRnOYALOJT7uxTHNrfhUnPDKfz9fnHXS/uZsLqG90deo5r355OffN2dAdw+sWOeV1bMrt3qBYtK/BKRk8IUTP43RJkxUh/J0EeIdjxHkQGA5r3vT1SPqE4YnhPHf1RLYXVHPaYwt5d0U2t54wkIn9o3hvZTY1DU6+21zI2WOTmj8wRwRZOXFYHB+szOaejzbwwapsPl2by4erslm7rxzXYSym/u2mArSGbzYWdLjP1vwqZr+1mpd+PnQJb055HdsLq1m9t7zN9uyyOp+Vlv7p003c8j/pQHo0NDjc2KxyCRZC9CxOKdEUwu/JO/hwBAQbWbwtn0F9+aH3F0fstJH9+PCWY7BZzUxJj+buU4Zw2eRUdhfX8uBXW6h3uLhwfHKbY+49YzgzBsfy7eYC/vDRBu56fx2//2gDFzy7hPF/m8vLnQjCWvthq7EA+8LMYuyO9jN0qzzB2ufrDz3/b/HOEgAKqxooqrYDMH97ETP/M48731tHtf3olm42Ot2syCojp7y+w9cnuo9dMnhCiB7I6ZISTSH8nQR4h2videC0w8aPfD2SPmNUcgQLfn8ib980FavZxFmjEwkNtPDuimzSooPbrL8HkBEbwgvXTGLN/afw02+PZ97vTmDB70/gqSvGMyQhjEfmbqemwdmpc+dX1rM5r4pjBsRQ1+hi6a7Sdvdb4wnwdhbVsDW/us1jLrcms7Bl26KdpTSt5d5UFrlwRwkWk+Lrjfmc89QiPlqd0zzP0Ns25FRQ5ykt3Vtad1TO2ZfZHS5ZB08I0eM4XRqLdNEUwq/Jp4vDlTjW+Fn1GrikScbREmAxNS+cHhJo4ZyxSQBcOCEZpdr/g2Q2KQbEhZIRG0L/mBDOGZvEvWcMw+5wM3dzx+WWrf2wtQiA+88eQUiAme892bz9rdpbxtSMaCwm1Wauntaa++Zs4JTHFrJwRzFut2bJzhJOHp6AUrAptwqAlXvKmJQexbs3TcOlNb/7cD2TH/yBh77Z1rl/oE74cWshN7y24oClJ5a0ClqzSmq67XyifQ1Ot2TwhBA9jtOtm//OCiH8k7yDj8Sxd0LRZvjwenAenSyLaOsX09MZkxLBpZNSu3TcxP5RpEQF8claowlLaU0Dv/9wPbe9vYa7P1jHLW+t5tLnl3L/p5uwO1z8sKWQjNgQhieGcfzQOH7cWoh7v3l8RVV2ssvqOWVEAjMGx/JFqzLNFxfu5oNVOVjNisd+2MHWgipKaxs5bWQ/MmJD2JhbSU2Dk815lUxJj2ZKRjQLf38iX905g5OHx/Pq4qw23TYLq+wHfX2fr89j+kM/sXpvWZvtW/KquP2dtczbXszG3Io2jy3ZVUJ6TDAAWSWSwfM2u8OFzSIBnhCiZ3G63ZilRFMIvyYB3pEYfTGc/m/Y9iW8fzU0evlDsSzLcIDBCWF8fvsMkiKDunScUorzxyWzeGcJRdV27puzkU/X5bK9sJrlu8vYVVyDW2veWraXa15ZztJdpZw8PB6lFCcPT6CwqoENuZUUVdmprDMCrzX7jPLMCf2jOHdsErkV9Xy2Lo8nf8zkoW+3cdboRB44dyRr91U0Z+RmDIplVFIEm3IrWb23HLeGKRkxzWMcmRTBHScNptHp5qsN+QB8tSGfqf/8kY9X5xzwurTWPP1TJne+u5bcinoe+mZbc5BZWtPATW+uIiTQAsCqPS3zR+0OF2v2VnDKiARiQwPYU1LbpX9P0XV2abIihOhhtNY4XBqrBHhC+DX5dHGkpt0CZz8Omd/D62dBdfule0ds5w/wrxSoKfLO8/dB549Pwq3htrfXMHdLIX84bRg/3H08i+89ie/vPp6Pbj2WJ68Yz/rsShpdbmYNTwDgpGHxmE2KC59dzJR//sgZTyykpsHJ6r3lBFhMjEwK59SR/Qi0mLjr/XX89/sdzBgUyyOXjOWSiakkRwbxc2YJg+JD6RdhY3RyBPmVdr7dlI/ZpBifFtlmnGNSIhgUH8rHa3Jwutw8Onc7AA9+vZXy2sY2+/5v2V4embuD88cl8aezhrNyTzk/Z5Zgd7i4+a3VlNQ08Or1k0iPCW5uCAOwem85jS43xw6MJSM2hKzSQwd4tQ1OPlyVfUAmU3SO3eEiUEo0hRA9SNPlXLpoCuHfLL4eQK8w6QYIjYePb4SXToLUKVC4GdKmwblPds85dv4IjTWQvQKGn909z9nHDYoPY1RyOCv3lHPMgBh+OSPjgH3OHZtEv3AbP20rYpKniUtkcAB/PnsE+8rqiAyy8t8fdvDY9ztYu6+cMckRBFrMBFrgv5eOo7S2gVnDE0hulWG8/aRB3DdnIzMGxQIwMjkcgDlrchmVHNGcYWuilOKiCSn8+9ttPPFjJrtLavnNyUN48qdMHvpmG/++eAxgfPP61rK9jE2J4LHLxtHocvPqoiwe/X4H8WGBrN5XzjNXTmBMSiQT+0czf3sRWmuUUizZVYLZpJicEU36xhAW7Cg+5L/fJ2tz+dOnmwgPsjavPyg6R2ttzMGTJitCiINQSv0auAlQwEta68eVUg8D5wCNwC7gBq11RXecz+Ey5mZLiaYQ/k0+XXSXYWfBL74Fqw1yVoEyGevkle/t/HO43bDuXWioPvCxXM/aZHlru2e8AoCrp/YnNjSQRy4di6mDP2hTMqK594xhbb7RvO7YdO4/ewR3zBrMFVPSeG1xFhtyKpmY3tLJ86wxiVx7THqb4A7g4okp/GJ6BldPSwOM7qBgNN2Ykt62E2iTC8YnY1Lw1E87GZUczp2zBnHjjAzeX5XNyj3GPLuNuZXsKKzh0smpKKUItJi5Y9Zg1mdX8P2WQh44ZyRnjk4EYFJ6FKW1jWR5SjEX7yxlbEoEoYEW0mNDKKpuOGSH0abOn+8s33fAYy639tlafv6gwdPgRjJ4QoiOKKVGYQR3U4CxwNlKqUHA98AorfUYYAdwX3ed0+lJ4Vmli6YQfk0CvO6UOBbuWA2/2QhXfQhKdW0x9K2fwae3wIJ/t93uckL+BuN3CfC61eVT0lj5x1kHBGFdcc9pw4gKDsDp1kxMaz9Aa81qNvHnc0YwKD4MgHCbtbm5yeT06HaP6RdhY7on4/fbU4ailOLXJw8mOTKI++ZspMHp4qPVOQRaTJw9Jqn5uIsnpjBzcCy/P20o1x2b3ry9KRu5am85m3IrWZdd0VyCmhEbAnDIeXib8jxLO2QWk13Wdv7pa4uzuPj5pZ1ehqKvaXAYAZ500RRCHMRwYLnWuk5r7QQWABdqred67gMsA1K664QulxHgWUzy8VAIfyYlmt4SmQqDToG1b8EJ94LZeuA+2SuMOXXDzzYaqCx63Ni+8lWYcTcEez7sF28FZz0ERRkBntbQwZIAous6Wl6hsyKCrfztvFH846stTMloP0A7lJHJEewpreswwAP4/WlDmdQ/mhOGxgEQHGDhwQtGcf1rK3n8h0w+W5fHaSP7ERHU8v+a1WzirV9OPeC5BsaFEhFkZfWecn7aWkSYzcLV0/oDkB7jCfBKa5uzi/trdLrZXlDNuWOT+HJDHu+t3MfvTxsGQHF1A0/8kMnE9ChCA+US0x6701hvUNbBEz2dw+EgJycHu/3gnXt7A5vNRkpKClZrO3+vfWMT8KBSKgaoB84EVu23zy+A97vrhA638eWTrIMnhH+TT1/eNOkGePdy2PEtDD+n7WO1pfDOZVBfDtfMAWWG/HUw9VZY/hwsew5O+qOxb1N55rirYOnTULEPovof1ZciDu6sMYmcNSbxsI//xfR0RiVFEBUS0OE+Y1IiGZMS2WbbCUPjOW9cEs/N3wUYGbvOMJkUE/tHMXdLAeV1Du6cNbg5MEyPNbKJ+2fwfs4sJjHCxqD4MDKLqnG4NKeMSKCu0cn7K3O46+QhWM0mHv5uG/UOF/efPaKzL79HUEqZMT485Wqtz1ZKZQDvATHAauAarXXjwZ6jsySDJ/xFTk4OYWFhpKenH/GXYT2Z1prS0lJycnLIyDhwPrYvaK23KqX+DcwFaoF1gKvpcaXUHwEn8HZ7xyulZgOzAdLS0jp1Tqdk8IToFeQd7E2DToHwZKPk8r2r4PHRsPZ/xmPf3w8NVRCdYTRn+envEBIPJz8Aw86GFS+A3SiBI3cN2CJh5IXG/fx1Pngxwpsm9o/m1hMGHtaxfz57BFHBVhJblXF2xqT0KMrrHIQGWvjF9PTm7cEBFhLCA9ushbc5r5IbXlvJ/83ZZNz3LMw+KjmCK6emUVLTwN0frOfj1Tl8uDqHX8zI+P/27js8qjL9//j7TiGhREoQBIIElV1pEiAgiiiKhW4vKMrau+gqK+xa1u8Wy8/li3wXVFTUXcUCrh0VRVgrAiJiBBVQlNBBQUCCSXh+fzwnJIGEJJDJzBk+r+vKlZkzZ87c80Dua+55GoceWG+v3k8UDQcWlbh/L/C/zrnDgJ+AS6vrhYp68LRNgsS6vLw80tPT47q4Az+SIz09PeZ6Kp1zjznnujrnjsXnoW8AzOx3wEDgAlfOhGfn3ATnXLZzLvvAAw+s1OsVqAdPJC7o00UkJSb5XrzVX8DK+b5Ie/laePFqmP+03yh9yLOQnwe5c/yWC8mpcOwtvrj7ZIK/zsp50KILHNQBEpI1D09KSa+Xwr8uOZKHL+xapZXPioaDDju6FQ3qlO45bN24Lt+t3wL4VdVGTF5AwQ7HnO9/ZNWmbeSs3ES9lCRaNarDcb9pwiU9W/POwjXcPPlz0uumcP0Jh1XfG6wBZpYBDAAeDe4bcAIwJTjlSeC06nq9vPygwNNG5xIC8V7cFYnF92lmTYLfBwNnAJPMrC/wB2Cwc65aN+At7sGLvbYQkcqLaIFnZn3N7GszW2JmI/dw3plm5swsO5LxREXPm+CG+XBTDlw2HTqeA59Pggat4NgRcOBv4fSHoNUxkB10EDTv7Hvx3v8HrF0EaxZC8y6QlAJN26nAk910zKi/2/DNimS3asgD52Vx3fFtdnusdeO6LNvgPzc8OHMpC1f9zMh+h+Oc32g9Z8Um2jU/gIQEIzHBuGNQOz75Ux/+fnpHHr6wC2mpMTOHpbLG4D8w7QjupwMbSyxkkAu0qK4Xy8svWkVT37GJVGTjxo2MHz++ys/r378/GzdurP6AatYLZrYQeBW4NtgO4Z9AGvC2mc03s4eq68WKe/CUm0TCLGJz8IL5LOOAk/AfjuaY2SvOuYW7nJeGHxr1SaRiiarEJD8MEyCpFpz+MBx8JGR0h1p+rhPtBvufkvrdB+OOhEnngCv0PXjgi78vX9RCK7LPzIxTs8quWTLT6/Lj1l9pd8eb/PJrIQOPaMZVxx3KawtW8tL8FSxdu5Uh3UvP6TggNZnzj6zcPI9YYmYDgbXOuU/NrPdePL/K81y27xyiqR48kYoUFXjXXHNNqeMFBQUkJZX/MWbq1KmRDi3inHO9yjgWsSESO7dJUA+eSKhF8iua7sAS59y3wcIEzwKnlnHeX/BzXWJr4HukJCRAt8ug2RF7Pq9+CzjxTr+gCvgePPAFXt4m+Om74nMXTIb37o9MvLJf6t+xGedmt2RI94O5bUBb7j6jIwCDjmhOzoqf2ZZfSIdgg/Y40BMYbGbL8HnqBOABoIGZFX16zABWlPXkvZnnUtSDpyGaIhUbOXIkS5cuJSsri27dutGrVy8GDx5Mu3Z+IafTTjuNrl270r59eyZMmLDzeZmZmaxfv55ly5bRtm1bLr/8ctq3b8/JJ5/Mtm3bovV2YlrREE1tdC4SbpFcRbMFsLzE/Vyg1HrtZtYFaOmce93MRkQwlnDKvhQWPA9b1sABwQqNzTv73+/+za+yOftRmDXOHzv0hOKePpF90LJRHe49a/cvIQYc0Yy73/gKoNwtFMLGOTeKYKPgoAfvFufcBWY2GTgLX/QNA16urtfcOQdPQzQlRO569UsWrvy5Wq/ZrvkB3Dmo/R7Pueeee8jJyWH+/PnMnDmTAQMGkJOTs3O1y4kTJ9KoUSO2bdtGt27dOPPMM0lPTy91jcWLF/PMM8/wyCOPcM455/DCCy8wdOjQan0v8SC/0H/5lKwhmiKhFrW/YDNLAEYDN1fi3CvMbK6ZzV23bl3kg4sVCQkwdAr87vXiY007QvcrYOHLMLazL+6yL4WU+vDhA5GLZd03sOrzyF1fQiGjYR26tmpIanIChwQbosexW4Hfm9kS/Jy8x6rrwkUFXop68ESqrHv37qW2Mhg7diydOnWiR48eLF++nMWLF+/2nNatW5OVlQVA165dWbZsWQ1FGy6FwRBNraIpEm6R7MFbAbQscX/XIU5pQAdgZrBy1UHAK2Y22DlXaiNP59wEYAJAdnZ2mcsBx63U+v6nSEIC9P9/0PNGmPOoX6Sl03mQeoAv8DYshfS9W26/XNt+gicHwtb10PduX2Bq/t9+6/aB7Vi2fmtcTsJ3zs0EZga3v8UPNa922wuK9sGLvzaU+FVRT1tNqVu3+MulmTNn8s477/Dxxx9Tp04devfuXeZWBykpKTtvJyYmaohmOfI1RFMkLkTy08UcoI2ZtTazWsB5wCtFDzrnNjnnGjvnMp1zmcAs/JK/c8u+nJRSNEev03n+/pFX+y0UPvq/6n+tt++Ereug1dHwxh/glethR2HFz5O4lNWyAad1rrYFJfdLO3vwtMiKSIXS0tLYvHlzmY9t2rSJhg0bUqdOHb766itmzZpVw9HFl6JVNDVEUyTcIvYXHCwvfh3wFn7z4Oedc1+a2f+Y2eA9P1uqLK0pZA2B+ZPg55XVd91lH8C8J+Goa+GiV6DXLfDZv+GdP1ffa0TKuq9h9iN+xVGRGFLUg5eSpA9RIhVJT0+nZ8+edOjQgREjSk/X79u3LwUFBbRt25aRI0fSo0ePKEUZH4pW0dQ+eCLhFskhmjjnpgJTdzl2Rznn9o5kLPuFY26Cz5+FqSPgvKf9seWz/UqcrY6GX7fC+6Nh8Vt+g/WWFYw+y9sEr9zg9+zr/Uc/PLTP7f74R2OhcRvoclHk39feev1mWPY+NMyENidFOxqRnbbnF2KmAk+ksiZNmlTm8ZSUFN54440yHyuaZ9e4cWNycnJ2Hr/llluqPb54UbzRuXKTSJhFtMCTGtYwE3qP9L1ri171Bd1LV4PbUXxOUm2//96LV8JVH0CturB5NVgC1GtSfF5hAUz+HWz8Hi56uXjPPoC+98CPS+G1m/xrtj62dBxfvwkz/gZnP1H98wHLUljg9xssaflsX9wlJMFbf4JDjt/9nOqQ97Nv56JVTkUqIa9gBylJCZjmsopIDCkoLNroXLlJJMz0FU28Oeo6v9Lmy9fCi1dB5jFw2XQ45W444Xa48Qs4+0n48Vt4+w6Y+ziM7QLjj4KVn/lrOAdv3gpL34UBo/01SkpMCoq3w+C5oX6FzSKF+f65qxfA02fB1g2Rfb9fTIH7WsO3M0sff3801G4Epz0I67+GeU/441vWVTx/cOMPkF+JCfjOwaRz4dETS19zzULNUZQ9yssv1CbnIhJzdm50rgJPJNTUgxdvEpNh8APw6ElwWB849ylIrg0Z2cXn1DsQelwDs8b7+62PhR+XwRODoNdNkPMirPkCjr4eug4r+3VS68P5z8EjfWDS2b6IrNsY5v0LfloGx90KH4yBZ4f4uXvJqfv2vn79BdYugrVfwoGH++Gl65fAq8Ph1y3w4tVwzUdQuyGszoFv3oDj/wQdz/YxTf8f+Hi873nsNMQXfrv2nqz9CmbeDQtfgqyhcFqwv+C2jfDNW9A8Cxr/pvh5C56DHz7yt5e9D4f09kXyhN5w4l1wzI2Vf3+rFviiuHOwL1P+NphyKRx5hb9uWQrz4akzoMsw6HhW+dee+zi06ArNdt/XTqIjL79QwzNFJOYULbKSqCGaEkL5+fnk5uaWuZJumKWmppKRkUFycnKln6MCLx616Op76uo1LX9YYp87/AbqLXtAt8tgy2r49+m+EDqwLZw6Djqdv+fXaZjp5/I9ORAmHO8Ly//e56/ZexQ0aQeTh8G022DA/aWfW5jvi6GMbr5gKiyAZ871xWGTdtB2MBxxtj938xp46BjYurb4+VlDYfXnvqA992n/Oq/fDF0vhnfuhFr1oPvl/tp974HnLvBzBpt1gs+fgYN7QNffFV/vh0/gif6QlOo3k1/wrB/u2qAlvHGrvw9Qt4mf69jpPJh2uz93/RJYMNkXYrMf8efNGg89roak4qW5y/XzSl+obV0H9VvCIcfBJw/D16/Dr5uLC7xVC+DbGXD0Df59ffMWfPce/PQ9tD8dEsroEfphFrx2o7/uNR9DSlrF8UjEbS/YoR48EYk5+YVaZEXCKzc3l7S0NDIzM+NmCoRzjg0bNpCbm1tq/8+K6CuaeFW/xZ7nnCXXhrMm+h6ihAQ4oDlcOg0umeYLgc5D/fGKtOzmN2JPSPAF4pbVfvsGM2h/mu8pnPMIfDOt9PNm/B0eO8kXMgAfjoEl7/hCZOVn8J/LYc2X/rFZ4+GX9XDGo3DdXF9gLXgWVn8Bpz8MbQfCcSMh5wVfbP60DAb8w/fmARzUAYZ/7nscz3zUz8eb+ofijdt3FMLUW3xBPPxzOOdffvjlrPE+lgXPQvYlMPif0LQ9vDUKxmb5gmzAaGh3qt94flOuHzJ60BG+eF7wXMXtV5gPUy7xPZQHtIA3R8KWtX6IaVLtoIBb5uN57SY/rLZoOOq8J/0cw43f+zmXZXn3r763dVMuTP9LxfFIjcjLLyRVm5yLSIwp3DlEUx8PJXzy8vJIT0+Pm+IOwMxIT0+vcq+k/oKlWGp9OPjIqm9inpHtF2zJvhS6X+lX7CzS505o0h5evsbPfwP/+5OHfG/ZW3+EWQ/CzHt8L9RFL8GV70HKAb4g2bYR5jwG7U7zPXqN28CJf/avd/5k+M0p/prH3OQ3fx80Fm76snh/wF0lJPoir046PH22L+Dm/csPjzz5L36YaYOD/dDOT5+E12/x5574Z+hyIVz4op/DWCvNF68tuvi4ft3sF6Up3A5nTICDOvo9CXfs8L2T27fsHsvGH/xcyR8+hsFjod+9sHYhTOzrr3feU4D5rS+++y+smAuW6IvjTbm+ID76BmjY2r/WrttBfPeeHzrae5TfnH72BN9TuSvn4M0/wqLXKv9vLvskL3+HNjkXkZhTtMiKNjqXsIqn4q7I3rwnfcKQ6pGSBgNHQ//7Sh9PTvUFVd7P8Oz5/veHY6AgDy5+w89pe3Mk1G4A/f/hn1OnERwz3M+je+kaX+z0+n3p6zZpC785ufh+YhKcdJefM5hce8+x1m3sC7XEWvD4AD+ks9Ux0P6M4nN6Dof8rb6o6j3KF79Q3DN5Uw6c8jd/LLMXpDWD3Dn+dpO2cPRwWP+Nn4N4fxu4OwMe6uXnDL54FTw5CB7oBF9MhmN+7+fQHT7QD8f8cSl0vhAOOxEOPR4+exreux/qHeRfM3c2/OdKvzpq12F+j8IVc2F5ieLNOXj3b5DW3A9b7XMH1M+AKRf7ns+SvpgMs8b5+YdSI/wcPPXgiVTGxo0bGT9+/F49d8yYMfzyyy/VHFH8KhqiqUVWRKoulnKVCjyJvKbtfJG3cp4vbOY8Ckec63u/hjwDLbL9oid104ufc+RVfsjk169Dm5N9j1h1anK4XximcRvfu9bv3tI9l02DeYBNO/gCaVdmxecnJEKHM/3t7lf43+1Pg0aH+o3iD+sDx47wReLCV2DZh7DtJ1/YDV/gh7QWXXPAaN82J9zmj3UeCj/n+p64o6/3vaQNDobvP/BDTRtmQtYFfjjqtNt9j6dz8N97YfksOPZmX2Sn1IPzJvnHHjuluLdu20++FzUpFdbk+IVsJOK2F+wgRT14IpUSSx+a4l3REM0kDdEUqbJYylVaZEVqRrvBfm7b88N8z9OxI/zxRq3h8um7n1+rrl/k5LXfQ6+bIxNTWlO45C3YvMrHsauzJvpYK7N/Xs/hviA9fIC/n5gMV73v9xesqEexpPRD/RDPIr8dAKkN/HWyL4akWn6F0pevLV4kplYd6H+/7xl8tI/f1uLTJ/wiOSWL02ZHwBUz4JkhftGZQ/v45/6ywQ93nXS2n0PY5/bKx1tSfp6fT/j9B1Dwq/83/MPSvbtWnMvLL+TAtEoswCMijBw5kqVLl5KVlcVJJ51EkyZNeP7559m+fTunn346d911F1u3buWcc84hNzeXwsJCbr/9dtasWcPKlSs5/vjjady4MTNmzIj2W4l5+cEqmlpkRaTqYilXqcCTmnP4ABj2ql8NszIboHe9GA47ya9kGSnJqWUXd+CLtMqq1wR63lD6WK26ex9XkeRUv5BMYnLx9bIu8ENbM7oVn9fxLL9Iy3NDfXHX/Uq/euiuC+WkHQQXT/WL23w01hd3Pa6BNidC6+MgZ4rvPXQ7YPHb8M2b8P2HfvXQpu18obkp12/unpLmr9f9Smh0CLx4he9x7XyhH3JbSyt2lkeraEoovTFy9yHe++qgjtDvnj2ecs8995CTk8P8+fOZNm0aU6ZMYfbs2TjnGDx4MO+99x7r1q2jefPmvP766wBs2rSJ+vXrM3r0aGbMmEHjxo2rN+44VaBVNCVO3PXqlyxc+XO1XrNd8wO4c1D7ch+PpVylAk9qVqujKn+uWWSLu7D4bd/S9838PoC7anWUX6BmxafQdlD5i+Uk1/Z79HW7DJZOhzbBQjUdz/I9g9/OgI/H+UVcatXzi+Zs+8kv9gJ+Ll9Kmu/5XPw2zJ3ot+ZY/gmc/Fc/lFT2SPvgieydadOmMW3aNDp37gzAli1bWLx4Mb169eLmm2/m1ltvZeDAgfTq1SvKkYZT0UbnWmRFZN9EO1epwBOJJ/Vb+J/KSKnnt3go0naQHxL7dLD/YP/7octFxXv5Fa3SWbJw3LzGL84y70nocS0cdd2+v4f9gO/BU4EnIVNBT1tNcM4xatQorrzyyt0emzdvHlOnTuW2226jT58+3HHHHVGIMNwKCneQlGBxuRKh7F/21NNWE6Kdq/QJQ0S81Pp+rmStujD0Bb9RfMmN2ksuLFMkrSkMGgO3fg99/171LTb2U9oHT6Ty0tLS2Lx5MwCnnHIKEydOZMsWv/XMihUrWLt2LStXrqROnToMHTqUESNGMG/evN2eKxUr2OFI0gqaInsllnKVevBEpNjgf8KOfD8EsypSD4hMPHHqhauPpn7tKszxFNmPpaen07NnTzp06EC/fv04//zzOeooP9y/Xr16PPXUUyxZsoQRI0aQkJBAcnIyDz74IABXXHEFffv2pXnz5lpkpRIu6dmawZ2aRzsMkVCKpVxlbtfNkWNcdna2mzt3brTDEJFqZGafOueyox3HvlJ+knizaNEi2rZtG+0wakxZ7zce8pNyk+wP4jlfVTU3aYimiIiIiIhInFCBJyIiIiIiEidU4ImIiIiIiMQJFXgiIiJSrrDN1d9b+8v7FIln8fh3vDfvSQWeiIiIlCk1NZUNGzbE5YemkpxzbNiwgdTU1GiHIiJ7KR7z1d7mJm2TICIiImXKyMggNzeXdevWRTuUiEtNTSUjIyPaYYjIXorXfLU3uUkFnoiIiJQpOTmZ1q1bRzsMEZEKKV8V0xBNERERERGROKECT0REREREJE6owBMREREREYkTFraVZsxsHfB9JU9vDKyPYDiREsa4FXPNiNeYWznnDqyJYCJpP8hPirlmhDFmCGfc+0V+Um6KWWGMWzHXjH3KTaEr8KrCzOY657KjHUdVhTFuxVwzFHP8CGO7KOaaEcaYIZxxhzHmSAtjm4QxZghn3Iq5ZuxrzBqiKSIiIiIiEidU4ImIiIiIiMSJeC/wJkQ7gL0UxrgVc81QzPEjjO2imGtGGGOGcMYdxpgjLYxtEsaYIZxxK+aasU8xx/UcPBERERERkf1JvPfgiYiIiIiI7DfitsAzs75m9rWZLTGzkdGOpyxm1tLMZpjZQjP70syGB8cbmdnbZrY4+N0w2rHuyswSzewzM3stuN/azD4J2vs5M6sV7RhLMrMGZjbFzL4ys0VmdlRI2vmm4P9Gjpk9Y2apsdbWZjbRzNaaWU6JY2W2rXljg9gXmFmX6EUeHcpNkRW23AThzE9hyE1BnMpPVaD8FFlhy0/KTRGNM6K5KS4LPDNLBMYB/YB2wBAzaxfdqMpUANzsnGsH9ACuDeIcCUx3zrUBpgf3Y81wYFGJ+/cC/+ucOwz4Cbg0KlGV7wHgTefc4UAnfOwx3c5m1gK4Ach2znUAEoHziL22fgLou8ux8tq2H9Am+LkCeLCGYowJyk01Imy5CUKWn0KUm0D5qdKUn2pE2PKTclPkPEEkc5NzLu5+gKOAt0rcHwWMinZclYj7ZeAk4GugWXCsGfB1tGPbJc6M4D/eCcBrgOE3Y0wqq/2j/QPUB74jmHNa4nist3MLYDnQCEgK2vqUWGxrIBPIqahtgYeBIWWdtz/8KDdFPM5Q5aYgptDlpzDlpiAW5afKtZPyU2TjDFV+Um6qkXgjlpvisgeP4n/gIrnBsZhlZplAZ+AToKlzblXw0GqgabTiKscY4A/AjuB+OrDROVcQ3I+19m4NrAMeD4ZGPGpmdYnxdnbOrQDuB34AVgGbgE+J7bYuUl7bhu5vs5qF7v0rN0Vc6PJTyHMTKD+VJ3TvX/kpopSbal615aZ4LfBCxczqAS8ANzrnfi75mPOleswsdWpmA4G1zrlPox1LFSQBXYAHnXOdga3sMqQg1toZIBh7fSo+yTYH6rJ7d37Mi8W2lcpRbqoRoctP8ZKbIPbaVipP+SnilJuiaF/bNl4LvBVAyxL3M4JjMcfMkvEJ6mnn3H+Cw2vMrFnweDNgbbTiK0NPYLCZLQOexQ81eABoYGZJwTmx1t65QK5z7pPg/hR80orldgY4EfjOObfOOZcP/Aff/rHc1kXKa9vQ/G1GSGjev3JTjQljfgpzbgLlp/KE5v0rP9UI5aaaV225KV4LvDlAm2DVnFr4CZavRDmm3ZiZAY8Bi5xzo0s89AowLLg9DD++PCY450Y55zKcc5n4dn3XOXcBMAM4Kzgt1mJeDSw3s98Gh/oAC4nhdg78APQwszrB/5WiuGO2rUsor21fAS4KVoTqAWwqMRxhf6DcFCFhzE0Q2vwU5twEyk/lUX6KkDDmJ+WmqKi+3BTtCYaR+gH6A98AS4E/RTuecmI8Bt/9ugCYH/z0x4/Lng4sBt4BGkU71nLi7w28Ftw+BJgNLAEmAynRjm+XWLOAuUFbvwQ0DEM7A3cBXwE5wL+BlFhra+AZ/Fj3fPw3fpeW17b4SeXjgr/LL/ArXUW9nWu4vZSbIh9/aHJTEGPo8lMYclMQp/JT1dpL+Sny8YcmPyk3RTTOiOYmC54oIiIiIiIiIRevQzRFRERERET2OyrwRERERERE4oQKPBERERERkTihAk9ERERERCROqMATERERERGJEyrwJCLMrNDM5pf4GVmN1840s5zqup6I7F+Un0QkFik3SXVJqvgUkb2yzTmXFe0gRETKoPwkIrFIuUmqhXrwpEaZ2TIzu8/MvjCz2WZ2WHA808zeNbMFZjbdzA4Ojjc1sxfN7PPg5+jgUolm9oiZfWlm08ysdnD+DWa2MLjOs1F6myISQspPIhKLlJukqlTgSaTU3mWYwbklHtvknOsI/BMYExz7P+BJ59wRwNPA2OD4WOC/zrlOQBfgy+B4G2Ccc649sBE4Mzg+EugcXOeqyLw1EQk55ScRiUXKTVItzDkX7RgkDpnZFudcvTKOLwNOcM59a2bJwGrnXLqZrQeaOefyg+OrnHONzWwdkOGc217iGpnA2865NsH9W4Fk59xfzexNYAvwEvCSc25LhN+qiISM8pOIxCLlJqku6sGTaHDl3K6K7SVuF1I8n3QAMA7/jdUcM9M8UxGpCuUnEYlFyk1SaSrwJBrOLfH74+D2R8B5we0LgPeD29OBqwHMLNHM6pd3UTNLAFo652YAtwL1gd2+CRMR2QPlJxGJRcpNUmmq0CVSapvZ/BL333TOFS3329DMFuC/SRoSHLseeNzMRgDrgIuD48OBCWZ2Kf7bpquBVeW8ZiLwVJDIDBjrnNtYTe9HROKH8pOIxCLlJqkWmoMnNSoYR57tnFsf7VhEREpSfhKRWKTcJFWlIZoiIiIiIiJxQj14IiIiIiIicUI9eCIiIiIiInFCBZ6IiIiIiEicUIEnIiIiIiISJ1TgiYiIiIiIxAkVeCIiIiIiInFCBZ6IiIiIiEic+P/NzOpm3nRxIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,3,figsize=(15,5))\n",
    "\n",
    "ax[0].plot(np.arange(len(history['train']['loss'])), history['train']['loss'])\n",
    "ax[0].plot(np.arange(len(history['test']['loss'])), history['test']['loss'])\n",
    "ax[0].set_ylabel('Loss')\n",
    "ax[0].set_xlabel('Epochs')\n",
    "ax[0].legend(['train','test'])\n",
    "ax[1].plot(np.arange(len(history['train']['top1-acc'])), history['train']['top1-acc'])\n",
    "ax[1].plot(np.arange(len(history['test']['top1-acc'])), history['test']['top1-acc'])\n",
    "ax[1].set_ylabel('Top-1 Accuracy')\n",
    "ax[1].set_xlabel('Epochs')\n",
    "ax[1].legend(['train','test'])\n",
    "ax[2].plot(np.arange(len(history['test']['top5-acc'])), history['test']['top5-acc'])\n",
    "ax[2].set_ylabel('Top-5 Accuracy')\n",
    "ax[2].set_xlabel('Epochs')\n",
    "ax[2].legend(['test'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b287ebb",
   "metadata": {},
   "source": [
    "# Compare to ResNet18 without CutMix\n",
    "\n",
    "Model | CutMix | Top1-Acc \n",
    "---|---|---\n",
    "ResNet18 | X | 91.54 \n",
    "ResNet18 | O | 92.67"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bc0f2843",
   "metadata": {},
   "outputs": [],
   "source": [
    "history_base = json.load(open('ResNet18_epoch100_lr01.json','r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "077834e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd8409dbac0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,2,figsize=(15,5))\n",
    "\n",
    "ax[0].plot(np.arange(len(history['test']['loss'])), history['test']['loss'])\n",
    "ax[0].plot(np.arange(len(history_base['test']['loss'])), history_base['test']['loss'])\n",
    "ax[0].set_ylabel('Loss')\n",
    "ax[0].set_xlabel('Epochs')\n",
    "ax[0].legend(['With CutMix','Without CutMix'])\n",
    "ax[1].plot(np.arange(len(history['test']['top1-acc'])), history['test']['top1-acc'])\n",
    "ax[1].plot(np.arange(len(history_base['test']['top1-acc'])), np.array(history_base['test']['top1-acc'])*100)\n",
    "ax[1].set_ylabel('Top-1 Accuracy')\n",
    "ax[1].set_xlabel('Epochs')\n",
    "ax[1].legend(['With CutMix','Without CutMix'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0920a818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Epochs')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(20, len(history['test']['top1-acc'])), history['test']['top1-acc'][20:])\n",
    "plt.plot(np.arange(20, len(history_base['test']['top1-acc'])), np.array(history_base['test']['top1-acc'][20:])*100)\n",
    "plt.ylabel('Top-1 Accuracy')\n",
    "plt.xlabel('Epochs')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}