11_Model_optimalization.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDW5seyDvIQ6"
      },
      "source": [
        "# DDPLKO Moduł 11 - praca domowa - Optymalizacja modelu\n",
        "\n",
        "To jedenasta praca domowa w Programie szkoleniowym Klasyfikacja obrazu od Deep Drive PL\n",
        "\n",
        "Twoim zadaniem w tym module będzie:\n",
        "- [ ] Wykonanie optymalizacji jednego ze swoich modeli: modelu do klasyfikacji rysunków z QuickDraw, modelu wytrenowanego z wykorzystaniem transfer learningu, modelu do klasyfikacji binarnej\n",
        "- [ ] Możesz wykorzystać pruning lub kwantyzację\n",
        "- [ ] Porównaj Validation accuracy przed i po optymalizaji\n",
        "- [ ] Porównaj wielkość zapisanych modeli - przed i po optymalizaji (rozmiar pliku zip z modelem)\n",
        "- [ ] Udostępnij porównanie validation accuracy dla Twojego modelu przed i po optymalizacji wraz rozmiarami pliku zip na Discordzie `#klasyfikacja-wyniki`\n",
        "\n",
        "Extra - dodatkowo możesz:\n",
        "- Jeżeli masz kartę graficzną Nvidia'i, możesz spróbować wykorzystać TensorRT\n",
        "- Wykonać wykres pruning-rate vs val-acc, by pokazać zależność Validation Accuracy od wartości pruningu (użyj early stoppingu w treningach)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gT7NC2DmvIRB"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "id": "mFi6g9T8vIRC"
      },
      "outputs": [],
      "source": [
        "!pip install timm --quiet\n",
        "import timm\n",
        "import os\n",
        "import urllib\n",
        "import numpy as np\n",
        "import zipfile\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import Dataset, TensorDataset, DataLoader\n",
        "from tqdm.notebook import tqdm\n",
        "import torch.quantization as quantization\n",
        "from collections import defaultdict\n",
        "import torch.nn.utils.prune as prune\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "id": "xGYfEXYK3QP5"
      },
      "outputs": [],
      "source": [
        "device = 'cpu'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "2w1jue19vIRE"
      },
      "outputs": [],
      "source": [
        "class_names=['spider', 'penguin', 'shark', 'sheep', 'rhinoceros', 'hedgehog', 'horse', 'fish', 'flamingo', 'frog']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "2GbWGSdyvIRE"
      },
      "outputs": [],
      "source": [
        "for name in class_names:\n",
        "    url = 'https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/%s.npy'%name\n",
        "    file_name = url.split('/')[-1].split('?')[0]\n",
        "\n",
        "    url = url.replace(' ','%20')\n",
        "    \n",
        "    if not os.path.isfile(file_name):\n",
        "        print(url, '==>', file_name)\n",
        "        urllib.request.urlretrieve(url, file_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-x7koE-vIRF",
        "outputId": "f87b7f3e-7ff5-40b2-9cd1-f866e5113b68"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "spider          <class 'numpy.ndarray'>\n",
            "penguin         <class 'numpy.ndarray'>\n",
            "shark           <class 'numpy.ndarray'>\n",
            "sheep           <class 'numpy.ndarray'>\n",
            "rhinoceros      <class 'numpy.ndarray'>\n",
            "hedgehog        <class 'numpy.ndarray'>\n",
            "horse           <class 'numpy.ndarray'>\n",
            "fish            <class 'numpy.ndarray'>\n",
            "flamingo        <class 'numpy.ndarray'>\n",
            "frog            <class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "data = []\n",
        "for name in class_names:\n",
        "    file_name = name + '.npy'\n",
        "    data.append(np.load(file_name, fix_imports=True, allow_pickle=True))\n",
        "    print('%-15s' % name, type(data[-1]))\n",
        "for i in range(len(data)):\n",
        "    data[i] = data[i][::100]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "JnIRNSzxvIRH"
      },
      "outputs": [],
      "source": [
        "data = [np.reshape(name, (-1, 28, 28)) for name in data]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "TciT_1FbvIRH"
      },
      "outputs": [],
      "source": [
        "X = np.concatenate(data).reshape(-1, 1, 28, 28)\n",
        "X = X = X.astype('float32')\n",
        "X = X / 255\n",
        "\n",
        "y = np.concatenate([np.array([i] * len(_)) for i, _ in enumerate(data)],\n",
        "                   axis=0)\n",
        "y = y = y.astype('int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "WnPIwGl_vIRI"
      },
      "outputs": [],
      "source": [
        "batch_size = 128\n",
        "\n",
        "dataset = TensorDataset(torch.tensor(X), torch.tensor(y))\n",
        "\n",
        "train_size = int(0.8 * len(dataset))\n",
        "test_size = len(dataset) - train_size\n",
        "\n",
        "train_set, val_set = torch.utils.data.random_split(dataset,\n",
        "                                                   [train_size, test_size])\n",
        "\n",
        "train_loader = DataLoader(train_set, batch_size=batch_size)\n",
        "val_loader = DataLoader(val_set, batch_size=batch_size)\n",
        "del X\n",
        "del y\n",
        "del data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "W98ezHnmvIRJ"
      },
      "outputs": [],
      "source": [
        "from torchsummary import summary\n",
        "class Net(nn.Module):\n",
        "    def __init__(self, dropout=0.3, pool2d=nn.AvgPool2d):\n",
        "        super(Net, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Conv2d(1, 32, 3),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(32),\n",
        "            nn.Dropout(dropout),\n",
        "\n",
        "            pool2d(2),\n",
        "            nn.Conv2d(32, 128, 3),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout),\n",
        "            nn.BatchNorm2d(128),\n",
        "\n",
        "            nn.Conv2d(128, 64, 3),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout),\n",
        "            nn.BatchNorm2d(64),\n",
        "        \n",
        "            nn.Flatten(),\n",
        "            nn.Linear(5184, 10))\n",
        "            \n",
        "\n",
        "        self.quant = torch.quantization.QuantStub()\n",
        "        self.dequant = torch.quantization.DeQuantStub()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.quant(x)\n",
        "        x = self.model(x)\n",
        "        x = self.dequant(x)\n",
        "        return F.log_softmax(x, dim=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYnXK_MS1Pqk",
        "outputId": "22f7f935-28b2-4947-a42d-3e713d55770e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "         QuantStub-1            [-1, 1, 28, 28]               0\n",
            "            Conv2d-2           [-1, 32, 26, 26]             320\n",
            "              ReLU-3           [-1, 32, 26, 26]               0\n",
            "       BatchNorm2d-4           [-1, 32, 26, 26]              64\n",
            "           Dropout-5           [-1, 32, 26, 26]               0\n",
            "         AvgPool2d-6           [-1, 32, 13, 13]               0\n",
            "            Conv2d-7          [-1, 128, 11, 11]          36,992\n",
            "              ReLU-8          [-1, 128, 11, 11]               0\n",
            "           Dropout-9          [-1, 128, 11, 11]               0\n",
            "      BatchNorm2d-10          [-1, 128, 11, 11]             256\n",
            "           Conv2d-11             [-1, 64, 9, 9]          73,792\n",
            "             ReLU-12             [-1, 64, 9, 9]               0\n",
            "          Dropout-13             [-1, 64, 9, 9]               0\n",
            "      BatchNorm2d-14             [-1, 64, 9, 9]             128\n",
            "          Flatten-15                 [-1, 5184]               0\n",
            "           Linear-16                   [-1, 10]          51,850\n",
            "      DeQuantStub-17                   [-1, 10]               0\n",
            "================================================================\n",
            "Total params: 163,402\n",
            "Trainable params: 163,402\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 1.38\n",
            "Params size (MB): 0.62\n",
            "Estimated Total Size (MB): 2.00\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "model_baseline = Net().to(device)\n",
        "summary(model_baseline, (1, 28, 28), device=device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "8NyzRIgSvIRK"
      },
      "outputs": [],
      "source": [
        "def train_model(model, epochs):\n",
        "    optimizer = torch.optim.Adam(model.parameters())\n",
        "\n",
        "    history = defaultdict(lambda: [])\n",
        "\n",
        "    for epoch in range(1, epochs + 1):\n",
        "        model.train()\n",
        "        train_loss = 0\n",
        "        correct = 0\n",
        "        for batch_idx, (data, target) in enumerate(train_loader):\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            optimizer.zero_grad()\n",
        "            output = model(data)\n",
        "            loss = F.nll_loss(output, target)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            pred = output.argmax(dim=1, keepdim=True)\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "            train_loss += loss.item()\n",
        "\n",
        "        train_loss /= len(train_loader)\n",
        "        acc = correct / len(train_loader.dataset)\n",
        "        history['loss'].append(train_loss)\n",
        "        history['accuracy'].append(acc)\n",
        "\n",
        "        model.eval()\n",
        "        val_loss = 0\n",
        "        correct = 0\n",
        "        with torch.no_grad():\n",
        "            for data, target in val_loader:\n",
        "                data, target = data.to(device), target.to(device)\n",
        "                output = model(data)\n",
        "                val_loss += F.nll_loss(output, target, reduction='sum').item()\n",
        "                pred = output.argmax(dim=1, keepdim=True)\n",
        "                correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "        val_loss /= len(val_loader.dataset)\n",
        "        val_acc = correct / len(val_loader.dataset)\n",
        "        history['val_loss'].append(val_loss)\n",
        "        history['val_accuracy'].append(val_acc)\n",
        "\n",
        "        print('Epoch %3d   Train loss: %f Acc: %f   Val loss: %f Acc: %f' %\n",
        "              (epoch, train_loss, acc, val_loss, val_acc))\n",
        "\n",
        "    return history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "_E4B-IKwvIRL"
      },
      "outputs": [],
      "source": [
        "learning_rate = 1e-3\n",
        "step = 8\n",
        "epochs = 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOe1VeOLvIRM",
        "outputId": "2e3beba8-17eb-4d46-efb7-6dfa503ad6c8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch   1   Train loss: 0.323068 Acc: 0.891108   Val loss: 0.552668 Acc: 0.846700\n",
            "Epoch   2   Train loss: 0.271301 Acc: 0.907766   Val loss: 0.593671 Acc: 0.840222\n",
            "Epoch   3   Train loss: 0.248019 Acc: 0.914167   Val loss: 0.594997 Acc: 0.843924\n",
            "Epoch   4   Train loss: 0.226009 Acc: 0.923190   Val loss: 0.581874 Acc: 0.851326\n",
            "Epoch   5   Train loss: 0.211241 Acc: 0.926968   Val loss: 0.596245 Acc: 0.850709\n",
            "Epoch   6   Train loss: 0.192622 Acc: 0.933446   Val loss: 0.612727 Acc: 0.847316\n",
            "Epoch   7   Train loss: 0.184716 Acc: 0.934835   Val loss: 0.643671 Acc: 0.848242\n",
            "Epoch   8   Train loss: 0.175513 Acc: 0.937688   Val loss: 0.625383 Acc: 0.844849\n",
            "Epoch   9   Train loss: 0.158829 Acc: 0.943857   Val loss: 0.643739 Acc: 0.853486\n",
            "Epoch  10   Train loss: 0.152109 Acc: 0.947868   Val loss: 0.640741 Acc: 0.851635\n"
          ]
        }
      ],
      "source": [
        "history_baseline = train_model(model_baseline, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "QM12-kbU2Sw5"
      },
      "outputs": [],
      "source": [
        "def plot_history(history, title):\n",
        "    plt.figure(figsize=(15, 7))\n",
        "    plt.suptitle(title)\n",
        "\n",
        "    plt.subplot(121)\n",
        "    plt.plot(history['accuracy'], label='train')\n",
        "    plt.plot(history['val_accuracy'], label='val')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(122)\n",
        "    plt.plot(history['loss'], label='train')\n",
        "    plt.plot(history['val_loss'], label='val')\n",
        "    plt.ylabel('loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "o9pkORot2TPh",
        "outputId": "8bee9850-699b-4f28-ecf8-a94272ad365a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_history(history_baseline, 'Baseline training')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "N0iKthfJ2arp"
      },
      "outputs": [],
      "source": [
        "torch.save(model_baseline, 'model_baseline.pt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yx2UNeHSvIRM"
      },
      "source": [
        "# Optymalizacja modelu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "3BvRZPpqwr50"
      },
      "outputs": [],
      "source": [
        "model_baseline_fused = torch.load('model_baseline.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q7GOB8gFxN2d",
        "outputId": "93ca6184-a16f-41aa-87cb-e48486274e26"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Net(\n",
              "  (model): Sequential(\n",
              "    (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): Dropout(p=0.3, inplace=False)\n",
              "    (4): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
              "    (5): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1))\n",
              "    (6): ReLU()\n",
              "    (7): Dropout(p=0.3, inplace=False)\n",
              "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (9): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1))\n",
              "    (10): ReLU()\n",
              "    (11): Dropout(p=0.3, inplace=False)\n",
              "    (12): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (13): Flatten(start_dim=1, end_dim=-1)\n",
              "    (14): Linear(in_features=5184, out_features=10, bias=True)\n",
              "  )\n",
              "  (quant): QuantStub()\n",
              "  (dequant): DeQuantStub()\n",
              ")"
            ]
          },
          "execution_count": 115,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_baseline_fused.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "id": "KUMz6e2h2fuJ"
      },
      "outputs": [],
      "source": [
        "def get_fuse_modules(model):\n",
        "    modules_list = list(model.model.named_modules())\n",
        "\n",
        "    fuse_modules = []\n",
        "    for i in range(len(modules_list)):\n",
        "        if isinstance(modules_list[i][1], nn.Conv2d):\n",
        "            if isinstance(modules_list[i + 1][1], nn.ReLU):\n",
        "                fuse_modules.append(\n",
        "                    [modules_list[i][0], modules_list[i + 1][0]])\n",
        "\n",
        "    return fuse_modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uw7hhuXj2inA",
        "outputId": "034a3a03-1105-41de-e373-2e85179061bd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): ConvReLU2d(\n",
              "    (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
              "    (1): ReLU()\n",
              "  )\n",
              "  (1): Identity()\n",
              "  (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (3): Dropout(p=0.3, inplace=False)\n",
              "  (4): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
              "  (5): ConvReLU2d(\n",
              "    (0): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1))\n",
              "    (1): ReLU()\n",
              "  )\n",
              "  (6): Identity()\n",
              "  (7): Dropout(p=0.3, inplace=False)\n",
              "  (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (9): ConvReLU2d(\n",
              "    (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1))\n",
              "    (1): ReLU()\n",
              "  )\n",
              "  (10): Identity()\n",
              "  (11): Dropout(p=0.3, inplace=False)\n",
              "  (12): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (13): Flatten(start_dim=1, end_dim=-1)\n",
              "  (14): Linear(in_features=5184, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fuse_modules = get_fuse_modules(model_baseline_fused)\n",
        "\n",
        "quantization.fuse_modules(model_baseline_fused.model,\n",
        "                          fuse_modules,\n",
        "                          inplace=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhIgeWPi2jvY",
        "outputId": "d8fe5db0-761e-49ab-bcb8-9715d795716b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Net(\n",
              "  (model): Sequential(\n",
              "    (0): ConvReLU2d(\n",
              "      (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
              "      (1): ReLU()\n",
              "    )\n",
              "    (1): Identity()\n",
              "    (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): Dropout(p=0.3, inplace=False)\n",
              "    (4): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
              "    (5): ConvReLU2d(\n",
              "      (0): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1))\n",
              "      (1): ReLU()\n",
              "    )\n",
              "    (6): Identity()\n",
              "    (7): Dropout(p=0.3, inplace=False)\n",
              "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (9): ConvReLU2d(\n",
              "      (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1))\n",
              "      (1): ReLU()\n",
              "    )\n",
              "    (10): Identity()\n",
              "    (11): Dropout(p=0.3, inplace=False)\n",
              "    (12): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (13): Flatten(start_dim=1, end_dim=-1)\n",
              "    (14): Linear(in_features=5184, out_features=10, bias=True)\n",
              "  )\n",
              "  (quant): QuantStub()\n",
              "  (dequant): DeQuantStub()\n",
              ")"
            ]
          },
          "execution_count": 118,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_baseline_fused"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "yEjfz4A92nck"
      },
      "outputs": [],
      "source": [
        "torch.save(model_baseline_fused, 'model_baseline_fused.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LY8rf1E62rdW",
        "outputId": "5c4ed6a7-4849-4ea7-aa5b-580a92e2a527"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Net(\n",
              "  (model): Sequential(\n",
              "    (0): ConvReLU2d(\n",
              "      (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
              "      (1): ReLU()\n",
              "    )\n",
              "    (1): Identity()\n",
              "    (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): Dropout(p=0.3, inplace=False)\n",
              "    (4): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
              "    (5): ConvReLU2d(\n",
              "      (0): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1))\n",
              "      (1): ReLU()\n",
              "    )\n",
              "    (6): Identity()\n",
              "    (7): Dropout(p=0.3, inplace=False)\n",
              "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (9): ConvReLU2d(\n",
              "      (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1))\n",
              "      (1): ReLU()\n",
              "    )\n",
              "    (10): Identity()\n",
              "    (11): Dropout(p=0.3, inplace=False)\n",
              "    (12): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (13): Flatten(start_dim=1, end_dim=-1)\n",
              "    (14): Linear(in_features=5184, out_features=10, bias=True)\n",
              "  )\n",
              "  (quant): QuantStub()\n",
              "  (dequant): DeQuantStub()\n",
              ")"
            ]
          },
          "execution_count": 120,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_quant = torch.load('model_baseline_fused.pt')\n",
        "model_quant.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4NrysQf2ukM",
        "outputId": "af4bc6d3-e66e-4d52-9fce-c0875981645d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Net(\n",
              "  (model): Sequential(\n",
              "    (0): ConvReLU2d(\n",
              "      (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
              "      (1): ReLU()\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (1): Identity()\n",
              "    (2): BatchNorm2d(\n",
              "      32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (3): Dropout(p=0.3, inplace=False)\n",
              "    (4): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
              "    (5): ConvReLU2d(\n",
              "      (0): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1))\n",
              "      (1): ReLU()\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (6): Identity()\n",
              "    (7): Dropout(p=0.3, inplace=False)\n",
              "    (8): BatchNorm2d(\n",
              "      128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (9): ConvReLU2d(\n",
              "      (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1))\n",
              "      (1): ReLU()\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (10): Identity()\n",
              "    (11): Dropout(p=0.3, inplace=False)\n",
              "    (12): BatchNorm2d(\n",
              "      64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (13): Flatten(start_dim=1, end_dim=-1)\n",
              "    (14): Linear(\n",
              "      in_features=5184, out_features=10, bias=True\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "  )\n",
              "  (quant): QuantStub(\n",
              "    (activation_post_process): HistogramObserver()\n",
              "  )\n",
              "  (dequant): DeQuantStub()\n",
              ")"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.backends.quantized.engine = 'qnnpack'\n",
        "model_quant.qconfig = quantization.get_default_qconfig('qnnpack')\n",
        "quantization.prepare(model_quant, inplace=True)  # added observers\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "-97H82bw273g"
      },
      "outputs": [],
      "source": [
        "num_calibration_batches = 10\n",
        "\n",
        "model_quant.eval()\n",
        "for i, (batch, target) in enumerate(train_loader):\n",
        "    batch, target = batch.to(device), target.to(device)\n",
        "    model_quant(batch)\n",
        "\n",
        "    if i + 1 == num_calibration_batches:\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "id": "XNDnaY_v3EXR"
      },
      "outputs": [],
      "source": [
        "model_quant = quantization.convert(model_quant)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "id": "IKtxMyzv4qui"
      },
      "outputs": [],
      "source": [
        "torch.save(model_quant, 'model_quant.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "id": "hD6K-k8E4rK5"
      },
      "outputs": [],
      "source": [
        "model_qat = Net()\n",
        "\n",
        "fuse_modules = get_fuse_modules(model_qat)\n",
        "quantization.fuse_modules(model_qat.model, fuse_modules, inplace=True)\n",
        "\n",
        "model_qat.qconfig = quantization.get_default_qconfig('qnnpack')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGSYj4zN4uJ-",
        "outputId": "910b0292-a30d-4f73-cee2-e2da8cfa6bc7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Net(\n",
              "  (model): Sequential(\n",
              "    (0): ConvReLU2d(\n",
              "      1, 32, kernel_size=(3, 3), stride=(1, 1)\n",
              "      (weight_fake_quant): MinMaxObserver(min_val=inf, max_val=-inf)\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (1): Identity()\n",
              "    (2): BatchNorm2d(\n",
              "      32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (3): Dropout(p=0.3, inplace=False)\n",
              "    (4): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
              "    (5): ConvReLU2d(\n",
              "      32, 128, kernel_size=(3, 3), stride=(1, 1)\n",
              "      (weight_fake_quant): MinMaxObserver(min_val=inf, max_val=-inf)\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (6): Identity()\n",
              "    (7): Dropout(p=0.3, inplace=False)\n",
              "    (8): BatchNorm2d(\n",
              "      128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (9): ConvReLU2d(\n",
              "      128, 64, kernel_size=(3, 3), stride=(1, 1)\n",
              "      (weight_fake_quant): MinMaxObserver(min_val=inf, max_val=-inf)\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (10): Identity()\n",
              "    (11): Dropout(p=0.3, inplace=False)\n",
              "    (12): BatchNorm2d(\n",
              "      64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "    (13): Flatten(start_dim=1, end_dim=-1)\n",
              "    (14): Linear(\n",
              "      in_features=5184, out_features=10, bias=True\n",
              "      (weight_fake_quant): MinMaxObserver(min_val=inf, max_val=-inf)\n",
              "      (activation_post_process): HistogramObserver()\n",
              "    )\n",
              "  )\n",
              "  (quant): QuantStub(\n",
              "    (activation_post_process): HistogramObserver()\n",
              "  )\n",
              "  (dequant): DeQuantStub()\n",
              ")"
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "quantization.prepare_qat(model_qat,\n",
        "                         inplace=True)  # performs \"fake quantization\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9pdspFoh4vOZ",
        "outputId": "138bc479-cc82-4d3c-8f38-7010a5e6060b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch   1   Train loss: 1.028683 Acc: 0.667464   Val loss: 0.707750 Acc: 0.777915\n",
            "Epoch   2   Train loss: 0.674882 Acc: 0.786149   Val loss: 0.619389 Acc: 0.809994\n",
            "Epoch   3   Train loss: 0.563311 Acc: 0.821007   Val loss: 0.592425 Acc: 0.817705\n",
            "Epoch   4   Train loss: 0.490148 Acc: 0.842523   Val loss: 0.550575 Acc: 0.834053\n",
            "Epoch   5   Train loss: 0.425847 Acc: 0.863731   Val loss: 0.549259 Acc: 0.838371\n",
            "Epoch   6   Train loss: 0.377645 Acc: 0.876456   Val loss: 0.519291 Acc: 0.842690\n",
            "Epoch   7   Train loss: 0.341198 Acc: 0.889026   Val loss: 0.516363 Acc: 0.851018\n",
            "Epoch   8   Train loss: 0.303123 Acc: 0.896044   Val loss: 0.525430 Acc: 0.847933\n",
            "Epoch   9   Train loss: 0.274085 Acc: 0.908691   Val loss: 0.522362 Acc: 0.853486\n",
            "Epoch  10   Train loss: 0.247462 Acc: 0.913550   Val loss: 0.538037 Acc: 0.846083\n"
          ]
        }
      ],
      "source": [
        "history_qat = train_model(model_qat, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "CsluWS1k4w7q",
        "outputId": "7a3a4e1c-1e62-4228-fdf0-7a97f7d83eda"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_history(history_qat, 'Quantization aware training')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "id": "0-OGMpYz4yHl"
      },
      "outputs": [],
      "source": [
        "model_qat.eval()\n",
        "model_qat = torch.quantization.convert(model_qat, inplace=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {
        "id": "Iksgwl2O40BG"
      },
      "outputs": [],
      "source": [
        "torch.save(model_qat, 'model_qat.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "id": "mDKjZqFV41iT"
      },
      "outputs": [],
      "source": [
        "def evaluate_model(model, model_name):\n",
        "    model.eval()\n",
        "    correct = 0\n",
        "    with torch.no_grad():\n",
        "        for data, target in val_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            pred = output.argmax(dim=1, keepdim=True)\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "        acc = correct / len(val_loader.dataset)\n",
        "\n",
        "    print(model_name, 'test accuracy:', acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AziLnogpvIRN"
      },
      "source": [
        "# Porównanie przed i po - Validation Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxVhRuNBvIRN",
        "outputId": "85b1ae79-8f41-49e6-a9f0-4ea6883c7e65"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Baseline test accuracy: 0.8516347933374461\n",
            "Static Quantization test accuracy: 0.8488587291795188\n",
            "Quantization aware trained model test accuracy: 0.8476249228871067\n"
          ]
        }
      ],
      "source": [
        "evaluate_model(model_baseline_fused, 'Baseline')\n",
        "evaluate_model(model_quant, 'Static Quantization')\n",
        "evaluate_model(model_qat, 'Quantization aware trained model')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ntGTh-vtvIRN"
      },
      "source": [
        "# Porównanie przed i po - Model size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-53jChzEvIRO",
        "outputId": "5a5e1bc9-5fd2-48ea-d863-8572174c5b6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pt model files size:\n",
            "\n",
            "Baseline:                   666528 bytes\n",
            "Static quantization:        179464 bytes\n",
            "Quantization aware trained: 179464 bytes\n"
          ]
        }
      ],
      "source": [
        "print('pt model files size:\\n')\n",
        "print('Baseline:                   {} bytes'.format(os.path.getsize('model_baseline_fused.pt')))\n",
        "print('Static quantization:        {} bytes'.format(os.path.getsize('model_quant.pt')))\n",
        "print('Quantization aware trained: {} bytes'.format(os.path.getsize('model_qat.pt')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTLx8-c45FAd",
        "outputId": "8443b116-8adb-4ff6-9337-7601bac3781c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3.7139927784959657"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "os.path.getsize('model_baseline_fused.pt') / os.path.getsize('model_quant.pt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iH-eeNqwvIRO"
      },
      "source": [
        "# Summary / extra"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "id": "Kjzdy3VWvIRO"
      },
      "outputs": [],
      "source": [
        "model_sparse = torch.load('model_baseline.pt')\n",
        "model_baseline = torch.load('model_baseline.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "ImKGcCkp6-bp",
        "outputId": "0e858fa0-7757-4300-aac1-252b5556b1a2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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vXtH27Uv0xRcvaOTIKWrXrl/a8tevR+rmzStq1aqH7OycVKJEKYWHn1Zg4DLt3eun//u/tapYsVJBH3aGJv/nP/L28VGP7t01csQIhZw9K+9Fi3Ti5Ekt+OGHbPuX3Ky/afNmeb3zjlwaNtT7772n2zEx+tnbW0OGDtXK5cvl5PggsArcsUNv/Otfqlmjhoa9+KLs7OwUfOaMli1frk2bNmn92rVycnJKW/7EiRN65dVXZW1trQHPPaeqVavq5s2bOnb8uK5dv57/FZdHUVGhmjLF1AZ79TK1wR07TG3QyytnbfDHH01t8KmnzNvglCkv6JVXpsjNrV+m669b961iYorm2rJIgprg0FD5+PqqZ7t2mjF+fFp5dScnTZo3T347dsjT3T3LbfjPm6caVapIkjxGjVLso8MVMtCva9e87biBBJ8/L5+1a9WzY0fN+PhBsle9ShVNmjVLfgEB8uzWLctt+P/8s2pUrSpJ8nj99Uzr0N7WVmtmz1ajR37x9+zWTf3fektfzp+vVd99l8cjKlpt25rCkhUrpKtXTWWnTkmDB5tGxSxenLvtdepkGiFz7ZrUsGHGy/j4SDExpv//wgtSqSKLTf++gsPD5ePvr56tW2vG6NFp5dUdHDRp0SL57dkjz3aZp/W9nnpKb3h4qMJDv4QM6dZNtZycNGf9eq0IDNSw7t0lSSVLlpT3uHFydXEx28Ygd3c9/T//oylLlsjTzS3bE72Rbd78te7du6U331ylqlUbSZJatOivmTM95Os7UV5eG2VhYZHlNgYNmpaurF274Zoz5znt3Pm9Ond+UyVKlEy3zKVLxxUU9LN69Hhfv/32n/w5oEISfPmyfLZvV88WLTTj9dfTyqtXqqRJK1bI78ABeT71VKbr92rZUm/07KkKD42kHNKpk2pVrqw5v/2mFbt3a9hD59R+rq7pthFx/brGXb2qpjVrysXZOZ+ODDnFdVHeUYcFJzgiQj47d6pns2aa8coraeXV7e01afVq+R08KM/WrTNdv12DBmqXQXjVpm5dvbNwoVbt26fXs7lmRdFYteprxcbe0kcfrVLNmqbzert2/fXxxx5atGiiJk3K+rxeokRJvf++txo2ND/vdOo0SP/3f09r2bIpatvWM+3ap1GjdhmO1GnQoI3mzHlHu3atUp8+r6d7vaAFBwfLZ9Ei9ezRQzOmT08rr+7srEmTJ8tvwwZ5enjky/oJCQn67PPPVbVKFS3y9pa1tbUkqXOnTnru+ec187vv9NnEiWnb+HnhQpUsUUKLf/lF9nZ2aeVP1K+v//34Y/36228akfIDTVxcnN4ZM0ZVq1aVz8KFKl++fP5UUAFavdrUBv/3f1epRg1TG3Rz669PPvHQ4sUT9emn2bfBsWMzb4PLl0+Rq6tnhtffFy4c15YtP2vAgPe1fHnhX1sWyTcC38BAJScna/gzz5iVD+rZU1Zly2pdQEC220g9keZGcnKybsfGKikpKdfrGo3vtm2mOnz2WbPyQX37mupwy5Zst5Ea0mTHrmLFdCGNJNWvVUsNatVS8PnzOdqOUZUqJdWuLV269CCkkaTEROnPP023RDnmYqRlnTqm7QUGSsnJmS+XGtKg6PgGBZk+Rz17mpUPcneXVZkyWrd7d5brN6tTxyykSdU35Yvw6bCwtLJSJUumC2kkycHGRq4NG+rqrVu6euvW4xyGIcTHx+rUqa2qXds1LaSRpLJlrdW69UBdvXpe4eFHH2vbJUuWUsWKTkpIuKv79xPTvZ6UdF9r136k+vU7ZXt7lBH57t9vaoePfOEb1KGDqR3u25fl+s1q1TILaVL1bdVKknT60qVs92FlUJCSkpP1fDEf1VVccV2Ud9RhwfH94w9T3T4SdA1yczP1UQcOPNZ2nVO+VN6Kjc3zPiL/xcXF6tChrWrY0DUtpJEkS0trdeo0UJGR53XuXNbn9ZIlS6X7gixJNjYOatDAVTExVxUTczWDNc3Z25t+QIiNLZrrJN8NG0yfgZdeMisf9PzzsrKy0rr16/Nt/X379ikqKkoDBw5MC2kkqVGjRnJ96ilt+PVXJSQkpJXfvn1bZcqWlc0jtxA6Vq4sSWbTYfy6caMuhIbKa/RolS9fXvHx8YqPj89hLRS+uLhYHT5saoOpIY1kaoMdO5ra4Pnzj9cGK1bMug0mJd2Xt/dHatKkk558smiuLYvkN/xjwcEqUaJEuqGhZcuUkUudOjoaHFwg79t68GDduXtXpUuV0lNNmuidYcPUIrPhDgZ37PRpUx0+sv9ly5SRS716Onr6dIHvQ1JSkqKuXZPDQ+ltcVSpkimsiYxM/1pqWeXKUlRU9tsqXVrq2FE6ccK0fJMm+buvyF/Hzp1TCQsLNa9b16y8bJkycqlZU0fPnXus7UakDB11sLHJ8fKlS5VSxRzc42xUERGnlJgYrxo10t8GWb26qSw8/KiqV2+eo+3Fxt5QcnKSYmOv69ixX3XmzA7Vrt1WpUunnwvh998XKDr6rAYP/jZvB1FEjl24YGqHtWqZlZctXVou1avraGjoY2034sYNSZJDNvM/JCcna1VQkMqVKSOPLH4VR8HhuijvqMOCc+ziRVMf9cichGVLl5ZLtWo6evFijrYTl5CgO3Fxik9M1JnISE319ZUkuTdunO/7jLwLCzOd1+vVS39er1vXVHb+/FHVrZuz8/qjrl+PUKlSpVWuXPpzVEJCnO7du6OEhHhdvnxGK1ZMlSQ1a5b1qLiCcuzYMVP/0tz8WMuWLSsXFxcdPXYs39ZP/f9Ptkg/d0rLFi0UtGePzp8/ryeeeEKS1LFDBx06fFgfjh+v10aOlJ2dnU4HB+s/X32lenXrqm/fvmnrbw8MlCRVrFBBQ196SQdSQthGLi4aO2aMOnXsmItaKXipbTC1vT3s4TZYp07+t0F//wWKiDirN98sumvLIglqoq5dk12FCipTunS615wqVdLBkycVn5CQ4euPw8HOTiP69VOTevVUztJSJ8+d08/gpdM/AAAgAElEQVTr1mno+PGa9/HHal8M51eJunpVdhUrqkwGk5s4OTjo4IkT+VqHGVns66sr167praFDC+w9CkNqWJ3R7eipZTkdGdiunWRhIe3Zkz/7hoIVdeNG5n2RnZ0Onjmj+MRElcnFfWn3k5I0e906lSpZUh5ubtkuv/3wYR05e1b92rdX2WI8WVFMjCnJrFjRKd1rqWW3bmWQhmbi2297KTbWFDSUKFFKjRv3lIfHJ+mWu379orZtm6EuXd6SnV11Xb8elm4Zo4u6eVN25ctn3A5tbHTw7NnHa4cbN6pUiRLyaNMmy2WDTp9W2NWreq5tW5XPwUT0yH9cF+UddVhwom7elJ21dYZ9kJONjQ6eP5+jPmp5UJA+W7067b+d7e311dChavPIjyUwhhs3TOd1W9v053U7O6eUZXJ+Xn/YkSPbde7cEbVr1y/DH2B27FiuX375LO2/HRyc9dprX6lBg6zPZwUlKipKdnZ2GX/vcnTUwYMHFR8fn+HruV0/KuWX4YfnlUnlmFIWGRWVFtS88c9/6uq1a1q5apXWp4SfkuTeubP+O3Wqyj80Kudcyg+Qo995Ry2aN9d/p07VzZs3NWfePP3zzTf1w7x5am+gkbVZtcHUssdtg0ePbtf580fk5pa+DUZHX9S6dTPk4fGWHByqKzq6aK4tiySouRsXl+mJMvWLyr0slsmt94YPN/vv7m5u8nB3V/+339Yns2dr09y5+fI+hamw6/BRfxw/rv/MnSuXunX15pAhBfIehSX1uuL+/fSvJSaaL5OVKlWkxo0lf3/TJMIwvrtxcZleWJZN+ezcy2KZjExetEgHz5zRmIEDVTeb2wvPR0Tog3nz5GRnp3GDB+d8xw0oIeGuJKlUqfQXIaVKlTVbJicGD56pxMQ43boVqePHNyohIU5xcXdkbW1vtty6dZ/Izq6G2rd/JZMtGd/dhITs22F8fO7a4YoVOnjunMZ4eqpuBhd7D1v++++SpAEGujj7u+G6KO+ow4KToz4qi2VSdW/WTHWdnBQbF6cT4eHaevy4rudw0mYUvvh40zm7dOn05/XUL7apy+RGZOR5/fDDB7Kzc9KgQeMyXKZly+6qUqWu4uJiFRp6QocObdXt20U30e3de/cy719Snnp27969TIOa3Kx/N2VurIy2ldaX3X1Q7yVKlJCTo6Pat2un7t27y9bGRn/88Ye8Fy3Su2PHatbMmSqd8t53Uj5vdevU0ezvvkub26Wdm5uefuYZfTN9uqGCmtT2ldG1ZV7b4I8/fiBbWyc9/3z6Nujj84kcHGqoe/eivbYskqDGqmxZXb2bcaXGpXzDtczmUX95VbtaNfXp2FGrtmzRufBw1SlmkycWZR0eO31ab3z0kRwrVdK8SZOKzSgACwvp0adz3r//IIwpmX5+0rSAJjH9tBhmSpSQ3N2lsDDpzJm87ysKh1XZspnOCxOXcv9vbj5H01aulM+WLXqhSxe9kcWkcpJ08coVjfjyS8nCQvPHjJF9MXk8aVLS/XRPVipd2lKlS5tGYiQmpk8pExPjUpbL+WiN2rUfTJ7bqtUALV8+Rt9/P0SjR/vJysp0S9nhw2t19uwujRzpo5IlC270YEGzKl1aV+PiMnwtrR3mop+d5usrn8BAvdChg97o1SvLZW/cuaPNhw+rrpOT2vxFHhFfHHFdlHfUYcHJUR+VgwCsiq2tqtjaSjKFNj2bN9fAb77Rvfh4vZEy8T4KX1LS/XRPtSld2lJlypjO2QkJ6c/rCQmm9pC6TE5duXJRX389QhYW0ttvz1eFCvYZLmdvX0X29qY5o558srtateqpzz8fqPj4e+rb941cvWd+sLK01NVM5lKKS/lsWD76JeMx17dK+d+M5o5J68seGv06bsIEHTx0SH7r1qVto0f37qpZs6Y++fRTrVm7Vs8PHGj2Hv379TObgLd27dp6smVL7T9wQLGxsTl63Hh+yq4NZnRt+bhtMDr6ov773xGSJC+v9G0wKGit/vxzl957z0elShXttWWRBDWO9vY6c/FihkNQI1Nv6SnAW3ZSOafMEHv91q1idzJ1rFRJZ0JDMxxmFxkdLTsbmwKpw+PBwRo5frwqWFtr4VdfycnBId/fo6CULy8NG2ZedvKkaT4Z6cEtUA9LLbt9O+ttN21qmnT4999Nj/pOlfpPU6GCaf4aJhA2FkdbW50JD8+4L7p+3TSMPoejGGasXq3Z69fruU6dNPGRX1ofFXbliob/5z+KjYvTgg8+UMMaNR77GArbzZuX9c03/zAra9nyWbVp84KkjG9vSi3L6LaonGrZsr+OHvXTiROb1Lr180pMjNfGjf/RE0+4q3z5yrp69YLZe8XFxejq1QsqV85OVlbGDsEcbWx0JiIi43aYeltUTtuhn59mb9yo59zcNDEHo7TW79un+MREDczi6WYoeFwX5R11WHAcbWx0JjIyw9ubIrO4LSo7LtWqqbGzs375/XeCmiJ07dpljRtnfl5v3/5ZububzusZ3Vpy/bqpLKNbUjITHR2mqVOH6969WL333gJVr57zuZxq1HBRzZqNtW3bL0US1Dg6OupMSEjG37uyuK3pcdZ3TOlDIiMjVe+RH1CiUibPTH0896VLl7Te11fDXnwxXVDUu3dvffLpp9q7b19aUOPk5KTTwcFyyOD7W+XKlZWcnKyY27cLPai5du2yJkwwb4Pt2j2rzp0zb4OpZbltg19/PVxxcbEaMyZ9G0xIiNfy5f9R06busrGprKgo07Vlanu/ezdGUVEXVL68XYbz2uS3Iglqmj7xhHYePKgjp0+rzUOzrcbFx+vkuXNmZQXpfMqTMBxS0v3ipGmDBtp54ICOnDqlNs2apZXHxcfrZEiIWVl+OR4crFfGjZO1lZV+/vJLOWcznN5oYmOldevSl8XEmEbMZHQ4qWVXrmS97QoVTKNqMhtEMXCglJAgff997vcbBadpnTraeeyYjpw9qzYPTf4YFx+vk6GhZmVZmbF6tWauXatnO3TQ56+8kuVjAsOuXNHL//mPYu7e1U8ffKDGj0wga3Tly1fW8OE/mZVVqOAoG5uqKlWqjC5ePJRunbAwU1m1ak0f+31Tfzm5e/dmyn/f050713T6dIBOnw5It/zhw+t0+PA69ez5gTp2fPWx37cwNK1VSztPntSRCxfUpn79tPK4hASdDAszK8vKDD8/zfz1Vz3btq0+f/HFbB+FLkkrdu9W6ZIl1b9t28fef+Qd10V5Rx0WnKY1amjnqVM6EhpqNp9MXEKCTl66lKc5Zu4lJOgmT30qUjY2lTVmjPl53dbWUfb2pvN6SEj68/rZs6ay2rVzdl6Pjg7TV1+9rLt3YzRmzE+qWTP3E0jHx9/TnTs3c71efmjatKl27tqlI0eOqM1D877FxcXp5MmTapPNRPy5Wb9ZU1OdHjx8ON1tSIcOH1b58uVVu3ZtSaaQRzLNS/eo+ym3A9x/aG6H5s2aacfOnYqIiEi3fERkpEqVKiXbHD4IIz/Z2FTWO+9k3gZT29vDUstq1cp5G/z6a1MbfPfdjNtgQsI9xcRc09GjATp6NCDd63v2rNOePes0cOAH6tmz4K8ti+Tx3H07dpSFhYV+fuRb87JNm3Q3Lk6eDz3+L/TyZYWEPf4EPrH37qUNE3vYiZAQbdy1S/Vq1FDNHD6m2kj6duliqsOHJmWTpGUbNpjqsFu3tLLQS5cU8phPDUl14swZjRw3TuUsLbXwq69y/GhvI7l/XwoPN/+7ft0U0ly4IFWrZnoCVKpSpaRGjaQbN8yf+FSmjGn0zMPB9cmT0m+/pf8LDze9vnWrae4aGEtfV1fT52jTJrPyZdu36258vDwfGmUQGhWlkAweczxz7VrNXLtW/dq31+RXX1WJEpl3q+HR0Xp5yhTdio3VD++9p6YpJ9ripHTpsqpXr73Zn6NjfZUta62GDbvq/Pm9iog4mbZ8XNwdHTiwQpUq1TZ74tO9ezG6ciXE7Daq+PhYxcWln68gKem+9u5dJOnBE6TKlLHSCy9MT/fn4fF/kqT69TvphRemy8WlW7rtGU3fVq1M7XDbNrPyZbt2mdrhQxd1oVeuKCSDC6yZv/6qmb/+qn6urpo8dGiW7TDV0QsXdDI8XF2bNlWlChXyfiB4bFwX5R11WHD6tmxpqtvt283KlwUFmfqoVq3SykKjoxXyyGM0r2Ryi3FQcLCCIyLUopj9YPFXU7p0WTVu3N7sr1q1+rK0tFaLFl116tReXbz44Lx+794d7dixQk5Otc2ethMbG6PLl0PS3cJy9Wq4pk59WbGxt/Tuuz9kGe7cvJnxL6MnTwYpPDxYdeumfxJSYejbp4/pM+DtbVa+bPly3b17V54P/VIbGhqqkLNnH3v9p556SpUrV9aKFSvS5pSRpJMnT2rvvn3q3atX2pwzdWrXVsmSJeW/ZYtuPfI5W7VmjaQHwY8keXh4qGTJklq+cqUSH5rX4eTJkzp06JDaurqmzZlTmLJqg82bZ9wGd+5cIUfHnLfBr782tcF33vkh03CnTBkrvfHG9HR/L75ourZs0qST3nhjulq0KJxryyIZUdOwdm0N7dtXPn5+GjV5stzbtFHIxYvy9vWVa9OmZifTER99pPCoKJ165MS7Zts2XUr59nzt5k0lJCZq1tKlkqRqjo7q37WrJOnCpUt6feJE/aNtW9WuVk1WKTPzr/T3V8kSJfTpv/9dSEedvxrWqaOhzzwjn7VrNWriRLm7uiokNFTea9bItXlzs6BmxIcfKjwyUqce+TK6xt9fl1JOpml1uMj0Zaiak5P6pwxDDY+M1Cvjxunm7dt6qX9//XH8uP44ftxsWz06dFC5Yvy0kKAgydnZNCLmyBHTZMCNGplufdqwwXzZOnWkbt2kffuk/ftNZVevmv4elXrtceGClDI3WJoGDR48TcrS0jRHTuq1zu3bUiE8Yf1vr2GNGhrarZt8tmzRqBkz5N68uUIuXZK3v79cGzaU50NPbRoxZYrCr17VqQUL0soW+ftrxurVqlapkto3aaL1QUFm23eoWFEdUk6Qt+/e1ctTpig8Olovde+ucxEROvfIF+4OTZrk+JHeRtS9+1idPRukn38eqXbtRsjS0lr79y9XTEykhg6dazbC488/N2v16vHq0mWUunUbLUm6evWCfvxxmJo06aVKleqoXDlb3boVqaNHfRUdfU4tWz6r2rVNoUXJkqXVpEnvdPuQ+tQne/uaGb5uRA2dnTW0Uyf5BAZq1Pz5cm/SRCEREfIOCJBr/fpmQc2IGTMUfu2aTs2cmVa2aPt2zfDzUzU7O7Vv2FDrUzumFA4VKqhDo0bp3nfF7t2SpIEGmjjw74rroryjDgtOw2rVNLRDB/ns3KlRP/0k90aNFBIZKe8dO+Rar55ZUDNi9myFX7+uU//9b1rZJytW6MqtW3J74glVs7NTXGKijoeFacPBg7IuW1bjnnmmKA4LOfDcc2P1559B+u9/R6pHjxGysrJWYOBy3bgRKS8v8/P6wYOb9dNP4+XpOUr9+pnO6/fu3dZXX72s6Ohwdev2kiIiziki4pzZezRu3EE2NqZbcXx8PtHNm1fk4uKmSpWqKSEhThcuHNfevRtkaWmd6eTDBa1hgwYaOmSIfH75RaO8vOTeubNCQkLkvWiRXJ96yixoGTFypMIvXdKp1LkVcrl+6dKl9T/jx+vdsWM19KWX9Pzzz+vO7dtasHCh7O3s5DVqVNqytra2Gv7SS/pxwQL1HzBAgwYOlI2Njf44eFDrfX1Vs0aNtNueJNMkwq+NHKm58+dr2PDherpPH928eVPePj6ytLTUB++/X8A1mXvPPTdWJ08Gadq0kere3dQGd+wwtcHRo83b4KFDm7VgwXh5eIzSM888aINff/2yrl7Nug1WrOigUqVKq3Xr9NeOqU99qly5ZoavF5QiCWokacJrr8nZ0VFLN21SwP79sqtYUcM8POT14os5+iVw5ebN2vvIM+unp4QMrk2bpp1MHezs1K5FC+05elTrt29XXHy8KtvZqW/Hjvrn88+rXvXq+X9whWTCm2/K2clJSzdsUMDevaY67NdPXsOH56wON27U3iNHzMqm//yzJMm1efO0oCYsIkI3UlLaGY8kwam2LFxYrIOaW7ek1aslNzfpySdNtzFFR0u+vg9GxeQ3FxdTOPSw1LsPwsMJagrLhKFD5ezgoKXbtyvg8GHZlS+vYd27y+vZZ7P9HB1NeczhpatX9eH8+eled23YMC2ouXH7tsJS7qHzzmR41cIPPyzWQU2lSrX02muLtXnzVO3cOU/37yeoatXGeuml71WvXvZhQMWKTmrRop8uXNivP//0V1zcHVlalleVKo3l7v6Wmjf3LISjKBoTBg6Uc6VKWrprlwKOH5edtbWGubvLy8Mj+3aYMmLy0vXr+jCDPtq1fv10Qc29+Hj5HTigqnZ26pRBiIPCx3VR3lGHBWdC//5ytrfX0t27FXDihKmP6tRJXr17Z1u3T7dqpbX792vtgQO6dvu2LCRVs7fXC+3a6dWuXVXNzq5wDgK55uRUS+PGLdaqVVP166+m83rNmo31zjvfq3Hj7M/rt2/fSPuSu3Vrxt8h3ntvYVpQ4+r6tHbvXqvdu9cqJuaaLCwsVKlSNbm7v6BevV5VpUrV8u/gcmnC+PFydnbW0uXLFbB9u+zs7DRs6FB5jRqVo/4lN+v36d1blpaWmj1njr786iuVKVNG7dzc9N6YMeke2/3B+++rTp06Wr5ihebOm6f4hAQ5OTpqyODBGv3vf6t86q/CKca8+66cnZ21aPFifTl1qiwtLdXW1VVvjx6d9shvI3F0rKUPPzS1wY0b5ykx0dQGvbzyrw2OHbtQFSsab95Vi+Tk5ORMX7SwUPLJk5m9jGxYuLhIkpLPny/aHSnGLFJuDZk1K9Nmimy89ZaFsviYFwsWFhZKTnmEMHLPImXExJIlxbsdFKXBg02/2CQ/MjIROWfRs+dfoy/iuuixpV0XUYePLa0OfX2LeE+KLws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kWnn1SpU0adUq+R08KM/WrTNdv1eLFnqje3dVeOiX2iEdOqiWn5/mbN6sFXv2aFinTmmv/RwQoJIlSmjx22/Lvnz5tPInqlTR/y5dql8PHdKIbK5ljeznn01tcNq0Vapb19QGu3Xrr7fe8tDs2RM1Z072bbBv3yEaMCB3bbB27Ybq2rVfvhwDcu7bb79WTMwt/fLLKrm4mP69PT37a8AAD33xxUStWZP9v3dutjFy5D/TrT9kyEuaPHmili37RceOHVWzZs3NXre0LKennzZ+2+CcnnfB58/LZ+1a9ezYUTMeGildvUoVTZo1S34BAfLM5HtWKv+ff1aNqlUlSR6vv57p91x7W1utmT1bjerVMyv37NZN/d96S1/On69V332XxyN6PEUyR41vYKCSk5M1/JEL5EG9esmqbFmt27Yt222kNt7cSE5O1u3YWCUlJeV6XaPJtA579jTVYUBAttv4O9eh7969pvrr3t2sfFDnzrIqU0brdu/Ocv1mtWubhTSp+j71lCTpdHh4hutt/uMPbT10SBOHDVPJbJJg5B9ff3/Tv/egQWblgzw9ZWVpqXWbNhXo+pK03Nc0bPv5hwKOm7du6eSZM2rTokVaSJPq2b59JUmrNmzIdtv5zdfPz3S8w4eblQ8aNEhWVlZat359vq2/b98+RUVFaeDAgWkhjSQ1atRIrq6u2vDrr0pISEgrnzN3ripVqqQ3/mm6yLtz506m/dGePXtUu3bttJAm1bPPPitJWrVqVZbHUVh8d+ww1dcj4Vdaf759e7bbyGl/npycrHkrV6pJ3bppF3S3795VcnJy7nccBeLevVjt2bNVzZq5poU0kmRlZa1evQYqPPy8Tp8+mu127OwcZGmZ/W2KD4uNva1Zsz5V375D1KCBsW4lyI7vH3+YPkePfAEa1K6d6by+f3+W6zerWdMspEnV98knJUmnL182K799757KlColm0euBRxtbCQp21utjCy1DTZt6poW0kimNtizZ8G2QUmKj4/TvXuZfzFF/rp7N1bbt29VmzauaQGLJJUrZ63/Z+++o6I6+j6Af+kgCCxSFFBQpIiIDVGxx44Njb2i0WgimmISo3k0j0k0RU006mOJJip2sYJGkSD2XhFFEUGQ3qR3eP+4LLDu0tuS9/s5h3N09t7ZvbNz587+7szcsWPH4/XrUDx5Uv73XRt5AECLFsYAgNTUZJmvFxQUIC0tTa6vWbym15zXxYtCGRb118QmOjsLZVjGkialiYM0FRFpa0sFaQCgrZkZrMzMEBQaWql86kKD/FJ88uIFFBUVYW9tLZGupqoKmzZt4B8UVCfv23XiRHSdOBH248Zh9n/+g0fvDJNvTJ4EBQll+M60IzVVVdi0bl13ZTh5MrpOngz78eMxe8WKRluGT0JDoaigAPvWrSXS1VRUYNOyJfyreVJGJyUBAPS1pYfppmVm4rsDBzCpXz/Yt2lTrfypep4EBgrnS7t2EulqamqwsbSEf2Bgne4fHhmJW/fvo6u9PdqUmuuaUxSA0JAxX1mjqJP/KCCg3LzrwhN/f+F47SXvZqmpqcHGxgb+/uV3tqqyv/jfnWUMK+3UsSPS0tIQWnQ+ZmRk4O7du7C3t4eHhwf69O2LLl27onOXLvjs888RHx8vsX9OTg40ZPzwEqc9fvxYLjozFbbnL1/W2nu9iohAWHQ0OtvYYMvhw+g+Ywa6TpkCh2nTsHLrVqSXc9eO6kdIyHPk5ubAxkb6nBCnVeZHcnXs3v0rCgryMWuW9DRnefckLEy4rpuZSaSrqajAxsQE/mFh1co3+u1bAID+O2vJ9baxQXp2Npbu34/AiAjEvH2LK8+e4aeTJ2FhZFQc4GmMxHWwXbuy62BQUN3UwZMn/8T773fE+PGd4OraD/v2bURubk6dvBcJXrx4jpycHNjbS3/fHToIaQEB5X/f1c0jPT0NSUmJePMmHF5eJ7F7907o6urCzk56JHFcXAycnDqjd++u6NmzMz7/3A0hIcGVOsb6xGt6zZUbK7CwgP+LF3X+GQoKChCbmAh9kajO36ssDTL1KTYxESJtbaiqqEi9ZtSsGR48e4ac3FyZr1eHvkgE1zFj0L5tWzRRV0dgSAj2nDqFaUuXYsd//wunBpp3VhOxiYkQNW1adhkGBtZNGVpYlJTh6dOYtmwZdqxc2ejKMPbtW4i0tGSXn0iEB8HByMnLg6py5U+R/IICbPXygrKSEkbKmDa11sMDhYWFWDKu4oUcqXbFxsdDpKMDVRnrBhnp6+OBv3+550tN9z9WNMJkwjt3V/T19CDS1cXDgABkZWdDvdQd2JsPhCHiUbGxlT7O2hIbGwuRSCT7eI2M8ODBA+Tk5Mh8var7xxYd37vr0ACAYVFaTEwMLC0tERYWhvz8fDx69AjXrl3Dh/PmwcbGBnfv3sVed3c8f/4cxzw8igMxlm3b4mVwMOLi4mBgYFCc761btwAIgZ/k5GToNvA6EuW253p6tdqehxSN9jt77Rpyc3Px0cSJMDU0hN/duzh8/jxCIiKw9/vv5Wbtnv+PEhKEc0JfX/qcEKclJMTU+vsGBj7E2bMH8eWX66Gp2fgWuI9NSYFIU1PmddtIRwcPQkKqd1339oayoiJGvjMyb/7gwUhIS8OxW7fgee9ecXo/W1v8OnMmtCqxYKi8SkwU6mCzZtJ1UJxW23VQQUER9vY90LPnIBgaGiM5ORFXrpzDoUP/Q2DgQ6xatbPBFtf/t4uLE75vQ0MZ1+GitNjY8r/v6ubx7bfL4eNzvvj/HTp0xLJlK6H9zg1PExNTdOrUBVZW1lBUVIS//yMcPrwft2/fwF9/HYClpfW7WTcYXtNrLjYhQYgVlNXvfvq0Vn/nynLQywtxiYn4eNq0OnuPijRIoCYzO7vMglUrSs8qZ5uq+sLVVeL/g3r2xMh+/eCyeDH+u2ULvCtYRVselVuGRZW6VsvwnSkMg3r0EMrwk0/w361b4b19e628T33JzMmpuA7m5FSpQ7fm0CE8CA7G5+PGoc07QxbvBQXh8OXLWDd3rswpU1S3MrOyKj5fytmmJvvn5+fjxN9/Q0tTE8PemTusoKAA14kT8duOHXBbvhyL586FSEcHN+7exaZdu6CspISsBlhwOjMrq8wgjPhJDFnlbFOV/TOL5gzL2r70tgCQVrQYXGJiIn74/ntMmDABADB48GBoaWlh85YtOHHyJKZOmQIAmD17Nr748kt8vHAhvvziC5iYmODR48dYs2YNVFRUkJubW5x3Q6rP9lx8dy0xORl/rVoFp6L1j4Y6OaGwsBAnLl7E5fv30a+ctTyobmVnC9+Rior0OaGioiaxTW3Jy8vFxo0r0LmzE/r1c67VvOtLZjnXbLWi9Cpf10+cwIPQUHw+YgTavBNMVlRQgJGODpysrDDI3h66TZrgfkgI3C9fxmd79uB/c+dCpZEGFsT1S1lZug6qqtZNHTQ0NMaaNXsk0oYMmYBNm1bg/PkjuHz5DAYMaPg1xf6NxNPMyr8Ol/99VzeP+fMXYvz4yUhKSsSdO7cQFPQcb4tGsZX23Xc/Svx/8OBh6N9/IObOnYF1637C9u1/lfv56hOv6TVX379z33U/IAA/bd8OmzZtsKCoT9kQGmTqk4aaWvGQ/3dlF6Wr1/HcXnMTEwzv0wevo6KKo5GNSbllmCMMEa3zMjQ2xvDevRtlGWqoqlZcB8v4oSnLhpMnsc/XF5P69sV8Z8lObk5eHla6u8OpXTuZI22o7mmoq1d8vpRz97Mm+1+9fRvRsbEYMWiQzClOH06fjgUzZ+L2gweYMG8eBk2ciB83bcLShQuh3bQptBogsKehro6cHNlDzbOLAkcVllcl9xeXiazt391W3KYpKipizBjJBQVdiuYx375d8vjOUaNG4T/ffIOQkBDMmDkT7w0ciK+++gqTJ02CjY0NAECr1CKgDaU+23Nxu2bUrFlxh07MpWhhvttPntTKe1H58vPzkZgYJ/GXnp4KNTVhRJis6R65ucI5Id6mthw9+geiol7j44+/rdV865OGqipy8mQ/qSq7KL1K1/WzZ7HvyhVM6tkT8wcPlnr96wMH4HHrFja6umJCjx4YbG+PpWPG4Jtx43D52TOcvC37UcLyJD8/H0lJcRJ/petgXp50HczJqZs6WJZJkxYAEJ4gRTWTn5+P+Pg4ib/U1FSoqwvfZfnX4fK/7+rmYWlpjR49nDB8+EisXPk9xowZBze3eXjw4J7Utu/q0sUBXbo44O7dW3Jx00WM1/Saa8jfuU9evMD8FStg2KwZdvzwQ3FgqCE0yIgaQz09vAwPlzlkKUY81KkOhzKJmRgaAgCSUlLQ+p2FPOUdy7BmDHV18TIyUnb5JSUJ06Iqeddt06lT2OrlhXG9emHVjBlSrx/w9cWrqCgsnTgRr2NKhn2mF11U3sTHIz0rCy1LTc2g2mWor4+XoaEyp+vExMdDpKtb7vlSk/09ZCwiXJqioiI++/BDzJ8xAy+Cg1EIwKZtWxQUFGDl2rXo1L59FY60dhgaGuJlcLDs442JKXNaU3X2NyxqQ2JiYmDxzmJusUXni3haVPOikWraMobDGhadPynJkgsQzpgxA5MmTcLz58+Rk5sLy7Ztoa2tjf0HDsDAwEAuAjWGenp4+eaN7PaonKnC1dFcXx8AoC9jupdB0TzslLS0WnkvKl98fBRmzx4okTZo0FgMHz6p6HXpaQLiNFlTUqorMTEWhw9vw8CBLigsLERk5GsAJVNbUlLeIjLyNfT0DKq1MGx9MdTWxsvoaJnTm2KSk8ucFiXLpr//xlZvb4xzdMSqdxaRB4DIpCR43ruH6X36SAV/hnXqhP8ePYrbwcGY0LNn9Q+oHsTHR+GDDyTr4MCBYzFsmFAHZU1vEqfVZh0sj75+CygqKiElJale3u/fLDo6CiNGSH7fo0aNxfjxwvcta2qSOE3WlKbSDAwMa5wHAIwYMRpr1qyCh8chdO5c8SgQY2NT3L17GykpyeXeQKpPvKbXnGGzZngZFlZ2v1tHp05+5wYEBWHOsmVoqqmJvWvXwqiofBtKgwRq7KyscPXBAzx+/hwOdnbF6dk5OQh89UoirS6FRkYCkF255Z2dpaVQhi9ewKHUD7nsnBwEhoRIpNWlxlqGdubmuBoQgMchIXAotdhXdm4uAsPDJdLKs+nUKWz29MRYJyesnjVL5hzQiMREFBQWYt6GDTLzmLB6NZqoqeFBAz367f8DOxsbXL19G4+fPZN41HV2djYCg4IqfPx1dfdPSErCxWvXYNO2LToUjeAoSxMNDXQq1fadK1rxvm+PHpU5xFpl16EDrl67hsePH8PBwaE4PTs7G4GBgRJpNd2/QwfhyTIPHj6Ek5OTRD4PHz2ClpYWzM3NAQD6+vowNjZGVFQUMjMzJRYKjo6OBgDoNWsm9XlUVVWL3wcQFjBOTEzE+PHjKyqKemFnaYmrDx/WS3tuZWYGNVVVxCYmSr0Wk5AAQHhUJdU9kcgAq1dLDtdv1swQBgYtoKKiisDAh1L7iNMsLWuvn5SUlICcnGz8/fdh/P33YanXjx7dgaNHd2D58o3o3XtYrb1vbbNr1QpXnz/H49ev4VAq6Judm4vAiAg4VHIR/01//43N589jbLduWD15sszrekzR1Ix8GU+cE6fl5+dX5zDqlUhkgB9+kKyDenoldfDZs/qpg+WJjg5HQUE+dHWl23aqGn19A2zbJvl9GxgYokWLFlBVVcXjx9Lft7+/kNa+ffnft6WlVY3zAIDc3FwUFBQgOVn2U5/eFRYWCmVlZejoyM91i9f0mrOzssLVe/eEWEGp/lt2Tg4Cg4Ml0mpLQFAQZn/9NTQ1NLDnl19gImPtxPrWIFOfnPv0gYKCAvacPi2RfuT8eWRmZ2NU//7FaWFRUQgOD6/2e2VkZRUPkSrtaXAwzl29CouWLdGqko/vkifOvXvLLkNvb6EMSz2eMiwqCsFv3lT7vcotw2vXGmUZOnfrJpSfj49E+pHLl5GZk4NRpaYohcXGIvidx3ICwGZPT2z29MSYnj2xxtUVimU8bvv9Xr2wccECqT/HopXM17i6Yu3cubV4dPQu54EDhe/7yBGJ9COensjMysKoIUOK08IiIhD8+nW19y/t5N9/IzcvD+NHjqzS501KTsZv27dDpKuLyS4uVdq3NjgPHy4c7x7J9QKOHDmCzMxMjCo1OigsLAzBr15Ve/9u3brBwMAAHh4eSC9agwYAAgMDcfv2bQwbNgwqpe6ajB49GoWFhTh8WPIH5cGDBwEA/fr2LffYsrOzsebHH6Gqqoo5c+aUu219ce7VSyivdx57XtyelzqmmrbnGmpqGNKzJ+KSknDh5k2J1w7+/TcANLq57I2VqqoaOnd2kvhr1aotNDQ00b37APj738arVyVPlMvMTMf58x4wNjaHtXXJE9XS01MRHh6M5GTpjnplNG9uiuXLN0r9TZu2CAAwcKALli/fCBsb+X6KkXPnzsJ59M6jb4/cuCFc10sFiMPi4xEcI33nf/O5c9h8/jzGODhgzZQpZV7XWxsaQklRET7+/kjJyJB47XjRYuUdSj3hT16pqqqhUycniT9xHXR0HIAnT6TroLe3UAetrGqvDgKQOWKmoKAA7u7CTS5HxwFSr1PVqKmpoUcPJ4k/C4u2aNJEE337DsDdu7fx/HnJ952RkY4TJzzQqpU57OxKvu/U1FSEhAQjKank+65qHvHxcTI/44ED7gAAe/uSG2CpqakyA5+XL/vh4cP76N7dqXgdHHnAa3rNOffvL5ThiRMS6UfOnhXKsGhaFwCERUYiuJpP9RN7+vIl5nz9NZqoq2Pv2rWVfrR3XWuQETXW5uaYNmIE9nl5wW31avRzcEBweDjcPT3haGcnEWRw/eYbRMTG4nnR9AGxk76+iCx6WkhiSgpyc3Pxv0OHAADGhobF8/JeR0Zi3rffYmCPHjA3NoZG0ROLjl24ACUlJXzn5lZPR127rM3NMc3ZGfvOnIHbmjUlZejlJV2GK1YIZfhOUOfkxYslZZicjNy8PPyv6MePsaEhXIoWPn0dGYl5q1ZhYPfukmXo4wMlRUV8t3BhPR117bE2NcW0AQOwz9cXblu2oF+HDgiOioK7ry8crawkAjWu69cjIiEBz3fuLE7b7+uLTadOwVhPD07t2sGzqGMmpq+tjV5FEXObli1h07Kl1Gfwe/wYADCgY0foNW18T9hoTKwtLDBt3DjsO3YMbsuXo1/PnggODYW7hwccO3XCqFLrD7h+8gkioqPx/OrVau1fmseZM1BTVcXoMgI5AHDpxg3sPHAAvbp1g76eHiJjYnDU0xMpqanY+tNPDXInxNraGtOmTsW+/fvhtmgR+vXti+DgYLjv2wfHbt0wqlTgydXVFRGRkXhe6hHlVdlfRUUF33zzDT777DNMmz4dEyZMQHpaGnbv2QM9PT0sXrRI4rPNmzsX3t7e+PmXXxASGgobGxvcu3cPnp6e6NGjB5xLrREVFBSEr5ctw4D+/WHUvDkS4uNx4uRJhIeHY82aNbCo5B32umZtbo5pw4dj39mzcPvpJ/Tr2lVoz8+cgWP79hKdOteVKxERF4fnJ09K5HHy4kVExgkd3+L2vCiwaGxgUNyeA8Dn06fjxqNHWPLrr5ju7AwTQ0Ncvn8ffnfvwmXAAHSpYPQX1T1X1yV4+PAm/vOfOXBxcUWTJpo4d+4oEhJisGrVdolRHtevX8Bvvy3D1KlumD695HyJiYmAr+8pAEBYmPA42Fu3LiI+Xhh99t57Y2BkZAJNzaYyR8poawvXNXNzK7keSSNmbWyMab17Y9+VK3D780/0a9cOwTExcL98GY4WFhhV6qlNrlu2ICIpCc9LjXTdf+UKNp07B2ORCE5WVvC8f18if/2mTdGr6AaLrqYmZvXrhz8vXoTLunWY2LMndIoWE/a8dw+t9PXlftpTRWbNWoJHj25i5co5GDNGqIPnzwt18NtvJevgjRsXsGHDMkyZ4lYc4AOA2FjpOnj7tmQdNDQUps1v2rQCGRlpaNeuM/T1WyAlJQnXr5/Hy5cB6NFjIHr1kv862JgtXrwEt2/fxEcfzcH06a7Q1NTE8eNHERsbg02bJL9vX98L+PbbZZg/3w0ffbSoWnmMHz8SnTp1Rbt2tjA0NMLbt0m4efM6bt26AUtLK0ybVvIQkzt3bmH9+h/Rt+8AmJq2hJKSMp48eYyzZ09DJBLhq6+W108hVRKv6TVn3bo1po0ejX2nTsFt1Sr0c3REcFgY3E+ehKO9vUSgxnXpUkTExOC5t7dEHid9fBBZFJAvLsP9+wEAxkZGcBk0CAAQEROD2V9/jeS0NMxwccH9gADcDwiQyGtwr15oolE/63KV1iCBGgBYPm8eTAwNcfj8efjduQORtjamjxyJxdOnl3kHo7Rj3t5SiyNt3LcPAOBoZ1ccqNEXidCzUyfc8veHp58fsnNyYKCnB+c+ffDhhAmwkPEDurFYPneuUIbe3vC7e7ekDKdOrVwZXrggXYZFFdjRzq64EdAXidCzY0ehDC9dEspQJIJz795CGZqa1v7B1YPlkyfDpFkzHL58GX7+/hBpaWH6e+9h8ZgxFZaff2goACAyMRFL//xT6nVHK6viQA3Jh+WLF8OkeXMcPn0afjduQKSjg+njx2PxBx9U6nyp6v73/f3x6vVrjBw8GDrvPGayNJPmzaGmqop9x47hbXIydHV00NPBAR/NmoU2DXhHdvny5TAxMcHhI0fg5+cHkUiE6dOmYfHixZUrryrsP3zYMKirqWHrtm345ZdfoKqqip49euCLL76Qemy3lpYW9u/fj40bN+Kff/7BsWPHYGRkhAXz5+Pjjz+WeHyrSCRC8+bNceToUSQmJkJLSwsODg5Y+8svsLe3hzxZ/sEH0u35iBFYXM5d/dKO+fjg9jsdi40HDgAAHNu3l+jUGRsY4PDPP+O3/ftx3NcXaRkZaNm8OZa6usJ1NJ+qIg+Mjc2wbt1B7N69DkeP7kBeXi4sLGzx/fc70bmzU8UZAIiJeQN3940Sadeve+P6daEz2759VxgZNZ615Spj+dixMNHTw+Hr1+EXECBc1/v0wWJn54qv60V3ZCOTkrC06NwpzdHCojhQAwBfjR6N1oaGOHrjBrZfuICcvDwY6ehgSq9eWDRsWKN+PDcg1MG1a4U66OFRUge/+24nOnWqXB2Mjn6DffvKroO2tl2LAzXduvWDr+9pnDt3BGlpyVBRUUGrVpb46KOVGD68cu0gVV+rVmbYvfsgfv99Hf78U/i+bWxssWXLTvToUbnvuyp5TJ06EzduXMPhwweQkpIMNTU1mJm1xqJFn2Pq1BnQ0ChZD8vcvDVsbe1w5YofEhISkJeXCyOj5hg/fjI++GCBVD9BHvCaXnPLFyyAiZERDp89C7/bt4UyHDMGi2fNqlwZnjuH20U3xcU2Fo30drS3Lw7UvImOxtuUFADAJnd3mXn9s3dvgwRqFAoLCwvLfFFBAYUvXtTn5/lXUSha56Sw1J1mqhqFoihw4eXLDfxJGi+Fvn1RzmneKCgoKKCwaPQXVZ1C0YK9hTLWU6DKUSjqFBQ+fdrAn6TxUrC1/Ve0RS9fNu5jaEht2wp31AvPnm3gT9J4KRSNGnzxgvWwuqysFBp9WwQI7VFGRuM/jobQpElRW8RrerUp2NoCAAqLbl5T1SmYm5fbFjE8TUREREREREQkJxioISIiIiIiIiKSEwzUEBERERERERHJCQZqiIiIiIiIiIjkBAM1RERERERERERygoEaIiIiIiIiIiI5wUANEREREREREZGcYKCGiIiIiIiIiEhOMFBDRERERERERCQnGKghIiIiIiIiIpITDNQQEREREREREckJBmqIiIiIiIiIiOQEAzVEREREtMB7eQAAIABJREFURERERHKCgRoiIiIiIiIiIjnBQA0RERERERERkZxgoIaIiIiIiIiISE4wUENEREREREREJCcYqCEiIiIiIiIikhMM1BARERERERERyQkGaoiIiIiIiIiI5AQDNUREREREREREcoKBGiIiIiIiIiIiOcFADRERERERERGRnGCghoiIiIiIiIhITjBQQ0REREREREQkJxioISIiIiIiIiKSEwzUEBERERERERHJCQZqiIiIiIiIiIjkBAM1RERERERERERygoEaIiIiIiIiIiI5wUANEREREREREZGcYKCGiIiIiIiIiEhOMFBDRERERERERCQnFAoLCwvLfFFBoT4/CxHVkXJO80aBbRHRvwPbIiKSB429LQLYHhH9G5TXFnFEDRERERERERGRnFCuaIPIyMYfcW4oxsZCpLvw9OkG/iSNl8Lo0QCAlBTWw+rS1v533HHx8GAdqK7x44vaosDABv4kjZeCjQ0AlmFNiMuwsbt9m21RdTk6FrVFsbEN/EkaLwVDQwDAkyesh9VlZ/fv6BcBwNmzrAfV4ews1IHTp1l+1TV6tFCG0dEsw+pq3rz8togjaoiIiIiIiIiI5AQDNUREREREREREcoKBGiIiIiIiIiIiOcFADRERERERERGRnGCghoiIiIiIiIhITjBQQ0REREREREQkJxioISIiIiIiIiKSEwzUEBERERERERHJCQZqiIiIiIiIiIjkBAM1RERERERERERygoEaIiIiIiIiIiI5wUANEREREREREZGcYKCGiIiIiIiIiEhOMFBDRERERERERCQnGKghIiIiIiIiIpITDNQQEREREREREckJBmqIiIiIiIiIiOQEAzVERERERERERHKCgRoiIiIiIiIiIjnBQA0RERERERERkZxgoIaIiIiIiIiISE4wUENEREREREREJCcYqCEiIiIiIiIikhMM1BARERERERERyQkGaoiIiIiIiIiI5AQDNUREREREREREcoKBGiIiIiIiIiIiOcFADRERERERERGRnGCghoiIiIiIiIhITjBQQ0REREREREQkJxioISIiIiIiIiKSEwzUEBERERERERHJCeX6fsOwsFfYvn0dHj26g7y8XFha2mL27EXo0qVnneTx4kUA9uzZDH//+8jMzICJSSuMGDEB48bNgJKSktT2eXl5OHXqAM6dO4Hw8BAoKSnB2LgVRo2ahNGjJ9fo2OtDQUEB9l69ikM3biAiKQl6mpoY3rEjFg8diiZqauXu+yo2FlsuXMDTiAjEpqQgLz8fLXR10a9dO3zQvz8MtbXr6SjqVmjoK2zatA73799Bbm4ubGxsMX/+InTrVvk6WN08goICMX36+8jPz8NPP23EoEHDJF7Py8vF3r27cPbsKUREhKNJE0106eKIhQs/hbm5RbWOlyovLu4Vzp1bh5CQO8jPz4WxsS0GDVoEC4vK1Y0rV/5EYOBFxMeHICPjLZo00YW+fms4Oc1E+/aDJbaNjw/Bgwen8fLlNSQkhCEvLxvNmrWCnd0w9Oo1C6qqTeriEOtEQUEB9np64tC5c4iIjYWejg6G9+qFxdOmoYm6eoX7bz96FAHBwQgIDsabmBiYGBrCd+dOmdt+vWEDTvj6ynxt49KlGNarV42OpaGwDOldkZGvcPjwOjx7JrRHZma2eP/9RWjfvuL2qLCwENeuncbDh3549eoJ3r6NRdOmIrRqZYMxYz5C27YdpfY5fXo7QkMDEBISgLi4N9DXN8GGDbLrSUMrKCjA3qNHcejUKURER0NPVxfDBwzA4rlz0URDo1b3LywshNeFC9h//DhCwsORk5MDYyMjDB84EK4TJ0JLU7N42xN//w2vCxcQFBKCpORkaDZpAjMTE0waMwZjhg6V2e+UZxERr+Duvg4BAUJ/u00bW0yevAgdOlTumhgU9BiXLp1GcPATvH79HFlZGXBz+xHvvTdOattNm77GxYsnysyrRQszbNniXe1joeqLiXkFT891CA4W2iJTU1sMG7YIVlaVa4vu3TuNgAA/hIc/QXJyLDQ1RTAxscHgwR/B3Fy6LUpNjcfff/+Op08vITU1AU2b6sPefhCGDVuMJk3+Hb9FACA29hXOnFmHV6+E88vU1BZDhiyCpWXlzi8/vz/x9OlFxMWV9DkNDVujd++Z6NBhcMUZyKmwsFfYtm0dHj4UysXKSvid37Vr1WIFlc0jJiYSe/duw/37NxAXFwNtbR1YWbXH5MkfoFOnbhLb5uXlwtPzCM6cOYbIyDAAQIsWLTFsmAtcXKZARUW1Zgf/jnoN1EREhMHNbQqUlJQwZcpcaGpqwcvrKL78ci5+/vkPODg41Woejx7dwRdfzIGWVlOMGzcDuroi3L17HVu2/IjXr4PxxRffS+Sdm5uD5cs/wsOHtzBo0CiMHj0Z+fl5ePPmNWJiImu9POrCmtOn4X71Kgbb2WFOv34Ijo2F+9WreBoRgd3z50NRsexBVDHJyYhLScFgOzsY6ehAWUkJL6KicOTmTZx5+BCnPvsMzZo2rcejqX1v3oThgw+E+jNz5lxoaWnhxImjcHObi99//wPdu1dcB6ubR0FBAX74YQXU1FSRkZEn9XphYSE+//xjXL9+Gf37D8KkSdORlJQED48DmD17MnbtOog2bdrWuAxItoSEMGzbNgWKikro23cu1NW1cOfOUfz111y4uv6Btm0rUzceQyQygbV1XzRpIkJmZjL8/c9h/343DBq0GO+9t7B427t3j+Hmzf1o1+49dOw4CkpKynj16hYuXNgAf/+/8dFHR6CiUvEPdHmwZtcuuHt6YnCPHpjj4oLgN2/g7uWFp69eYff335fb7gDAr+7u0G3aFLZt2iA1Pb1S7/nLZ59JpdlbWVXr88sDliGVFhMThu++E9qjkSPnQkNDC35+R/HLL3Px5Zd/wM6u/PYoNzcH27Z9BTOzdujZ0xkGBqZ4+zYO//xzCKtWTcL8+T+jd+8xEvscOfIrtLR0YW5ui4yM1Lo8vBpb8/vvcPfwwOC+fTFn8mQEv34Ndw8PPA0Kwu4NGyo8X6qy/4YdO7DN3R09unaF2+zZUFZWxu0HD7Bp1y5cvnEDh7dvh4KCAgAg4PlzaDdtimnjxkFPJEJGRgYu3biBZWvW4O6jR1izbFmdlkttio4Ow7JlQl/HxWUumjTRgo/PUXz33Vz85z9/oGPHiq+J9+5dwrlz+2Fi0gZmZtZ4/vxBmdsOGTIJ9vbSP6L8/W/C1/c4HBwG1Oh4qHri48OwcaPQFr33ntAW3bhxFNu2zcX8+X/A2rr8epCXl4N9+76CiUk7dO7sjGbNTJGSEodr1w5h48ZJmDbtZzg4lLRFqakJ+PXXiUhJiYWT0yQ0b26J6OggXLt2CMHBd/HJJwehqlpxMFbexceHYfNmoVz79xf6nLduHcUff8zF3Ll/wMqq4vMrPPwx9PRM0K5dX2hqipCRkYxHj85hzx43DB26GIMHL6wwD3kTERGGjz8W2p2pU0t+53/xxVysXVv5WEFl84iPj8HcueOQn5+P0aMnwdTUDAkJsfD0PIpPP52JH3/cip49+xdvv2bN1/Dx8UK/fkMxcuR45OcX4Pr1i9i0aQ2ePHmAVas21Gp51Gug5o8/1iMtLQXbtx+HpWU7AMCQIS6YPXskNm5chb17zxVf7Gojj99//wGKiorYsuUwjI1bAgBcXKZh/fqV8PQ8jCFDxsDe3qE47717/4d7925g/fo/0blzj7oogjoVFB2NfdeuYUiHDtg0a1ZxuqmeHn44eRJnHj7EqC5dyty/p6UlelpaSqU7tGmDT93dcfzuXcwb0LgvlJs3r0dqagrc3Y/D2lqoPyNGuGDixJH4+edVOHas4jpY3TwOH3bHq1cvMXPmB9i+fZPU65cu/YPr1y9j7NhJ+Oab74rTnZ3HYNKkkVi37gf873+7a3D0VJ7z59cjKysFCxceh7Gx8L127uyCjRtH4vTpVfjss4rrxpQp0g20k9MsbNkyDpcv70T//gugqCjcUbWzG4r+/edDXb0k+Nm9+xQ0a2YGP79tuHvXAz17Tq/FI6wbQWFh2OflhSE9e2JTqR8hpkZG+GHHDpy5cgWj+vUrNw+fHTvQsnlzAMBINzdkZGVV+L5jGnlbVBrLkN51+PB6pKen4IcfjsPMTGiP+vRxwdKlI7F79yqsXVt+e6SkpIRvvnFHu3aOEukDBkzE0qUjcODAz3ByGiURkPj1Vx8YGgp9pa+/HomsrIw6OLKaC3r1CvuOHcOQfv2wafXq4nTTFi3ww4YNOOPjg1FDhtTK/nl5edhz9CjaW1nhr99+Ky6vKS4uUFJSgqe3NwJfvkS7or7Tfz79VOr9Zk2ciA+//BLHz57FZx9+CINmzWqlHOravn3rkZGRgrVrj6N1a6EO9u/vgk8+GYk//liFTZsqviYOGzYFLi4fQF29Ca5fP1duoMbaujOsrTtLpV+6dBoAMHDg+BocDVWXl9d6ZGamYMmS4zA1FepBt24u+OmnkfDwWIXly8uvB4qKSnBzc0fbtpJtUc+eE/HTTyNw8uTP6NKlpC26cGEbkpIiMGPGenTtOrJ4e3PzznB3XwI/v78wZMjHdXCk9evsWaFcP/30OExMhHJ1cHDB2rUjceLEKnz1VcXn14wZ0n3OPn1mYcOGcfDz24mBA0v6nI3Fjh3C7/w//ij5nT90qAtmzRqJ335bhX37Ki6XquRx7txJJCcnYfXqLejTZ1BxHgMHjsTUqUPg6XmkOFATHx8DHx8v9OkzCN9//3vxtuPGTcOiRdPg53cOqanJaNpUp9bKo97WqMnMzMC1a77o1MmxuNAAoEkTTYwYMR7h4aEIDPSvtTxSU5MRHBwIe3uH4iCN2LBhYwEA584dl8j72LG96N17IDp37oHCwkJkZKTV+Ljrk9eDBygsLMSsPn0k0id27w4NFRWcvn+/WvmaiEQAgJTMzBp/xoaUmZmBy5d90bWrY3GABRDqj4vLeISFhSIgoOI6WJ08oqOjsHXrBnz4oRuaNzeWmffduzcBAKNHSw4JNjVtic6dHXD79g1ERzeOkV2NTU5OBgIDfdG6tWNxkAYA1NQ04eAwHvHxoXjzpvy6URYlJWVoaxshNzcT+fklI6lMTTtIBGnE7O2dAQAxMS+q9X71zevyZaHdGT1aIn3ikCHQUFPDaT+/CvMQBxiqorCwEGkZGSgoKKjyvvKGZUilZWVl4MEDX7Rr51gcpAEAdXVN9O8/HtHRoXj1qvz2SElJWSpIAwA6Ovpo184RKSkJSElJkHhNHKSRd14+PsL5MnGiRPrEUaOgoa6O097lT4+pyv55+fnIys6GfrNmUqN0DPX1AQAalZiaaGxkhMLCwkqPdmtoWVkZuHPHF+3bOxYHaQBAQ0MTgwaNR2RkKF6+rPiaqKurD3X16k/jjY2NwOPH12Fl1QmtWknfSKS6lZ2dgSdPfNG2rWNxkAYQ+kY9eoxHXFwowsIqboveDdIAQNOm+rCwcERaWgLS0kraopcvb0FFRR1duoyQ2L5zZ2eoqKjh1q3j72bV6GRnZ+DpU19YWDgWB2kAoVy7dxfKNTy8+n1OHR0j5ORI9jkbg/J+548cKfzOf/as+rECWXmkpwu/9fX1DSXy0dPTh6KiItTVS0ZvZWQI7XezZpLbKigooFkzAygqKkJVtfxlRqqq3gI1r149R25uDmxtO0m9Jk6rKFBTlTxycnIAQKKAxdTUhLSnTx8Vpz1+fBcZGemwsmqPTZt+gLNzFzg7d8WYMT3wxx+/Ii9P/iv7k/BwKCoowL5VK4l0NRUV2JiYwD88vFL5ZOfmIjE9HdFv3+Lq8+dY6eEBAOhnY1Prn7k+BQU9R05ODjp0kK4/dnZC2tOn5dfB6ubx00//hYlJS0yZMkvqNbGSOivd6ROnPXnySOo1qrno6OfIy8tBq1bS32vLlkJaVQI1GRlvkZaWiNjYYPzzz2YEBV1B69bdoaJScQOenBwNANDS0q/0+zWkJ0FBUFRUlJoyo6aqCpvWreEfFFQn79t18mR0nTwZ9uPHY/aKFXj0/HmdvE99YBlSaeHhQl/H0lK6PWrbVkirKFBTnsTEaCgrqzTatR6eBAYK50u7dhLpampqsLG0hH9gYK3tr66mhm4dO+LKrVvYsW8fXr95gzdRUTh+9iwOnjiB0UOHwryldIArNS0NiW/fIjQ8HPuOHcOxs2dh3rIlzExManDk9ef1a6EOWltL10ErKyGtMoGamvL1PY6CggIMGsTRNA0hMlLoG5mbS9cDcVpFgZryvH0bDSUlFWholLRFeXk5UFFRkxo1oaioCBUVdSQkhCMtLbHa7ykPoqKEcjUzky5XcVpVAjXiPmdMTDC8vTfj+fMrsLCoXJ9TngQHC7+x2revfqygqnl06yasyffbb6vw4MEtxMXF4Nmzx/juuyXQ0GiCSZPmFG9rbNwKJiatcPbsMXh5HUVU1BtERITh8OG/cPnyBUyb9iHU1Gp3yYJ6m/oUHx8LADAwMJJ6TV/fqGibmFrLQ09PHzo6Ijx9+hDZ2VkSBffggTByITY2qjgtPDwEAODhsQfKyipYsOBLaGvrwsfHE/v3b0d8fAyWLfu5cgfbQGJTUiDS1ISqsvTXaqStjQehocjJy5P5emlHb93C9ydPFv/fRCTC2qlT4dCmTa1/5voUFyfUH0ND6fojTouLK78OVicPb++zuHbtEnbtOgjlcsq+TRvhbtGdOzdhaVkSFMvKyiwO0ERHR5f7+ah6UlKE71VbW/p7FaelpJRfN0r79dehyMh4CwBQVFRG+/ZDMHr0fyvcr6AgHxcvboWiojI6dhxZ4fbyIDYxEaKmTaGqoiL1mlGzZngQGIic3FyZr1eHvkgE1zFj0N7CAk3U1REYEoI9p09j2rJl2LFyJZw6SV+c5R3LkEpLShLaI5FIuj3S0xPSEhMr3x6V9vDhJQQHP0bv3mNq/c5ffYmNj4dIRweqqtKLNhrp6+OBv3+550tV91/37bf4evVqrN+2Deu3bQMg3EFdMHMmPpk7V+Z7zPrkEwQUBT4VFBTg5OCAVV980WgWE05MFOqguL6V1qyZkJaQUL06WFkFBQXw9T0OdfUm6NXLuU7fi2QT9410dKTrgTgtObl69eDp00sIC3sMB4cxEgGF5s0t8fixN968eSYxiufNm2fIyEgGACQlRUFLS69a7ysPyitXcZ+zKuX600+Sfc4OHYZg3Lj/1vyD1rPyfueL02oSK5CVR5cuPfDZZyvx55+/45NPZhanm5qaY+vWIxIPclFWVsaPP27FmjVL8csv/ymVroJPPvkPXFymVniMVVVvgZrsbGHajKzVkMWdhays8qfWVCUPBQUFTJjgip07f8OKFW6YM2cxdHREuHfvBnbv3gQlJWVklZrDLx7OlJKSjN27vdCqlRCUGDDAGZ9+OgPnz5/E1KkfwsxMfp+8k5mbW2YQRq2ow5FVzjZig+zs0MbQEBk5OXgaEQHfgAAkNZLhuuUR142a1MGq5pGSkoz161fDxWUi7O2l516X5uw8Gn/+uRXbt/8ODY0mcHTsibdvk7B9+ya8ffu2Up+Pqic3VyhXZWXp71XcgRBvUxnTpm1GXl42UlJi4O9/Drm52cjJSQdQfsfCy2sNwsIeYMiQz2Fg0DgCo5nZ2WX+IFIr+iGUVc42VfXFLMlRaYN69MDIfv3g8skn+O/WrfDevr1W3qc+sQyptJycsq8z4vZIvE1VREeHYtu2ryASGWHq1K9r9iEbUGZWVsXnSznbVHV/FRUVmLZoAZdhw9C3h7B+4Xk/P2zdswdqqqr4aJb0SNn/LlmCtPR0xCYk4NKNG4hPTERyaioax+Sy8vvb4joo3qauPHp0DfHxkRg4cDw0NDQr3oFqnbidkdU3UlauflsUFxeKffu+go6OEVxcJNuifv1mwd/fB3v2fIqxY5ejRQtLREUF4cSJNVBSUkF+fm6V+mPyqLb7nLNmCX3O5OQYPH4s9Dmzs9MbXTCrvmMFYrq6erC2tkPXrk5o2dIc4eGhOHRoF5Yu/RC//74PRkYtJPIwNTWHjU0HdOnSA1lZWTh37gQ2bPge6upNMGyYSxWOuGK1HqjJz8/H27eSQ9LU1NSLpxvl5uZI7ZOTkw1A9jQlyXyqlsfUqR8iKysTR478hQULJgAANDSaYOHCZdi58zfk5+dLfEYAsLXtWBykERs61AUPH97Gw4e35DpQo6GigoTsbJmvZefmAgDUK9HRb66ri+a6ugCEoM2QDh0wfuNGZOXkYP7AgbX3getIfn4+kpIk66C6unpx3ahJHaxqHhs3/oLCwkIsWrSkws+tra2D//3vL6xcuRSrV68oTu/SxRGzZs3Frl1boaWlVWE+VLaCgnykp0vWDRUVdaioCN9ZXp7095qbm120XeWfMtC6dcnj/Lp2fR+HDn2O7dun4NNPz0BDQ/YiYxcubMDNm/vQrdsk9O8/v9Lv1dA01NSQUMb6Vdni6XxqdXvn3tzYGMN798bxf/5BSEQEWjeS6QViLMP/nwoK8pGSItkeqaqqFz/RRNZ1RtweVfWpJ7Gx4fjxR1cAwFdf/QFt7cbVgS9NQ10dCUlJMl/LLmcKcXX2z8zKwpQFC2BrbY3fVq0q3m7EoEH47Ntv8fuuXRg6YADavDPl3N7WtvjfLsOGYf22bZju5obTe/aglRydW/n5sutgef1tcR0Ub1NX/vlHmHY/aNCEOn0fEtqid6cTqaiUtEWy+kZ5edVrixISwrFliysUFID58/+QCiZYWDhg1qxfcfz4auzY8SEAYUHiHj3GIy0tEY8fX4C6euPoC5dVrrXd57SwKOlzOjq+j337PsfmzVPw5Zdn0KRJ7S1sW1vkKVbg6XkEv/66Crt2nUCbNiXTzx0de2Pu3HHYsWM9VqxYBwBISIjD/PnjMWLEBCxY8EXxtkOGjMbChVOwYcN36NVrQK0uJlzrgZrY2ChMmSL5Y37o0LEYPXoSANlTS8RDkMTTl8oiXuinsnkoKipi7tzPMG3afLx69QJAISwsbFBQUID161dKrHVjYCAsxKinZyCVtzgtNTWl3M/X0Ay1tfEyJkbm9KaYcqZFVcTG2Bi2JiY4cP16owjUxMREYfRoyc85cuRYjBsn1MHYWOn6I06TNVSuNAMDw0rnERgYgNOnj2H+/EVITn6L5GRhVExiorBoWkJCPMLDX8PIqEXxEOy2ba1x4MBJhIe/RlxcLAwMDNGypRk2bvwFAGBu3jhGWcir5OQorF0rWTe6dBkLR0ehbsia3iROkzUtqrK6dHHB48dnEBDgDQcH6U6nj88mXLy4FV27joOLyyoZOcgvQz09vAwPlznVICYhASJt7VobCVIeE0Ph3ExKSWl0QQaW4f9PCQlR+OwzyfaoT5+xeO89oT1KSpJuj8RTnmRNSSlLXNwbrFkzC1lZGVi2bDdatrSuwadueIb6+ngZGoqcnByp6Usx8fEQ6eqWe75UZf/zFy8i9M0bfL5ggVQ+wwYMwNl//sG9x4+lAjXvchk+HDv27cPxs2fx6bx5lT3UOpeQEIUFCyTr4IABYzFkiFAHZU2xE095Ek+BqgupqUm4ffsftGplJXOdHKpdSUlR+P57yXrQrdtYODkJ9UDWNBxxmqzpO2VJSHiDzZtnIScnAx9/vBvGxrLbok6dhsPefggiI18gOzsdhoat0bRpM/z663goKipDX9+s0u/ZkN6+jcKaNZLl6uAwFj16lF2u4j5nVcr1XQ4OLnj48Az8/b3Rvbv8BTpjY6MwaZJkuQwbVn6sQJxWk1iBrDz27duOVq3aSARpAMDCwhqtWrXBw4d3itM8PQ8jOfktBgwYJrGtoqIi+vUbioCAh3jx4im6du1Z7mesiloP1OjpGWDdur8k0vT1DWFo2AIqKqp4+vSh1D7iNGtru3Lzbt3aqlp5aGg0kVhUyM/vHAoLC9G9e9/itHbtOgAA4uKk1wARf7G6uvL9SEW7li1x9cULPA4Lk1hPJjs3F4ERETVaYyYrNxfJGfL5qM53NWtmgC1bJOuggYEhmjcXAiL+/tL158kTIc3Wtvw62LatVaXziI6OQmFhIbZt+x3btv0utf3atd8DAPbu9YCtbQeJ11q2NEPLliUXouvXr0BTUwsdO5b9eHWqmJaWAebMkawbTZsaQle3BZSVVREWJv29hocLaaam5deN8ojvkIjnV5fm47MJvr6b0aXLWIwdu7rCxw7KGztLS1x98ACPX7yAQ/v2xenZOTkIDAmRSKtLoZHCE9H0i0YDNiYsw/+fdHQM8PXXku2RSGSIZs2E/lJQkHR79PKlkNa6deXao7i4N1i9eiYyMlKxbNlfMDe3rXgnOWdnY4Ort2/j8bNncOjYsTg9OzsbgUFBEmk13T8mPh4AJEZgi4nT8ivxsInsotHOySnydcNPV9cA334rWQf19Ayhry/UwefPpevgixdCmoVF9a+JFfHzO4W8vFwMHPh+nb0HldDWNsBHH0nWAx0dQ4hEQt8oNFS6HojTWrasXD0QgjQzkZWVio8//gumpuW3RYqKShJr1KSkxOHNm2do27ZblUfxNJSmTQ3w4YeS5aqtXVKur19Ll6s4ra76nPJAT88Av/4qO1agqqqKgICyf+fb2JRfLm3aWFUpj/j4GBgbyw605+fnSbT94nVK8/Oln5QpfsJWbT9pq9YDNWpqanBwcJL5mpPTAFy5cgEvXwaibVthsdSMjHScOeMBU1NztGtnX7xtWloqEhJioaMjgq6uMCyuSRPNKuUhS3JyEnbu/A06OiKMHj25OL1Fi5aws+uCgIAHePEiAFZWQsc4Pz8fZ84cgZKScvHK0PLKuVMnbPf1xZ4rVySCMkdu3UJmbi5GdSn5kR8WH4/cggJYGJY8YiwuJQUG2tJPgbj58iWCoqPhaCG/075KU1NTQ/fusutgnz4DcPHiBbx4EQgrq5Jm/pZ+AAAgAElEQVT6c/KkB1q1Mkf79pJ1MD4+Frq6knWwsnm0b98BP/20Ueoz3Lt3G0eP7sf06XNgZ9cRpqbl34k7dMgdwcEvMG+eGzQ0qv+YSxLm/rZtK7tu2NgMQEDABURFBaJFC+F7zc5Ox927HmjWzBympiV1IysrFSkpsdDUFEFTU6gbOTkZKCwshJqa5Fz6goJ83Ly5H0DJE6TE/vlnM3x9N6Nz5zEYN26N1ONfGwPn3r2x/ehR7Dl9WiKgcMTbG5nZ2RjVr19xWlhUFHLz82Fhalqt98rIyoKSomLxOhJiT4ODce7aNVi0bIlWLVqUsbf8Yhn+/6SqqgY7O9ntUefOA3DnzgW8fh0IMzOhPcrKSoefnweaNzeHhUVJe5SRkYq3b2PRtKkITZuWTCOIj4/AmjUzkZGRgqVL/6x0cEfeOQ8ciO3u7thz5IhEUOWIpycys7IwasiQ4rSwiAjk5uXBwsysWvtbmJsDAE6eOwfnd0YUn/j7bwBAh6KnR+Xl5SE1PR0iHelh7+5FT8/sWE9B18pSVVVDx46y66CDwwDcunUBISGBaN1aqIOZmenw8fFAixbmsLQsqYPp6alISoqFtraoVqbV+fh4QFlZBf37j6lxXlQxFRU1WFvLrgft2w/A48cXEBERCBOTkr7RzZseMDAwh5lZST3IzCzpG5We0pSYGIEtW2YiMzMFH3/8Z6WDO2IFBQU4fvwHFBbmY/Bg6dFt8kpFRQ1WVrLL1dZ2APz9LyAyMhDGxiXleuuWB/T1zdGqlWS5pqZK9jmzszMAyO5zXr8u9DllPVVKHlQUK7h8Wfp3vpdX1WIFlc3D3LwtgoOfIyDgocSgjidPHuDNm1D06NGv1LbC7+Bz547D1rYkj7y8XPj4eEFJSRmWlrV7M6TeFhMGgHnzluD+/Zv48ss5GD/eFZqamvDyOoq4uBj89NN2iTvJV65cwM8/L8OsWW6YPXtRtfK4efMSDh3aCQeHXtDT00dMTCTOnDmK1NQUrF69tfhLFfvkkxVYtGgaliyZjXHjZkBHRxe+vmfx7NljzJq1EEZGxnVfSDVg3aIFpjk5Yd+1a3DbvRv92rVDcEwM3K9ehWObNhjVuWQxW9ft2xGRlITn69YVp/33+HHEpaSgR9u2MBaJkJ2Xh4A3b3D24UNoqqnh61GjGuKwapWb2xLcuXMTbm5zMHWqUH9OnBDqz4YNkvXn4sULWLVqGebNc8P8+YuqnIeBgREGDRom9RkyM4WRSXZ2HaVeX7x4HkxMWqJNGwsoKCjg5s1r8PPzQe/e/fHBB43n4tQYDR26BMHBN/Hnn3PQu7cr1NQ0cefOUaSkxGDmTMm6ERBwAceOLcN777lh0CChbsTHv8Yff0yHnd1QGBi0hoaGLlJSYvDokRfi40PQpctYtG7tUJzHjRv78c8/m6CrawwLCyc8euQp8Xm0tPRhaSnfwWEAsDY3xzRnZ+w7cwZua9agn4MDgsPD4e7lBUc7O4kgg+uKFYiIjcXz06cl8jh58SIiY4U7FYnJycjNy8P/Dh8GABgbGsJlwAAAwOvISMxbtQoDu3eHubExNIqeWHTMxwdKior4buHCejrq2sUypHdNnLgEAQE38fPPczBsmCs0NDTh53cUSUkx+OILyfbo7t0L2LFjGcaOdcP77wvtUWZmGlavnom4uAgMGTIDUVEhiIoKkXiPDh16QUdHv/j/V6+eRHy8MKoqJSUReXm5OHnyfwAAfX1j9O5du4skVpe1hQWmjRuHfceOwW35cvTr2RPBoaFw9/CAY6dOGDV4cPG2rp98gojoaDy/erVa+w9wcoJ9u3a4dOMGpi1ciMFF5+KFS5dw99EjDBswAO2thekbGZmZ6Dd2LAb37QvLNm3QTE8P8QkJ8LlyBU8CA9Gza1eJvOXd9OlL4O9/E999NwejRgl10MfnKBITY/DNN5J18NatC9i8eRkmTnTD5Mkl/aXY2AhcunQKABAe/hIAcPfuRSQkCKPX+/UbA0NDyWmWL148Qnh4EJychqNpU1FdHyZVYOTIJQgKuomtW+egf39XqKtr4saNo0hOjsG8eZL14PHjCzh4cBmGDnXD8OFCPcjKSsOWLTORmBiBPn1mIDY2BLGxkm2RtXUvNG0qtEXZ2en49dcJsLcfDD09U2RlpeL+fS+EhwdgxIjPYGnZo/4Ovg45OwvlumPHHPTtK/Q5b90S+pwffCBZrk+eXMDhw8sweLAbhg4t6XNu3Tod9vZCn7NJE10kJ8fgwQMvxMWFwMFhLNq0cSjr7eXWhx8uwb17N7FkyRxMmFDyOz8+PgY//ywdK/jxx2VwdXXDnDmLqpWHq6sbVqxYhM8/n40xYybD1NQcb96E4tSpQ1BWVsHs2W7F2zo7vw8Pj704efIgYmOj4ejYB9nZmfD2Po3g4OeYMuUDiES1O/umXgM1pqZm2Lz5IHbsWIcDB3YgLy8Xlpa2+OWXnWVG1mqSR/PmJlBVVcPx4/uQkvIWOjq66NKlJ2bM+EhqwWAAsLS0xZYtB7Fr1wZ4eOxBTk42zMwssHTpjxg+fFytlEFdWz5mDExEIhy+dQt+z55BpKmJ6b17Y/HQoRXerR/RqRNO3buHU/fuITE9HQoAjEUiTOrRAx/07w9jUeO/YLZsaYZduw5i06Z12L17B3Jzc2FjY4vff99Z5iicusijLPb2neDt/Te8vE4AAFq3boOlS1di3LjJjeaxno1Vs2ZmmD//IM6fX4dLl3YgPz8Xxsa2cHXdWeYonNJ0dIzQufMYhIbexdOnPsjOToe6uhaMjW3x3nsfo2NHyUBnRIQ/AODt20h4eCyVyq91a8dGEagBgOVz58LE0BCHvb3hd/cuRNramD5yJBZPnVqpUULHLlzA7SdPJNI27hfuCDna2RUHGfRFIvTs2BG3/P3heekSsnNyYCASwbl3b3w4YUK1R5nIA5Yhlda8uRlWrjyIw4fXwctL6OuYm9viq692ljkKp7S0tLeIi3sDAPD2dpe5zfLleyUCNX5+xxAYeFtiGw8PYVSojY2j3ARqAGD54sUwad4ch0+fht+NGxDp6GD6+PFY/MEHlTpfKru/kpIS/tq4ETvc3eF96RLWbd0KBQUFmJua4ouPPsLsSZOKt1VXV8e0ceNw59EjXL1zB6lpadDU0EDb1q2x8rPPMHHMmEZ1HW/Rwgxr1hyEu/s6HD8u1ME2bWyxYsXOMkfhvCs29g0OHpQcWXzzpjdu3vQGALRr11UqUMNFhOWLgYEZFi8+CC+vdfDxEfpGpqa2mD9/Z5mjcEpLT3+LhAShLbpyxR1Xrkhvs3Dh3uJAjZKSCoyNbXDvnidSUuKgqqqBli07YP78nWjXrk+tHltD0tc3g5vbQZw9uw6+vkK5mpjYYu7cnWWOwilNV9cIXbqMQUjIXfj7l/Q5TUxsMXjwx+jcuXHeXDc1NcOWLQexfbvwOz83NxdWVrZYu7ZqsYLK5tGnzyCsX/8nDh3ahbNnjyM9PRVaWtro1q03Zs36GJaWJdPvNDW1sG3bEezevQU3bvjh9u2rUFZWhrl5W3z55fcYObL22yyFwsLCwjJfVFBAZGSZL1MFjI2FiF3hO3c+qfIURo8GAKSksB5Wl7a2Aso5zRsFBQUFeHg07mNoSOPHF7VFgYEN/EkaLwUbYfgsy7D6FGxs/hVt0e3bjfsYGpKjY1FbVDT6i6pOoWjK+pMnrIfVZWfX+PtFgNAenT3b+I+jITg7C23R6dMsv+oaPVoow+holmF1NW9eflvU+BZEICIiIiIiIiL6l2KghoiIiIiIiIhITjBQQ0REREREREQkJxioISIiIiIiIiKSEwzUEBERERERERHJCQZqiIiIiIiIiIjkBAM1RERERERERERygoEaIiIiIiIiIiI5wUANEREREREREZGcYKCGiIiIiIiIiEhOMFBDRERERERERCQnGKghIqL/a+8+A6o42jYA3/SmAgpYAEERKQEUUew1gg17jw2NJTFqEk1MNHmTmNcYNZrYyyvGWFEgqIBdURSliCXYURQRpBeRXr8fK+DxHKqUJd99/dI5u3t2h9ln9zw7M0tERERERCLBRA0RERERERERkUgwUUNEREREREREJBJM1BARERERERERiQQTNUREREREREREIsFEDRERERERERGRSDBRQ0REREREREQkEkzUEBERERERERGJBBM1REREREREREQiwUQNEREREREREZFIMFFDRERERERERCQSTNQQEREREREREYkEEzVERERERERERCLBRA0RERERERERkUgwUUNEREREREREJBJM1BARERERERERiQQTNUREREREREREIsFEDRERERERERGRSDBRQ0REREREREQkEkzUEBERERERERGJBBM1REREREREREQiwUQNEREREREREZFIyBUVFRWV+aGcXF3uCxHVknJO8waBsYjo34GxiIjEoKHHIoDxiOjfoLxYxB41REREREREREQioVjRAunpDT/jXF8aNRIy3azD6iuuw8BA1mF1dev273jicvIk20B1DR0qtIGiy5freU8aLrk+fQAARYmJ9bwnDZecjk5970KNCA1lLKouG5s3sej8+Xrek4ZLbuBAAEBEBNthdRkb/zvuiwCeS9VVfB4VpaXV8540XHJNmgAAoqIYi6rLwKD8WMQeNUREREREREREIsFEDRERERERERGRSDBRQ0REREREREQkEkzUEBERERERERGJBBM1REREREREREQiwUQNEREREREREZFIMFFDRERERERERCQSTNQQEREREREREYkEEzVERERERERERCLBRA0RERERERERkUgwUUNEREREREREJBJM1BARERERERERiQQTNUREREREREREIsFEDRERERERERGRSDBRQ0REREREREQkEkzUEBERERERERGJBBM1REREREREREQiwUQNEREREREREZFIMFFDRERERERERCQSTNQQEREREREREYkEEzVERERERERERCLBRA0RERERERERkUgwUUNEREREREREJBJM1BARERERERERiQQTNUREREREREREIsFEDRERERERERGRSDBRQ0REREREREQkEkzUEBERERERERGJBBM1REREREREREQiwUQNEREREREREZFIMFFDRERERERERCQSTNQQEREREREREYkEEzVERERERERERCKhWNdfGBHxFJs2rcONG9eRl5cHc3NLfPLJQtjbd6/xbVy5cgl//30Yjx+HISUlCUpKytDXN8CwYSMxbtxkqKioSCyfl5eHfft248SJ44iOfgF1dQ3Y2dnjs8++QJs2JjVy/DWhLuvwXWFhDzF16ljk5+dj7dqNGDhwsNQy+fn5cHc/BG/vo4iIeAZFRQUYGLTGmDETMW7cpCofb12JiXmKI0fW4eHD68jPz4OxsSXGjFkIS8uK67WoqAjXrnnh9u1LePbsLlJT49GokTaMjMwxYsSnMDHp8M53PcO1a164e/cq4uMjkZeXAz291rC3H4xBg2ZARUW9tg6TyhEX9xTe3usQHn4dBQV5MDCwxODBC9G+feXawI0bXrh37xJevLiLV6/ioaGhDX19czg4fApj4w5S67x+nYhTpzbh/n0/vH6dhMaNdWBjMxCDBy+CunqT2jjEWlFYWIh958/j8OXLiE5MRNPGjTGkc2csGjUK6u/E2Xc9i42FV2Agrt6/j8j4eOTk5aG1nh4G29lhhoNDhesfungRKw4eBAAE/PEHmjZuXGPHVRsKCwuxz80Nh48dQ3RsLJpqaWHIgAFYNGcO1NXUamz9oJs3MX3BgnK3dWjHDtjZ2JT8/2VsLHbs3YuAkBDEJSRAs0kTfGBmho8/+ghdbG2rf9BULdHRT3HgwDrcvy9ck9q0scTEiQthbV25a/3jx6G4fNkLT5/exfPnj5CdnYnPPvsV/fuPkbl8Wloyjh93wfXrvkhMjIG6eiMYGJhg6NDpsLcfWJOHVmsKCwux7+JFHPb3R3RSEpo2aoQhdnZY5ORUcSyKi4NXcDCuPniAyMREIRbp6mKwrS1mDBhQ5vqX7tzBX76+uBcZidz8fLTQ1kZPCwv8MHFibRxinXrx4ilcXNbhzh3hXtHU1BLTpi2ErW3l2uDDh6G4cMELjx/fxdOnQhv86qtf4ego3QYfP76Hixd9cPt2IGJjowAArVq1hqPjGAwdOgGKiko1emxUNp5HlVdYWIh9rq447OmJ6JgYNNXWxpCBA7Hok08qf02v5PrT5s5F8M2bMrfjsW8frC0ty/yeh48fY+zUqcgvKMDG1asxeGDDiOkViYx8il271uGff4TrpKmpJWbMWIhOnSoXox48CMX5814IC7uL8HAhRi1d+isGD5Z9naxrdZqoefEiEjNnToaCggJmzJiNRo0a4ehRdyxYMBubN+9C1649anQbT56EQUFBAaNGjYWOjh5ycrJx61YI1q//Ff7+fti27U/IyckBEH5kLV48H1evXkb//gMxadJUpKSkwN39EJydJ2HPHle0bduu1uqmsuq6Dt9WWFiIlSv/A2VlZeTn58tcJi8vF1988SlCQoIwZMhwjB07CQUF+YiMfI6YmJfvdey1KS4uEj//LNTJsGGzoabWCJcuueO332ZjyZJdsLIqv17z8nKxc+dStG5tgW7dhkJX1wCpqQnw9T2Mn3+eiLlz16Bnz5Ely1++/DcuXDgIW9sB6N59OBQVFXH/fhA8PDYgKOgUfvzRDcrKqrV92PSWxMRIbNw4GfLyChgwQGgDAQHu2LFjNubN2wUzs/LbQH5+Lg4cWAp9fQvY2g5Fs2YGSEtLwNWrh7Fx40RMmbIGnTuXtoHXr5Pw++8TkJYWjx49JqJFC1PExj7G1auHER4egs8/d4WycsUXeTFYdeQI9l+4AAdbW8xydER4TAz2+/ri/osX+GvxYsjLl915829/fxy8eBEDOnbE8K5doaiggKCHD7Hh2DGcCgmB2/LlUFVWlrluXGoq1nt6Ql1FBZk5ObV1eDVq1caN2O/uDoe+fTFr8mSER0Rgv7s77oeF4a9Nm8qtq6qsb2JsjLU//CC1fm5eHn5YswbampqweeumLi4hAWNmzUJBQQEmjhwJI0NDxCcmwt3LC9MXLsT2NWvQr2fPmq0MKlNsbCS++064Jo0cORvq6o1w/rw7Vq6cje++2wUbm4qv9Tdv+uHMmYNo1aotjIzM8OjRrTKXzcnJwnffTUZSUgw+/HACjIzMkJ6eikuXjmLt2s8wZ86PGDToo5o8xFqxysMD+y9dgkOHDpj14YcIj43F/osXhVi0aFH5sSggAAf9/DDAxgbDu3QRYlFYGDZ4e+PUzZtw+/prqVi05cQJbD5xAr0sLbHQyQlqysp4mZyMR9HRtX2ote7ly0h88YXQBsePnw0NjUY4dcody5fPxi+/7EKnThW3weBgP3h7H4ShYVu0bWuG+/fLboNubi64desaevQYiCFDxqOwsBBBQRexZcvPCAjwxapVLiX37FS7eB5V3qrff8f+w4fh0L8/Zk2divBnz7D/8GHcf/QIf23bVvE1vYrra2tpYdnixVLbMdTXL/M7CgsL8Z+VK6GsooL8zMzqHagIRUdHYtEiIUZNmiTEqBMn3PHNN7OxevUu2NlVHKOCgvxw/LgQo0xMzHDvXtkxqj7UaaJmy5b1eP06DQcPesLMzAIA4OQ0CuPGOWH16hXw9DxdYRCuyjZmzpwrtf6kSdOgpbUC7u6HcO/eHVhZCU8TL126gKtXL2PMmIn4/vufS5YfNmwkJkxwwtq1K7Fjx181UQ3vpa7r8G2HD+9HePgTzJjxMXbs2Cxz27t2bUNwcAC2bfsTXbp0e8+jrTvu7uuRmZmGn3/2hJGRUCe9eo3CsmVO2LdvBdasKb9eFRQUsHz5fpib20uU9+s3AcuWDYOr6xp07z68JOB26TIIw4fPg7p66dP/AQMmw8PDCF5eO+Dn5wEHh6m1cKRUFh+f9cjKSsOSJZ4wMBDaQJcuo7B6tRM8PFZg+fLy24C8vAIWLNiPdu0k20D37hOwevUwHDu2Bp06lbaBc+d2ICUlGtOmrYednVPJ8sbGtti/fwkuXdoDR8f5tXCkNetxdDQO+PrCsVMnbJ5fur8GOjpY6eqKE9evY3jXrmWuP6hzZ8wbOhSN1Ut7kU3u1w9GR49ix4kT8PD3x9QBA2Su+/PBg2itq4t2rVrBKzCw5g6qljx++hQHPDzg2K8fNq9aVVJu0KoVVv7xB06cP4/hjo41sr5O06YYOVi6x6PP2bMoLCzEyCFDoKRYegtw7NQppKSmYuvq1RjYp09JuZODAxwnTICblxcTNXXo4EHhmrRmjSfatBHiUd++o/Dll05wcVmBjRsrvtYPGjQZI0d+DFVVdQQEnC43UXP9+gXExERg5szlGDZsRkm5g8NEzJvXF+fOHRF9oubxy5c44OcHx44dsXlu6f2fQbNmWOnujhM3bmB4ly5lrj/I1hbzBg1C47eeYk/u0wdGXl7Ycfo0PK5dw9R+/Uo+u/bwITafOIFFTk74bOjQWjmm+vTnn+uRkZGGrVs9YWIitEEHh1GYM8cJW7aswO7dFbfB4cMnY/z4j6Gmpo7Ll0+Xm6gZNWoqvv56NZSVS3tcjBw5FatXfwVfX28EBV1Ct279a+bgqEw8jyrvcXg4Dhw5Asf+/bH5t99Kyg1atcLKdetw4uxZDJdxHX6f9dXV1DCyivW0/8gRPHn6FB9Pn47NO3dWaV0xc3FZj/T0NOzY4Yl27YQY5eg4CjNnOmHjxhXYu7fiGDVixGRMnCjEKD+/06JL1NTZHDVZWZnw8/OFnZ19SXIAANTVNTB69Dg8fx6Be/fu1Po2AKBly1YAgLS0VyVlISHCTf6IEZJdnQwMDNGxY2cEBwfUe4+Q+qzD2NgYbNu2AfPmLUCLFq3K3Lar6z707fshunTphqKiImRkpFfzaOtOTk4mbt3yhYWFfUmSBgBUVTXQt+84xMZG4OnT8utVQUFRKkkDAJqaOjA3t0daWhLS0pJKytu2tZZI0hTr2lUIvlFRYdU9HKqGnJxM3L3ri3bt7EuSNACgoqKBbt3GISEhApGRFbeBd5M0ANC4sQ5MTOyRnp6E9PTSNvDkSRCUlFTRqdMwieVtbYdCSUkFQUGe73lUdcMnOBhFRUWY8U432gl9+kBNWRleAQHlrm9tbCyRpCk29M2NYFgZT9TO3bwJ39u3sWLqVChU8MRKLHzOnRPqasIEifIJI0ZATVUVXmfO1Or6AODu7Q0AGD98uER5ekYGAEBPR0eiXKdpU8jLy0OtEl24qWZkZ2ciJMQXlpb2JUkaAFBT08CHH47Dy5cRePKk4nsdLS0dqKpWbhhtZqZwrdbW1pMoV1dvDBUVNaioiP/v7xMSIpwf7yR2J/TqJcSi4OBy17c2MpL4cVlsqJ0dACDspeQ94I7Tp9GscWPMGzQIAJCRnY3CwsL3OQTRyMrKRECAL2xs7EuSNIDQBgcPHoeoqAg8elRxG9TW1oGaWuXa4Acf2EkkaYr17SvcF0VEPK7k3tP74HlUeT5nzgh19ZFkEnvC6NHCNfnkyVpZv7CwEOnp6SgqKqpwH2NiY7Fh+3YsmDsXrVq0qHD5hiIrKxPXrvmiQwf7kiQNIMSoYcOEGPXwYcUxqmnTyseo+lBnd7dhYY+Qm5sLG5uOUp9ZWwtlFSUZqruNjIx0pKQkIyrqBXx8jmHvXhdoaWnB2rp0zojc3FwAgKqq9HATNTWh7O7df8rdv9pWn3W4atVP0Nc3xEcfzZD6rNjNmyHIyMiAhcUH+O23lejduxN697bDgAHdsHnz72UOl6pvkZGPkJeXi3btpOukuOzZs4pP9rIkJ8dCUVGpUnOOJCfHAhASPFR3Xr58hPz8XBgbS7eB4rKKEjXlSU2NhYKCEtTUSttAfn4ulJRUpLL98vLyUFJSRVLSC6SnJ1f7O+vK3YgIyMvJwaZNG4lyFSUlmBsa4k5ERLW2G5uSAgDQaSJ93qRnZeHnQ4cwsW9f2LRtW63t14e7Dx5AXl5eYsgRAKioqMDc1BR3Hjyo1fVfvHyJoJs3YdehA9oaGUl81tNeSDKuWLcOQTdvIi4hAaH372PJjz9CXU0NsyZPruxh0nt6/ly4JpmZScej9u2FssokaqrC2robFBQUcejQ77h50w9JSbF4/vwRtm1bjoyMNIwd+2mNfl9tuPv8uRCL3mnbKkpKMDcwwJ3nz6u13djUVACSsSgzJwchT57AxtgYHteuofeyZei0eDFsv/wSX+7ejcS0tOofiAg8eya0QQsL6TZYXBYWVrNtsCyJicJ9kbZ2szr5vv/veB5V3t3794Vr8gcfSJSrqKjAvH173Ll/v8bXj4uPh23v3rDr1w+2vXtjwddfI7yc+6yfVq+Gob4+ZvzLruFPnwoxytKy7BhVmWSy2NXZ0KfExHgAgJ5ec6nPisvi4+NqZRs//bQcFy6UPmm0suqAb7/9AY0blwYLExNTAMD164Fo3968pDwrKwt37ggJmri42HL3r7bVVx2eOXMSV6/64c8/XaGoWHaTef78GQDg0KG9UFJSwueffw1NTS2cOuWNPXt2IiEhDj//vKbc/asPqalCnWhrS9dJcVlKSvn1WpZ//vHD06eh6NlzpMwnRW8rLCzA8ePboaCgiO7dncpdlmpWWprQBjQ1pdtAcdmrV9VrA/fv+yEyMhSdO4+EklJpG2jRwhShoWcRFfVAohdPVNQDZGYKvf1SUmLQqFHTan1vXYlPTYV2o0ZQVpKe6LG5tjZuhYcjNz8fyuXEjncVFBZiu48PFBUU4CRj2NRvHh4oKirCkjHimOytsuITE6GtqQllGXPuNNfVxa07d5CblyezLmti/b99fFBUVCTVmwYAutnZ4YclS7DJxUViEmJjQ0O47doFE2PjSh4lva+UFCEeNW0qHY+Ky5KTqxePytKypTG+/PIP7J0y9DgAABe+SURBVNnzC1atKh3uoKWlg59+2gtzc7sa/b7aEP/qVdmxSEsLt54+rV4sOnkSivLycHpruEdkQgIKCgvxz7NnuPrgAeY6OsLcwAAhT55g38WLeBQdjb+//RZqZcyvJXZJSUIb1NGRboPFZYmJNdsGZcnKyoC7+25oaDRG9+4f1vr3Ec+jqohPSIC2lpbsa7KeHm6FhpZ/Ta/i+gb6+ujUoQPMTE0hLy+Pf+7dw0E3NwQEB+PQ7t0wayc5l+rJs2fhd/UqXHfvLvf3W0MklhhV2+rsr5aVlQUAUFKSbozFP2Czs7NqZRtz536GceMmISUlGdevB+Hx40d49SpVYpmhQ0fAxWU7duzYBDU1dXTt2h2pqSnYsWMzUt9kgSvav9pWH3WYlvYK69b9gtGjJ6BDh/Lf+pGZmVGyjru7D4yNhSfdjo5DMXfuNPj4HIOz81y0bSueN2gBQG6ucLyKitJ1UvzDOien6n/72NgI7NixFNrazTF58rcVLn/gwCo8eXIL48cvRsuWDaeXwL9BeW1AUVFFYpmqSEiIwIEDS6Gp2RyjRkm2gb59Z+DOnfPYu/cLjB69HC1bmiIm5jGOHl0FBQUlFBTkIS+vfmNOZWTl5pZ5E6Lypjw7N7dKN3WrDh/GrfBwLB4zBm3f6ap74/FjHLl8Getmz5Y5ZErMsrKzZd6QAYDKm/Ls7Owy6/N91i8oKMDRkyfRSEMDg8uY86eptjaszM3Ro0sXGBsaIuLFC+w+eBBzv/oKB7ZuRcvm0jdEVPOKrzey4lHxdbo68agiGhqNYWRkhg8/HA9jY3MkJ8fB23sP1qyZjx9/3AtjY/OKN1KPssqJMypvyqsci9zdcevZMyweMQJt32r/6dnZAIDk9HSsnDIF49/M3+TQsSMaqapiy8mTOBoYiI/emu+pISlug+XdK1bnvqgqCgoKsHr114iNjcKyZevRpIlWrX4fCXgeVV5WOdfrSl/Tq7D+rz/+KLHM4IED8WGfPpg2bx5W//479mzbVvLZq7Q0/LJ+PSaMGgXbt97u+G9R/FtV1j1RZX8TNwQ1nqgpKChASopkd30VFdWS8e15eblS6+TmCm/rUFUtfwx0dbdhampW8u/Bg53g4XEYCxfOgYvLAXTsKDwlatJEE9u378EPP3yDlSv/U7K8nZ09nJ1nw8VlOzQ0GpW7fzVFTHW4YcNaFBUVYdGiJRXut4qKMETM2rpDSZKm2LBhoxASEowbN4LqLVFTWFiAtDTJelVWVi15s05+vnSd5OUJdVLV8fkJCS+werUz5OSAr77ahSZNyu8V4eGxAefPH0D//hMxfPi8Kn0XVV5hYYHUcCIlpfLbQH6+0Aaq+gampKQX2LpVaAPz5u2S6hljYtIZM2b8Dk/PX/C//wlPsOXlFdCt2zikpycjNPQcVFXrJua8DzVlZSSV0T05Jy8PAMp8a5MsG44dwwFfX0zs0wfz3pkwLzc/Hz/s348eFhYye9qInZqqKpLeDOl6V045w29rYn3/oCDExsdj4qhRUJOxjNvx41ixbh2O/vUX2puUxuheXbtijLMz1m/fjnU//VTmvlHVFRTIviYVX29kxaPi63RNvxHu9u0rWLVqLpYt2wlb29IfRV27OuLzz4fAxWUFVq50rdHvrGlqyspIev1a5mc5b4ZeVykWeXvjgJ8fJvbqhXnvTOhZvB15OTmMtJecm2xUt27YcvIkgsPCRPsDs1hBQQFevZK+3yxug+XdK9bmvEWFhYVYv345AgIuYObML9G/P3sZ1xWeR5VXn9f0Yp1tbdHZ1hZBN24gOzu7ZPm1GzcKPY8XLqzwOMRMVoxSVlYt+a1aPHXJ2yr7m7ghqPFETVxcDJycJLsnDh8+GmPGTAQge2hOcZms4Thv09HRe+9tAMCwYSOwevUKeHgcLknUAEJCx9X1GCIjnyMhIR66unpo3doIGzasBQCp5ENtEUsdPnhwD8eP/41PPlmI1NTUkp5FycnChKiJiYmIjHyOFi1aQllZGXp6wpPvZs10ZXyvUJZWj+NNk5JisGSJZL326jUa/fsL9SpreFNxmaxhUWVJSIjCr7/OQE5OJr755i8YGpqVu7yn52Z4eW1H795j4Oy8otLfQ1WXkhKD//5Xsg106TIaPXoIbUDW8KbiMlnDosqSlBSFLVtmIDc3E/Pn/4VWrWS3gY4dh8DGxhEvX4YhJycDenpt0LhxM/z++zjIyytCR8dI5npioqelhScvX8rs3huXkiJ0oa7kk7fNx49ju48PxvTsiRXTpkl9fsjXF09jYvDNhAl4Hlf6t8p481QuKjERGdnZMNSVjkFioKejgycREcjNzZV6ChRX3AW6jKdr77u+h48PAOlJhIvt3L8fbY2MJJI0AGBmYoK2Rka4fvt2hcdHVZOUFIP58yXjUb9+o+HgIMQjWcObistkDYt6H8eO7YKKippEkgYAtLV1YWHRGbduXUZeXq7MHhZioaepiScxMbJjUfEQzcrGIh8fbD91CmO6d8cKGXM7tNASenc0UVeX+i49TU0AQFoDeA1uQkIMpk+XbIMODqMxbJjQBmUNHSgukzXkoCYUFhbijz++w/nzxzB16gJMnvxJrXwPycbzqPL0dHXx5Nkz2dfk+PiKr+nvuX4xg1atEHzjBl69fg1VVVXce/gQf3t5YeG8eUh99Qqpr4Th9EnJQsIjMSkJz1+8QMvmzcvspSsWCQkx+OgjyRg1aNBoODnVX4yqSzWeqGnWTBfbt++RKNPV1Sv5MR8aKn2zd+eOUGZpaVXutk1N27/3NgAgLy8PhYWFEm99elvr1kZo3br0B9K1a1fQqFEjdOzYqcJt1wSx1GFsbAyKioqwffsmbN++SWr5tWv/CwA4cMADlpbWsLKyBiB7Lp/iJFDTpvU3GZympi6WLpWsV21tPTRr1hJKSsp48kS6TorL2rSpuF0BxUma6cjMfI1vvtkDY2PLcpf39NyMY8e2oFev0fj4418qfI0cvZ8mTXTx6aeSbUBTUw/a2i2hqKiMiAjpNlBcZmhYuTYgJGmmIzv7NebP3wMDg/LbgLy8gsQcNWlpCYiKeoB27brU+FPz2mBlbAz/e/cQ+uwZOrdvX1Kek5eHhy9eSJSVZ/Px49ji7Y3RPXrglxkzZJ4L0cnJKCwqwpwNG2RuY/wvv0BdRQW3tm6t3sHUMisLC/gHByP0/n107lg6AV5OTg4ePn4sUVaT6yclJ+Oivz/MTU1hbWEhc5m4hAS01teX+Vl+QQEKCgoqOjyqIi0tXfzwg/Q1SUdHuCY9eiQdj8LChDITk8rFo8pKTo5DUVERioqKpM69goJ8FBYWVOoNI/XJysgI/g8eIPT5c3R+a66GnLw8PIyKkigrz2YfH2w5eRKju3XDL1OmyIxFOk2aoFXTpohJSUFWbq7EHBrFE6E3bSz9ZkexadpUF6tXS7bBZs30oKcntMEHD6TbYHGZqWnNtkGgNElz5ownPvroU0yf3rB7AzREPI8qz8rSEv6BgQi9dw+dbUunh8jJycHDsDB07lT+78b3Xb9YRGQkFBUUoPVmouaY2FgUFRVh044d2LRjh9Ty/33zKnCPfftgbVn+PWp9a9pUF7/9Jh2jmjcXYtT9+2XHKDOzmo9Rda3GEzUqKiro2rWHzM/69OkPX99zCAt7WDJhb2ZmBo4e9UDr1sawsiodQ/f69WskJsZDS0sb2trCkAF1dY0qbSMxMaGkJ8fbXF33A4DEW5/Kcvjwfjx5Eoa5cxfU2eu7xFKHVlbWWLt2o9Q+hIQEw83tIKZNmwVr6w4wMGgNANDXN0THjp3wzz+38ODBPVhYCLOYFxQU4OhRNygqKqJbt541VEtVp6ysAisr2fXasWN/hIScQ2TkQ7RuLdRJdnYG/Pw80Ly5Mdq2La3XzMzXSE2NR+PG2mjcuHQ4S2Ji9JskTRqWLv2zwuTOsWNbcOzYFvTsORKzZ6+CfAN5xXBDpqSkAjMz2W3ggw/6IzT0HKKjH0JfX2gDOTkZCAz0gK6uMYyMSttAVtZrpKXFQ0NDW2JIU3JyNLZunY6srDTMn/9npZM7xQoLC+HpuRJFRQVwcGgYTxGHdumCnSdPYu/58xJJGbfLl5GVm4vhbw1RioyPR15BAUxatpTYxhZvb2zx9sbI7t2xytm5zHNhbM+esJNxk3jw4kUEP3qEVc7O0NTQqKEjq3lDBw7Ezn37sNfNTSKp4ublhazsbAx3dCwpi4yKQl5+vsQkvlVZ/23HTp9GXn4+xjmVPXygnbExHoWH4/bdu+hoVdpub925g4gXL9C3h+zzhqpPWVkFNjay69XOrj+Cg88hIuJhydwwWVkZuHDBAy1bGsPUtDQeZWSUXpMqGmZbFgODdnj5MgIBAafQo0fpkMO4uBd48CAErVu3r3BC/Po21M4OO8+cwV5fX4kfk27+/kIsemcS07yCApi8MwfWlpMnseXkSYy0t8eqqVPLvS6PsLfHjtOnceTKFTh/WPrE1/XKFQBAXyvx/0hQVlZBp06y22C3bv1x9eo5hIc/hIlJaRs8fdoD+vrGMDeXbINJSfHQ1NSGpmb12mBRURH++ON7nDnjicmTP4Gz8xfV2g69H55HlTfU0RE79+zB3kOHJBItbkePCtfkt4Z6ybymV2H91+npUFdTg4KCgsQ+XPL3x81//kGfHj2goiLEaOsPPsDG1aul9jf4xg0cdHfHrKlT0cHKCq0NDN67DmqbsrIK7Oxkx6ju3fvD3186Rp044QEDA8kYlZ7+GsnJ7xej6kOdTgG9YMESBAcHYv78WZgyxRkaGho4etQdCQlx2Lhxp0S29eLFc/jpp2WYO3cBPvlkYbW2MWGCEzp2tIO5uSX09JojNTUFgYHXEBwcgHbt2ku9anrhwjnQ1zdE27YmkJOTQ2DgVVy8eB69evXDxx+L40dTXdahrm5zDBw4WGofMt90Q7S27iD1+dKl/8HHH0/Bp5/OxKRJ06ClpYWzZ0/i7t1QzJnzGVq2bFUb1fLeJkxYgvv3A7F27SwMGuQMNTUNXLrkjpSUOCxeLFmvN26cw65dyzBq1AKMGSPUa1ZWOn79dToSE6Ph4DANMTHPEBPzTOI7rKx6lrx2+/z5g/D03IxmzVrhgw96ICDAW2JZTU0dWFnVX1Lr/yMnpyV4/DgQ27fPQr9+zlBV1UBAgDtevYrDnDmSbSA09BxcXZdh0KAFGDJEaAPZ2enYunU6kpOj0bv3NMTHP0N8vGQbMDPricaNhTaQk5OB338fDxsbBzRtaoDs7Ne4edMHL17cw7BhX8LUtFvdHfx7MDMwwJT+/XHA1xcLtm5FX2trhMfEYL+vL+zbt5dI1DivX4/opCQ8cnEpKTvo64vNx4+jVdOm6GFhAe+gIInt6zRpgp5vXl1pbmgIc0NDqX24FBoKAOjfoYOon76ZmZhgytixOODhgQXLlqFv9+4Ij4jAfnd32NvaSiRanBctQnRsLB5du1at9d/m4e0NFWVljBg0qMx9W/Dxx1i4fDlmfvEFJo0aBWMDA0REReHw0aNQUlTEglmzaq4iqEJTpizB3buB+O9/Z8HJSbgmnT/vjuTkOCxfLhmPgoPPYevWZRg/fgEmTiy91ickRMPP7zgA4MWLJwCAkJCLSEoSer327TsSurpCL6oxY+bh9u0r2LRpKe7dC4axsQWSkmJx9qwr8vJy8NFHi+vq0KvNTF8fU/r0wQE/PyzYuRN9rawQHhuL/Rcvwt7UVOIHpvPGjYhOTsajtybfPOjnh80+PkIsMjeH9/XrEtvXadIEPd/qkTbHwQFnb93CGk9PPIuPh7m+Pm6Eh8P7+nV0MzPDUDvxvymrPLNmLcHt24FYtmwWxoxxhrq6Bk6dckdiYhxWrpRsg1evnsO6dcswdeoCiV4wcXHROH9eaIPPnwttMDDwIhIShDY4cOBING8utMFdu9bizJm/0batOQwN25asV6xVq9awtCz/pRb0/ngeVZ5Zu3aYMn48Dri5YcHXX6Nvz54If/YM+w8fhn2nThKJFudPP0V0TAwehYRUa/2gkBD8+scf6N+7Nwz19aGooIDQe/fgdeoUtLW0sHxJ6VyizXV1MXjgQKn9zXzzQpkOVlYyP29o5sxZglu3ArF06SyMHSv8nj1xQohRv/4qGaP8/c9h7dplmD59AZydS2NUbGw0zp0TYk1EhBCjAgJKY5SDw0i0aCG7t3FdqNNETevWRvjzT1ds3rwOe/b8D/n5eTA3t8SWLS5l9iB5n21MnjwdAQFX4eZ2CGlpr6CiogIjozZYsGAxJk+eJtVDxsamI86ePQVv76MAgDZt2uLbb3/A2LGTpDKY9aWu67CqzM0tsWePK7Zt24BDh/YiNzcHbdqY4KeffsWIEeJ9lW7z5kb4/ntXuLmtw4kTQp0YGVniq69cyuyF87b09FQkJEQBAM6d2y9zmWXL9pUkap4+vQMASEp6if/97xupZc3N7ZmoqWO6ukZYtMgVPj7rcP78/1BQkAcDA0vMm+dSZi+ct2VkpCIpSWgDV67sx5uHQRI++2xfSaJGQUEJrVqZ48YNb6SlJUBZWQ2GhtaYN88FFha9a/TYatvySZOg36wZjly+jEt37kC7USNMHTAAi0aOrLCn2J2ICADAy+RkfPPnn1Kf27dvX5Ko+TdY/vnn0G/RAke8vHDp2jVoa2pi6rhxWDRnTqV61VV1/Zt37uDp8+dwcnSE5ptu0bIM7NMHf27YgN2HDsHTxwevMzLQpHFj9OraFfOdnWFRySFsVDNatjTCypWuOHBgHY4eFa5Jbdta4vvvXcrshfOuuLgoHD4s2Ss2KOgsgoLOAgDMze1KEjXt2tlg5crD8PTcgcDAszh3zg1qahowNbXBqFFzYWXVMCbvXj5+vBCL/P1x6d49aGtoYGq/fljk5FRxLHr+HMCbWLRvn9Tn9qamEj8wG6mp4eCSJdjo7Y0LoaH4+9o1NNfSwieDBmH+0KFQaOC9ZPX1jfDHH67YvXsdjhwR2mC7dpZYtcqlzF4474qNjcLevZJt0N//LPz9hTZoZWVXkqgJC7sLAHj69CHWrl0qtS0Hh9FM1NQRnkeVt3zJEui3aoUjnp645O8PbS0tTJ04EYs++aRy1/RKrt/GyAhWFha4dOUKkpKTkZefjxZ6epg0diw+mTkTzfX0avMwRUlf3wibNrli1651cHUVYpSpqSXWrHEpsxfOu2Jjo7Bnj2SMunLlLK5cEWKUtbVdvSZq5IrKGXQsJyeH9HRxj0kWs0aNhEwe67D6iuswMJB1WF3dusmJfm6BisjJyeHkyYZ9DPVp6FDhPCq6fLme96Thknvz1omixMR63pOGS05H518Ri0JDG/Yx1Ccbmzex6Pz5et6ThkvuzZPwiAi2w+oyNm7490WAEI94LlVP8XlUVI8vOWno5N48/ImKavjnUn0xMCg/Fok7TUlERERERERE9P8IEzVERERERERERCLBRA0RERERERERkUgwUUNEREREREREJBJM1BARERERERERiQQTNUREREREREREIsFEDRERERERERGRSDBRQ0REREREREQkEkzUEBERERERERGJBBM1REREREREREQiwUQNEREREREREZFIMFFDRERERERERCQSTNQQEREREREREYkEEzVERERERERERCLBRA0RERERERERkUgwUUNEREREREREJBJM1BARERERERERiQQTNUREREREREREIsFEDRERERERERGRSDBRQ0REREREREQkEkzUEBERERERERGJBBM1REREREREREQiwUQNEREREREREZFIMFFDRERERERERCQSTNQQEREREREREYkEEzVERERERERERCLBRA0RERERERERkUgwUUNEREREREREJBJM1BARERERERERiQQTNUREREREREREIsFEDRERERERERGRSDBRQ0REREREREQkEkzUEBERERERERGJhFxRUVFRmR/KydXlvhBRLSnnNG8QGIuI/h0Yi4hIDBp6LAIYj4j+DcqLReUmaoiIiIiIiIiIqO5w6BMRERERERERkUgwUUNEREREREREJBJM1BARERERERERiQQTNUREREREREREIsFEDRERERERERGRSDBRQ0REREREREQkEv8HlH36KOdi6RIAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1440x720 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.set(style='whitegrid', font_scale=1.5)\n",
        "\n",
        "w = model_sparse.model[0].weight.cpu()[:, 0, :, :].detach().numpy()\n",
        "\n",
        "plt.figure(figsize=(20, 10))\n",
        "for i in range(8):\n",
        "    plt.subplot(2, 4, 1 + i)\n",
        "    sns.heatmap(w[i],\n",
        "                cmap=\"bwr\",\n",
        "                annot=w[i],\n",
        "                cbar=False,\n",
        "                vmin=-1,\n",
        "                vmax=1,\n",
        "                linewidths=1,\n",
        "                linecolor=(0, 0, 0))\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "id": "KQgag4ET6D6w"
      },
      "outputs": [],
      "source": [
        "for layer in model_sparse.model:\n",
        "    if isinstance(layer, torch.nn.Conv2d):\n",
        "        prune.l1_unstructured(layer, name='weight', amount=0.8)\n",
        "\n",
        "    if isinstance(layer, torch.nn.Linear):\n",
        "        prune.l1_unstructured(layer, name='weight', amount=0.8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jgq19n8S6LHO",
        "outputId": "9686a30a-4a0c-44ad-844e-efef91b6481f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Baseline test accuracy: 0.8516347933374461\n",
            "Sparse test accuracy: 0.13417643429981493\n"
          ]
        }
      ],
      "source": [
        "evaluate_model(model_baseline, 'Baseline')\n",
        "evaluate_model(model_sparse, 'Sparse')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vSdReBo6oQ6",
        "outputId": "dce3706e-27bd-4b85-e2fe-5de0bdaaef79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch   1   Train loss: 0.907841 Acc: 0.712809   Val loss: 0.699300 Acc: 0.791487\n",
            "Epoch   2   Train loss: 0.595167 Acc: 0.803116   Val loss: 0.640627 Acc: 0.809377\n",
            "Epoch   3   Train loss: 0.501591 Acc: 0.830493   Val loss: 0.610718 Acc: 0.818630\n",
            "Epoch   4   Train loss: 0.455596 Acc: 0.847922   Val loss: 0.590661 Acc: 0.828192\n",
            "Epoch   5   Train loss: 0.424528 Acc: 0.855325   Val loss: 0.584758 Acc: 0.829118\n"
          ]
        }
      ],
      "source": [
        "history_sparse = train_model(model_sparse, 5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "D20GrS4o8DuT",
        "outputId": "9bf9d5e3-ac8b-4b29-c385-a0994ec7866f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_history(history_sparse, 'Pruned model fine-tuning')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "HVrqlMgG8Fi_",
        "outputId": "439c2116-6a7c-4bcc-ec5c-22c0e5007b74"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "w = model_sparse.model[0].weight.cpu()[:, 0, :, :].detach().numpy()\n",
        "\n",
        "plt.figure(figsize=(20, 10))\n",
        "for i in range(8):\n",
        "    plt.subplot(2, 4, 1 + i)\n",
        "    sns.heatmap(w[i],\n",
        "                cmap=\"bwr\",\n",
        "                annot=w[i],\n",
        "                cbar=False,\n",
        "                vmin=-1,\n",
        "                vmax=1,\n",
        "                linewidths=1,\n",
        "                linecolor=(0, 0, 0))\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "id": "yXPozul18HF8"
      },
      "outputs": [],
      "source": [
        "for layer in model_sparse.model:\n",
        "    if isinstance(layer, torch.nn.Conv2d):\n",
        "        prune.remove(layer, 'weight')\n",
        "\n",
        "    if isinstance(layer, torch.nn.Linear):\n",
        "        prune.remove(layer, 'weight')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "id": "RbeoI7PM8K46"
      },
      "outputs": [],
      "source": [
        "torch.save(model_sparse, 'model_sparse.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffQhIODt8Ni2",
        "outputId": "0ecfe780-d355-40fe-cca4-0b47562689b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Baseline test accuracy: 0.8516347933374461\n",
            "Sparse test accuracy: 0.8291178285009253\n"
          ]
        }
      ],
      "source": [
        "evaluate_model(model_baseline, 'Baseline')\n",
        "evaluate_model(model_sparse, 'Sparse')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4_Ky4PH8PUt",
        "outputId": "4045f0f6-73a3-409a-ec10-3dfe2ce79f7a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pth model files size:\n",
            "\n",
            "Baseline: 665568 bytes\n",
            "Sparse:   665824 bytes\n"
          ]
        }
      ],
      "source": [
        "print('pth model files size:\\n')\n",
        "print('Baseline: {} bytes'.format(os.path.getsize('model_baseline.pt')))\n",
        "print('Sparse:   {} bytes'.format(os.path.getsize('model_sparse.pt')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "id": "z61HciE38Pls"
      },
      "outputs": [],
      "source": [
        "with zipfile.ZipFile('model_baseline_pt.zip', 'w', compression=zipfile.ZIP_DEFLATED) as f:\n",
        "    f.write('model_baseline.pt')\n",
        "    \n",
        "with zipfile.ZipFile('model_sparse_pt.zip', 'w', compression=zipfile.ZIP_DEFLATED) as f:\n",
        "    f.write('model_sparse.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rd7z2cCW8SNd",
        "outputId": "434f1f85-9ef6-467d-ad6e-cbd69c38ecb5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Zipped model files size:\n",
            "\n",
            "Baseline: 612331 bytes\n",
            "Sparse:   202212 bytes\n"
          ]
        }
      ],
      "source": [
        "print('Zipped model files size:\\n')\n",
        "print('Baseline: {} bytes'.format(os.path.getsize('model_baseline_pt.zip')))\n",
        "print('Sparse:   {} bytes'.format(os.path.getsize('model_sparse_pt.zip')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YVnawN-G8ToE",
        "outputId": "8231c368-361c-482c-d31f-4917e61396b9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3.028163511562123"
            ]
          },
          "execution_count": 169,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "os.path.getsize('model_baseline_pt.zip') / os.path.getsize('model_sparse_pt.zip')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 200,
      "metadata": {
        "id": "fGZTLIXR81qD"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "\n",
        "\n",
        "def time_inference(model,\n",
        "                   num_iterations=100,\n",
        "                   batch_sizes=[512, 1024, 2048, 4096, 8192]):\n",
        "    model.eval()\n",
        "    times = []\n",
        "\n",
        "    for bs in batch_sizes:\n",
        "        val_loader = torch.utils.data.DataLoader(val_set,\n",
        "                                                 batch_size=bs,\n",
        "                                                 shuffle=False)\n",
        "        tt = []\n",
        "\n",
        "        for _ in tqdm(range(num_iterations)):\n",
        "            t = 0\n",
        "\n",
        "            with torch.no_grad():\n",
        "                for data, target in val_loader:\n",
        "                    data, target = data.to(device), target.to(device)\n",
        "\n",
        "                    t0 = time.time()\n",
        "                    output = model(data)\n",
        "                    t1 = time.time()\n",
        "                    t += t1 - t0\n",
        "\n",
        "                    del output\n",
        "            tt.append(t)\n",
        "        times.append(tt)\n",
        "\n",
        "    return np.array(times)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "metadata": {
        "id": "57aR-Kg992mB"
      },
      "outputs": [],
      "source": [
        "device = 'cuda'"
      ]
    },
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      "source": [
        "times_baseline = time_inference(model_baseline.to(device))"
      ]
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      ],
      "source": [
        "times_sparse = time_inference(model_sparse.to(device))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 203,
      "metadata": {
        "id": "0cOK02Os_DYS"
      },
      "outputs": [],
      "source": [
        "batch_sizes = np.asarray([[bs]*100 for bs in [512, 1024, 2048, 4096, 8192]]).ravel()\n",
        "batch_sizes = np.append(batch_sizes, batch_sizes)\n",
        "models = ['baseline']*500 + ['sparse']*500\n",
        "times = np.concatenate([_ for _ in times_baseline] + [_ for _ in times_sparse])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 204,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 424
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      "outputs": [
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "      <td>sparse</td>\n",
              "      <td>0.001328</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13907a1f-6146-4aed-bd91-33cf6294e9f4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-13907a1f-6146-4aed-bd91-33cf6294e9f4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-13907a1f-6146-4aed-bd91-33cf6294e9f4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     batch size     model      time\n",
              "0           512  baseline  0.007754\n",
              "1           512  baseline  0.006637\n",
              "2           512  baseline  0.005881\n",
              "3           512  baseline  0.006922\n",
              "4           512  baseline  0.006193\n",
              "..          ...       ...       ...\n",
              "995        8192    sparse  0.001217\n",
              "996        8192    sparse  0.001268\n",
              "997        8192    sparse  0.001258\n",
              "998        8192    sparse  0.001248\n",
              "999        8192    sparse  0.001328\n",
              "\n",
              "[1000 rows x 3 columns]"
            ]
          },
          "execution_count": 204,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df_times = pd.DataFrame({'batch size': batch_sizes, 'model': models, 'time': times})\n",
        "df_times"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 205,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "id": "XwQ9yNUv_uZ0",
        "outputId": "dd8deb6c-fb06-4a63-f40a-8afd4e9a4a32"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1440x792 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(20,11))\n",
        "sns.violinplot(x='batch size', y='time', hue='model', data=df_times, split=True, \n",
        "               palette=[sns.color_palette('tab10')[0],sns.color_palette('tab10')[2]], scale='width');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 232,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_03iubiUBRNL",
        "outputId": "6603965d-de7b-4a00-8e04-8763a1d3fa45"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch   1   Train loss: 0.152530 Acc: 0.944166   Val loss: 0.672641 Acc: 0.857187\n",
            "Epoch   2   Train loss: 0.135979 Acc: 0.951955   Val loss: 0.655773 Acc: 0.848550\n",
            "Epoch   3   Train loss: 0.126993 Acc: 0.956659   Val loss: 0.685272 Acc: 0.857495\n",
            "Epoch   4   Train loss: 0.126565 Acc: 0.956428   Val loss: 0.700741 Acc: 0.850093\n",
            "Epoch   5   Train loss: 0.122326 Acc: 0.957662   Val loss: 0.703955 Acc: 0.852869\n",
            "Epoch   6   Train loss: 0.108282 Acc: 0.961286   Val loss: 0.690594 Acc: 0.856570\n",
            "Epoch   7   Train loss: 0.104256 Acc: 0.961132   Val loss: 0.699459 Acc: 0.857804\n",
            "Epoch   8   Train loss: 0.108075 Acc: 0.962058   Val loss: 0.714508 Acc: 0.852560\n",
            "Epoch   9   Train loss: 0.100155 Acc: 0.964371   Val loss: 0.715367 Acc: 0.853486\n",
            "Epoch  10   Train loss: 0.102064 Acc: 0.963214   Val loss: 0.731339 Acc: 0.854102\n",
            "Sparse@10% test accuracy: 0.8541024059222702\n",
            "Epoch   1   Train loss: 0.158688 Acc: 0.944783   Val loss: 0.611212 Acc: 0.863048\n",
            "Epoch   2   Train loss: 0.125579 Acc: 0.953497   Val loss: 0.644477 Acc: 0.854411\n",
            "Epoch   3   Train loss: 0.127582 Acc: 0.954577   Val loss: 0.656113 Acc: 0.851635\n",
            "Epoch   4   Train loss: 0.124161 Acc: 0.954808   Val loss: 0.650542 Acc: 0.851943\n",
            "Epoch   5   Train loss: 0.120356 Acc: 0.956968   Val loss: 0.674879 Acc: 0.851326\n",
            "Epoch   6   Train loss: 0.114843 Acc: 0.959513   Val loss: 0.681738 Acc: 0.854719\n",
            "Epoch   7   Train loss: 0.102519 Acc: 0.963214   Val loss: 0.665455 Acc: 0.857495\n",
            "Epoch   8   Train loss: 0.103581 Acc: 0.963214   Val loss: 0.672201 Acc: 0.849167\n",
            "Epoch   9   Train loss: 0.103210 Acc: 0.962983   Val loss: 0.699522 Acc: 0.855953\n",
            "Epoch  10   Train loss: 0.099418 Acc: 0.963060   Val loss: 0.695707 Acc: 0.854411\n",
            "Sparse@30% test accuracy: 0.8544108574953733\n",
            "Epoch   1   Train loss: 0.234990 Acc: 0.918408   Val loss: 0.619893 Acc: 0.849784\n",
            "Epoch   2   Train loss: 0.178308 Acc: 0.939307   Val loss: 0.606189 Acc: 0.853794\n",
            "Epoch   3   Train loss: 0.151943 Acc: 0.943472   Val loss: 0.623646 Acc: 0.855953\n",
            "Epoch   4   Train loss: 0.142287 Acc: 0.949950   Val loss: 0.625406 Acc: 0.853794\n",
            "Epoch   5   Train loss: 0.130667 Acc: 0.954346   Val loss: 0.625747 Acc: 0.857187\n",
            "Epoch   6   Train loss: 0.125988 Acc: 0.955580   Val loss: 0.639624 Acc: 0.852560\n",
            "Epoch   7   Train loss: 0.124359 Acc: 0.955271   Val loss: 0.645110 Acc: 0.853794\n",
            "Epoch   8   Train loss: 0.119230 Acc: 0.956813   Val loss: 0.646242 Acc: 0.858421\n",
            "Epoch   9   Train loss: 0.115471 Acc: 0.957816   Val loss: 0.656891 Acc: 0.859346\n",
            "Epoch  10   Train loss: 0.110860 Acc: 0.960207   Val loss: 0.666068 Acc: 0.852252\n",
            "Sparse@50% test accuracy: 0.8522516964836521\n",
            "Epoch   1   Train loss: 0.623606 Acc: 0.798488   Val loss: 0.649224 Acc: 0.821715\n",
            "Epoch   2   Train loss: 0.383056 Acc: 0.867587   Val loss: 0.606078 Acc: 0.835287\n",
            "Epoch   3   Train loss: 0.325847 Acc: 0.888332   Val loss: 0.595128 Acc: 0.836212\n",
            "Epoch   4   Train loss: 0.299398 Acc: 0.898820   Val loss: 0.589514 Acc: 0.835287\n",
            "Epoch   5   Train loss: 0.271347 Acc: 0.904604   Val loss: 0.577722 Acc: 0.842073\n",
            "Epoch   6   Train loss: 0.254756 Acc: 0.910851   Val loss: 0.584364 Acc: 0.845157\n",
            "Epoch   7   Train loss: 0.246681 Acc: 0.910542   Val loss: 0.583434 Acc: 0.844232\n",
            "Epoch   8   Train loss: 0.230305 Acc: 0.920645   Val loss: 0.590319 Acc: 0.845466\n",
            "Epoch   9   Train loss: 0.229719 Acc: 0.920259   Val loss: 0.596311 Acc: 0.844849\n",
            "Epoch  10   Train loss: 0.220358 Acc: 0.923035   Val loss: 0.586590 Acc: 0.850093\n",
            "Sparse@70% test accuracy: 0.8500925354719309\n",
            "Epoch   1   Train loss: 1.894786 Acc: 0.394232   Val loss: 1.123563 Acc: 0.633868\n",
            "Epoch   2   Train loss: 1.169493 Acc: 0.607002   Val loss: 0.950579 Acc: 0.687847\n",
            "Epoch   3   Train loss: 0.977526 Acc: 0.674790   Val loss: 0.833125 Acc: 0.730722\n",
            "Epoch   4   Train loss: 0.887687 Acc: 0.705483   Val loss: 0.793217 Acc: 0.740284\n",
            "Epoch   5   Train loss: 0.840146 Acc: 0.725843   Val loss: 0.752164 Acc: 0.757249\n",
            "Epoch   6   Train loss: 0.795438 Acc: 0.739338   Val loss: 0.748542 Acc: 0.756940\n",
            "Epoch   7   Train loss: 0.761489 Acc: 0.750289   Val loss: 0.704022 Acc: 0.770820\n",
            "Epoch   8   Train loss: 0.724675 Acc: 0.757307   Val loss: 0.700318 Acc: 0.782542\n",
            "Epoch   9   Train loss: 0.714084 Acc: 0.764710   Val loss: 0.696669 Acc: 0.776681\n",
            "Epoch  10   Train loss: 0.705521 Acc: 0.769338   Val loss: 0.676393 Acc: 0.786243\n",
            "Sparse@90% test accuracy: 0.7862430598396052\n"
          ]
        }
      ],
      "source": [
        "device = 'cuda'\n",
        "histories = []\n",
        "\n",
        "for i in range(1, 10, 2):\n",
        "    model_sparse = torch.load('model_baseline.pt').to(device)\n",
        "    for layer in model_sparse.model:\n",
        "        if isinstance(layer, torch.nn.Conv2d):\n",
        "            prune.l1_unstructured(layer, name='weight', amount=0.1 * i)\n",
        "\n",
        "        if isinstance(layer, torch.nn.Linear):\n",
        "            prune.l1_unstructured(layer, name='weight', amount=0.1 * i)\n",
        "    history_sparse = train_model(model_sparse, 10)\n",
        "    histories.append(history_sparse)\n",
        "    for layer in model_sparse.model:\n",
        "        if isinstance(layer, torch.nn.Conv2d):\n",
        "            prune.remove(layer, 'weight')\n",
        "\n",
        "        if isinstance(layer, torch.nn.Linear):\n",
        "            prune.remove(layer, 'weight')\n",
        "    evaluate_model(model_sparse, f'Sparse@{10*i}%')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 230,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "CbSKvO74EV8h",
        "outputId": "0bc3fa95-c394-48ac-90ae-601be3c8f083"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f05064cec50>"
            ]
          },
          "execution_count": 230,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(14, 7))\n",
        "plt.plot(history_baseline['val_accuracy'], label='baseline')\n",
        "for i, history in enumerate(histories):\n",
        "    plt.plot(history['val_accuracy'], label=f'Sparse@{(i*2+1)*10}%')\n",
        "\n",
        "plt.legend()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "11-Praca-domowa-optymalizacja.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
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        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.8"
    },
    "widgets": {
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            "layout": "IPY_MODEL_d974c08f80aa49c0984d01ee4f13c142",
            "placeholder": "​",
            "style": "IPY_MODEL_46558571462b42149744224d2c49345d",
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