Autoencoder_CIFAR10,_Denoising_MNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Autoencoder: CIFAR10, Denoising MNIST.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "zc-hS4L74FZ8",
        "zevaxdJSgT3m",
        "0Kgm9Yn8gjXW"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/abhigoogol/Autoencoders-using-Pytorch/blob/master/Autoencoder_CIFAR10,_Denoising_MNIST.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "hNk5SeaDcu7Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "b5668070-18ff-4961-e947-fb7b343b957c"
      },
      "cell_type": "code",
      "source": [
        "from os import path\n",
        "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
        "platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
        "\n",
        "accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'\n",
        "\n",
        "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.0-{platform}-linux_x86_64.whl torchvision\n",
        "import torch\n",
        "print(torch.__version__)\n",
        "print(\"GPU: \", torch.cuda.is_available())"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tcmalloc: large alloc 1073750016 bytes == 0x5b7de000 @  0x7f569ade82a4 0x594e17 0x626104 0x51190a 0x4f5277 0x510c78 0x5119bd 0x4f5277 0x4f3338 0x510fb0 0x5119bd 0x4f5277 0x4f3338 0x510fb0 0x5119bd 0x4f5277 0x4f3338 0x510fb0 0x5119bd 0x4f6070 0x510c78 0x5119bd 0x4f5277 0x4f3338 0x510fb0 0x5119bd 0x4f6070 0x4f3338 0x510fb0 0x5119bd 0x4f6070\n",
            "0.4.0\n",
            "GPU:  True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2sWBZGT3c8z2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.autograd import Variable\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6FaerIzWIySa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "outputId": "41bd5e33-ccbf-48c3-e11d-0a6c9aa86aa9"
      },
      "cell_type": "code",
      "source": [
        "# Courtesy : https://github.com/GunhoChoi\n",
        "!wget --header=\"Host: raw.githubusercontent.com\" --header=\"User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8\" --header=\"Accept-Language: en-US,en;q=0.9\" \"https://raw.githubusercontent.com/GunhoChoi/Kind-PyTorch-Tutorial/master/06_Autoencoder_Model_Save/model/autoencoder.pkl\" -O \"autoencoder.pkl\" -c"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-10-11 19:39:12--  https://raw.githubusercontent.com/GunhoChoi/Kind-PyTorch-Tutorial/master/06_Autoencoder_Model_Save/model/autoencoder.pkl\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 416 Range Not Satisfiable\n",
            "\n",
            "    The file is already fully retrieved; nothing to do.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zc-hS4L74FZ8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# MNIST Dataset"
      ]
    },
    {
      "metadata": {
        "id": "2tmUFujSdLjq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "batch_size = 8\n",
        "learning_rate = 0.01"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "x5TOS0IGeVGG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "324d05eb-6d73-4df2-f9dd-4e9981ca6e88"
      },
      "cell_type": "code",
      "source": [
        "traindataset = datasets.MNIST(\"./\", train=True, download=True, transform=transform)\n",
        "testdataset = datasets.MNIST(\"./\", train=False, download=True, transform=transform)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vxBgGA97fPv3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "traindataloader = DataLoader(traindataset, shuffle=True, batch_size=batch_size, num_workers=8)\n",
        "testdataloader = DataLoader(testdataset, shuffle=False, batch_size=batch_size, num_workers=8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zevaxdJSgT3m",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Encoder-Decoder (Big Network)"
      ]
    },
    {
      "metadata": {
        "id": "HYKU0OClgYdW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class Encoder(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Encoder, self).__init__()\n",
        "    self.layer1 = nn.Sequential(nn.Conv2d(1, 16, 3, padding=1),\n",
        "                               nn.ReLU(),\n",
        "                               nn.BatchNorm2d(16),\n",
        "                               nn.Conv2d(16, 32, 3, padding=1),\n",
        "                               nn.ReLU(),\n",
        "                               nn.BatchNorm2d(32),\n",
        "                               nn.Conv2d(32, 64, 3, padding=1),\n",
        "                               nn.ReLU(),\n",
        "                               nn.BatchNorm2d(64),\n",
        "                               nn.MaxPool2d(2, 2))\n",
        "    self.layer2 = nn.Sequential(nn.Conv2d(64, 128, 3, padding=1),\n",
        "                               nn.ReLU(),\n",
        "                               nn.BatchNorm2d(128),\n",
        "                               nn.MaxPool2d(2, 2),\n",
        "                               nn.Conv2d(128, 256, 3, padding=1),\n",
        "                               nn.ReLU())\n",
        "  def forward(self, x):\n",
        "    out = self.layer1(x)\n",
        "    out = self.layer2(out)\n",
        "    out = out.view(batch_size, -1)\n",
        "    return out\n",
        "\n",
        "encoder = Encoder().cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mrqsmTKYm_0G",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class Decoder(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Decoder, self).__init__()\n",
        "    self.layer1 = nn.Sequential(nn.ConvTranspose2d(256, 128, 3, 2, 1, 1),\n",
        "                                nn.ReLU(),\n",
        "                                nn.BatchNorm2d(128),\n",
        "                                nn.ConvTranspose2d(128, 64, 3, 1, 1),\n",
        "                                nn.ReLU(),\n",
        "                                nn.BatchNorm2d(64))\n",
        "    self.layer2 = nn.Sequential(nn.ConvTranspose2d(64, 16, 3, 1, 1),\n",
        "                                nn.ReLU(),\n",
        "                                nn.BatchNorm2d(16),\n",
        "                                nn.ConvTranspose2d(16, 1, 3, 2, 1, 1),\n",
        "                                nn.ReLU())\n",
        "  def forward(self, x):\n",
        "    out = x.view(batch_size, 256, 7, 7)\n",
        "    out = self.layer1(out)\n",
        "    out = self.layer2(out)\n",
        "    return out\n",
        "\n",
        "decoder = Decoder().cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LIc6gzZLrLdC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "parameters = list(encoder.parameters()) + list(decoder.parameters())\n",
        "len(parameters)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2gjazxcQrgzI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "loss_fn = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(parameters, lr=learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "K3DJiP6Su9V1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "traindataloader.dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kamFXrHwJP3f",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "try:\n",
        "\tencoder, decoder = torch.load('autoencoder.pkl')\n",
        "\tprint(\"\\n--------Model Loaded--------\\n\")\n",
        "except:\n",
        "  print(\"No file\")\n",
        "  pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wxhWild7rms4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in range(6):\n",
        "  for image, label in traindataloader:\n",
        "    image = Variable(image).cuda()\n",
        "    optimizer.zero_grad()\n",
        "    output = encoder(image)\n",
        "    output = decoder(output)\n",
        "    loss = loss_fn(output, image)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "  if i % 2 == 0:\n",
        "    torch.save([encoder,decoder],'autoencoder.pkl')\n",
        "    print(loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GlSTkp_Js4iE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "input_img = image[0].cpu()\n",
        "output_img = output[0].cpu()\n",
        "\n",
        "inp = input_img.data.numpy()\n",
        "out = output_img.data.numpy()\n",
        "\n",
        "plt.title(\"Input\", fontsize=20)\n",
        "plt.imshow(inp[0],cmap='gray')\n",
        "plt.show()\n",
        "\n",
        "plt.title(\"Output\", fontsize=20)\n",
        "plt.imshow(out[0],cmap=\"gray\")\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CJDbWUDUXG6M",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "0Kgm9Yn8gjXW"
      },
      "cell_type": "markdown",
      "source": [
        "## Encoder-Decoder (Small Network)"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "xwO1UJuygjXZ"
      },
      "cell_type": "code",
      "source": [
        "class Encoder(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Encoder, self).__init__()\n",
        "    self.layer1 = nn.Sequential(nn.Conv2d(1, 64, 3, padding=1),\n",
        "                               nn.ReLU(),\n",
        "                               nn.BatchNorm2d(64),\n",
        "                               nn.MaxPool2d(2, 2))\n",
        "    self.layer2 = nn.Sequential(nn.Conv2d(64, 128, 3, padding=1),\n",
        "                               nn.ReLU(),\n",
        "                               nn.BatchNorm2d(128),\n",
        "                               nn.MaxPool2d(2, 2),\n",
        "                               nn.Conv2d(128, 256, 3, padding=1),\n",
        "                               nn.ReLU())\n",
        "  def forward(self, x):\n",
        "    out = self.layer1(x)\n",
        "    out = self.layer2(out)\n",
        "    out = out.view(batch_size, -1)\n",
        "    return out\n",
        "\n",
        "encoder = Encoder().cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "HuTztLe-gjXd"
      },
      "cell_type": "code",
      "source": [
        "class Decoder(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Decoder, self).__init__()\n",
        "    \n",
        "#     class torch.nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1)\n",
        "        \n",
        "    self.layer1 = nn.Sequential(nn.ConvTranspose2d(256, 128, 3, 2, 1, 1),\n",
        "                                nn.ReLU(),\n",
        "                                nn.BatchNorm2d(128),\n",
        "                                nn.ConvTranspose2d(128, 64, 3, 1, 1),\n",
        "                                nn.ReLU(),\n",
        "                                nn.BatchNorm2d(64))\n",
        "    self.layer2 = nn.Sequential(nn.ConvTranspose2d(64, 1, 3, 2, 1, 1),\n",
        "                                nn.ReLU())\n",
        "  def forward(self, x):\n",
        "    out = x.view(batch_size, 256, 7, 7)\n",
        "    out = self.layer1(out)\n",
        "    out = self.layer2(out)\n",
        "    return out\n",
        "\n",
        "decoder = Decoder().cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "6f9aef03-3c9c-4b7c-ad40-af49164327a6",
        "id": "L_9hlypGgjXg"
      },
      "cell_type": "code",
      "source": [
        "parameters = list(encoder.parameters()) + list(decoder.parameters())\n",
        "len(parameters)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "drB9QwEQgjXj"
      },
      "cell_type": "code",
      "source": [
        "loss_fn = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(parameters, lr=learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "4b533ee8-8ef9-429a-ea1b-444ff3de095a",
        "id": "_ZF0dPY-gjXm"
      },
      "cell_type": "code",
      "source": [
        "traindataloader.dataset"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset MNIST\n",
              "    Number of datapoints: 60000\n",
              "    Split: train\n",
              "    Root Location: ./\n",
              "    Transforms (if any): Compose(\n",
              "                             ToTensor()\n",
              "                             Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
              "                         )\n",
              "    Target Transforms (if any): None"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "553181d8-828f-4049-e329-1c1ec98ae2ab",
        "id": "Lvvv95F2gjXr"
      },
      "cell_type": "code",
      "source": [
        "for i in range(2):\n",
        "  for image, label in traindataloader:\n",
        "    image = Variable(image).cuda()\n",
        "    optimizer.zero_grad()\n",
        "    output = encoder(image)\n",
        "#     print(output.shape)\n",
        "    output = decoder(output)\n",
        "    loss = loss_fn(output, image)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "  print(loss)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(0.8307, device='cuda:0')\n",
            "tensor(0.8126, device='cuda:0')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fy13DPJ51JjS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Test Model for Denoising Images (MNIST)"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "ms4VVkJVgjXx"
      },
      "cell_type": "code",
      "source": [
        "def show_images(images, cols = 1, titles = None):\n",
        "    \"\"\"Display a list of images in a single figure with matplotlib.\n",
        "    \n",
        "    Parameters\n",
        "    ---------\n",
        "    images: List of np.arrays compatible with plt.imshow.\n",
        "    \n",
        "    cols (Default = 1): Number of columns in figure (number of rows is \n",
        "                        set to np.ceil(n_images/float(cols))).\n",
        "    \n",
        "    titles: List of titles corresponding to each image. Must have\n",
        "            the same length as titles.\n",
        "    \"\"\"\n",
        "    assert((titles is None)or (len(images) == len(titles)))\n",
        "    n_images = len(images)\n",
        "    if titles is None: titles = ['Image (%d)' % i for i in range(1,n_images + 1)]\n",
        "    fig = plt.figure()\n",
        "    for n, (image, title) in enumerate(zip(images, titles)):\n",
        "        a = fig.add_subplot(cols, np.ceil(n_images/float(cols)), n + 1)\n",
        "        if image.ndim == 2:\n",
        "            plt.gray()\n",
        "        plt.imshow(image)\n",
        "        a.set_title(title)\n",
        "    fig.set_size_inches(np.array(fig.get_size_inches()) * n_images/2)\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "V6QZF3n0lUuI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 4958
        },
        "outputId": "3b1028da-6d5e-4f4f-ec61-5b5300e1d0bd"
      },
      "cell_type": "code",
      "source": [
        "i = 1\n",
        "number_images_show = 10\n",
        "for image, label in testdataloader:\n",
        "  image1 = image.cpu().detach().numpy()\n",
        "  \n",
        "  # Adding noise to the image batch\n",
        "  noise = image1 + 3 * image1.std() * np.random.random(image1.shape)\n",
        "  noise = torch.from_numpy(noise).float()\n",
        "  noise = Variable(noise).cuda()\n",
        "\n",
        "  image = Variable(image).cuda()\n",
        " \n",
        "  output = encoder(noise)\n",
        "  output = decoder(output)\n",
        "  \n",
        "  input_img = image[0].cpu().detach().numpy()\n",
        "  noise_img = noise[0].cpu().detach().numpy()\n",
        "  output_img = output[0].cpu().detach().numpy()\n",
        "  img = np.concatenate((input_img, noise_img, output_img), axis=0)\n",
        "  show_images(img, cols = 3, titles = [\"Input\", \"Noise\", \"Output\"])\n",
        "  \n",
        "  i+=1\n",
        "  if i > number_images_show:\n",
        "    break"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gaos8WtjEqkePHhWusdj61b/97W+5PqLvwx5Us8nzAcDJyYmTsboD/GE8W5Gr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\nMkhCKsggCakggySk4v8BRwDc/U88WjkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa055321668>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5wkVDNjvEmDpksxc1REOV4ZA9ePBgnD59Wjhks1Vq2dLHgHjIZkce0RDHxo8D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\nJCEV5JCEVJBDElJBDklIxf8BwrYq5Itz8vwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa0552b46a0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xcbFFvnLlClleV3XMVXCBeylPqEJItvif//kfR3eLxFUr+DplhAwODrZUj83JyYGfnx+a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\nDZJRCjZIRinYIBml+H9lFxtn4d4QzwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa05507c208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RJdD/29/+pmqLAuTZ8M9nnnlGmAMoNTVV1Wa9hACxYbFrZ9F/ODacFYBhg2kM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\nIAmpIIMkpOL/AVCv7vEf1F6hAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa055335d30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dEG5oaIC7uztXZeqTTz7hrvv973/P6YqLizkd6+0jCuQXPaMOGjRIJYty9pw6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\nQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa054e92fd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4cAAYNWqUqi2qlSNywBZVZmCfqej7KoJGSEIqyCAJqTBrPF23bh1SUlJQUlKC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\nGqSyU0vFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa05471dcf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u88Tk9a+88goZnCeuPSmDsdcgxVLKorHbwtCxz5w5c8wxuSkpKeT/rQxjBENx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\nKdggGaVgg2SU4v8Bibq/QwUBovcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa0550266d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D6UVDA+hqWKLni1hdDDAexI8dOxb79u3D2LFjpXZi0oHt27dLbagkpmJSA+oA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\nkIxSsEEySsEGySgFGySjFGyQjFL8P4Uv0lI/GfEJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa059e12d68>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YGIUuMzMTISEhRp+dPXs2cnJymqtrCnQVfNkQZZl/f4tsalxdXVFaWqqXi4uL4eLiYomP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\nA3RiznG3ft7QAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa0552c1128>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OnJwctG7d2mKdJmouZp1DBgYGomnTpujRowc2btyIzz77TFj+V8eyZcvU15s2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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa05475a978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "RWcIbCUf2se1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "j5nKsDdY4V1c"
      },
      "cell_type": "markdown",
      "source": [
        "# CIFAR10 Dataset"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "MrhYD_ya4V1d"
      },
      "cell_type": "code",
      "source": [
        "# transform = transforms.Compose([transforms.ToTensor(),\n",
        "#                                 transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))])\n",
        "\n",
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "batch_size = 8\n",
        "learning_rate = 0.01"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "id": "pbIwtIDf4V1g",
        "outputId": "d1ee6dff-bcd6-4f0f-eb2a-f4df111ced5e"
      },
      "cell_type": "code",
      "source": [
        "traindataset = datasets.CIFAR10(\"./\", train=True, download=True, transform=transform)\n",
        "testdataset = datasets.CIFAR10(\"./\", train=False, download=True, transform=transform)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "-nhh_S1b4V1i"
      },
      "cell_type": "code",
      "source": [
        "traindataloader = DataLoader(traindataset, shuffle=True, batch_size=batch_size, num_workers=8)\n",
        "testdataloader = DataLoader(testdataset, shuffle=False, batch_size=batch_size, num_workers=8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tocQWnSf6AwQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "outputId": "da29c1a1-f22f-4c28-8eef-e362c9508dc2"
      },
      "cell_type": "code",
      "source": [
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "dir(traindataloader)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['_DataLoader__initialized',\n",
              " '__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__iter__',\n",
              " '__le__',\n",
              " '__len__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " 'batch_sampler',\n",
              " 'batch_size',\n",
              " 'collate_fn',\n",
              " 'dataset',\n",
              " 'drop_last',\n",
              " 'num_workers',\n",
              " 'pin_memory',\n",
              " 'sampler',\n",
              " 'timeout',\n",
              " 'worker_init_fn']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "Zq0D-Gjf6L-G",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "trn = iter(traindataloader)\n",
        "images, labels = next(trn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FMJGtu186T3J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "outputId": "eede29e2-ad89-4d18-ea68-58ec46a92dd4"
      },
      "cell_type": "code",
      "source": [
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "\n",
        "# imshow(images[0])\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(batch_size)))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " bird truck   dog   car  frog  bird   car   dog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1gmJ76zMzeOe97wYAhIx8RUnM98eudfO6oUbjhKtx2O21jcpdYT/7ZsLaZaS+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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa08c2c9e48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "67IDBeKL4V18"
      },
      "cell_type": "markdown",
      "source": [
        "## Encoder-Decoder"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "M7feQSvy4V19"
      },
      "cell_type": "code",
      "source": [
        "class Encoder(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Encoder, self).__init__()\n",
        "    \n",
        "#     class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)\n",
        "    \n",
        "    self.layer1 = nn.Sequential(nn.Conv2d(3, 64, 3, padding=1),\n",
        "                               nn.ReLU(),\n",
        "                               nn.BatchNorm2d(64),\n",
        "                               nn.MaxPool2d(2, 2))\n",
        "    self.layer2 = nn.Sequential(nn.Conv2d(64, 128, 3, padding=1),\n",
        "                               nn.ReLU(),\n",
        "                               nn.BatchNorm2d(128),\n",
        "                               nn.MaxPool2d(2, 2),\n",
        "                               nn.Conv2d(128, 256, 3, padding=1),\n",
        "                               nn.ReLU())\n",
        "  def forward(self, x):\n",
        "#     print(x.shape)\n",
        "    out = self.layer1(x)\n",
        "#     print(out.shape)\n",
        "    out = self.layer2(out)\n",
        "#     print(out.shape)\n",
        "    out = out.view(batch_size, -1)\n",
        "#     print(out.shape)\n",
        "    return out\n",
        "\n",
        "encoder = Encoder().cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "h6tdlb5O4V1-"
      },
      "cell_type": "code",
      "source": [
        "class Decoder(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Decoder, self).__init__()\n",
        "    \n",
        "#     class torch.nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1)\n",
        "        \n",
        "    self.layer1 = nn.Sequential(nn.ConvTranspose2d(256, 128, 3, 2, 1, 1),\n",
        "                                nn.ReLU(),\n",
        "                                nn.BatchNorm2d(128),\n",
        "                                nn.ConvTranspose2d(128, 64, 3, 1, 1),\n",
        "                                nn.ReLU(),\n",
        "                                nn.BatchNorm2d(64))\n",
        "    self.layer2 = nn.Sequential(nn.ConvTranspose2d(64, 3, 3, 2, 1, 1),\n",
        "                                nn.ReLU())\n",
        "  def forward(self, x):\n",
        "    out = x.view(batch_size, 256, 8, 8)\n",
        "    out = self.layer1(out)\n",
        "    out = self.layer2(out)\n",
        "    return out\n",
        "\n",
        "decoder = Decoder().cuda()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "34344fdf-6b66-412d-bbd5-63cf3a93ecf4",
        "id": "dHPcJ9Q14V2A"
      },
      "cell_type": "code",
      "source": [
        "parameters = list(encoder.parameters()) + list(decoder.parameters())\n",
        "len(parameters)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "tgwV_3th4V2F"
      },
      "cell_type": "code",
      "source": [
        "loss_fn = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(parameters, lr=learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "8f10af5f-74a1-452f-ec64-8aaed2a150b1",
        "id": "_71g3OdF4V2I"
      },
      "cell_type": "code",
      "source": [
        "traindataloader.dataset"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset CIFAR10\n",
              "    Number of datapoints: 50000\n",
              "    Split: train\n",
              "    Root Location: ./\n",
              "    Transforms (if any): Compose(\n",
              "                             ToTensor()\n",
              "                             Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
              "                         )\n",
              "    Target Transforms (if any): None"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "93e4283a-2360-415b-b0c4-8ca410f1c296",
        "id": "_7I8176A4V2L"
      },
      "cell_type": "code",
      "source": [
        "for i in range(6):\n",
        "  for image, label in traindataloader:\n",
        "    image = Variable(image).cuda()\n",
        "    optimizer.zero_grad()\n",
        "    output = encoder(image)\n",
        "#     print(output.shape)\n",
        "    output = decoder(output)\n",
        "    loss = loss_fn(output, image)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "  print(loss)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(0.1569, device='cuda:0')\n",
            "tensor(0.1970, device='cuda:0')\n",
            "tensor(0.1240, device='cuda:0')\n",
            "tensor(0.1346, device='cuda:0')\n",
            "tensor(0.1202, device='cuda:0')\n",
            "tensor(0.1289, device='cuda:0')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "IPhzbP5X4V2Q"
      },
      "cell_type": "markdown",
      "source": [
        "### Test Model"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "Wo0V_8Zz4V2Q"
      },
      "cell_type": "code",
      "source": [
        "def show_images(images, cols = 1, titles = None):\n",
        "    \"\"\"Display a list of images in a single figure with matplotlib.\n",
        "    \n",
        "    Parameters\n",
        "    ---------\n",
        "    images: List of np.arrays compatible with plt.imshow.\n",
        "    \n",
        "    cols (Default = 1): Number of columns in figure (number of rows is \n",
        "                        set to np.ceil(n_images/float(cols))).\n",
        "    \n",
        "    titles: List of titles corresponding to each image. Must have\n",
        "            the same length as titles.\n",
        "    \"\"\"\n",
        "    assert((titles is None)or (len(images) == len(titles)))\n",
        "    n_images = len(images)\n",
        "    if titles is None: titles = ['Image (%d)' % i for i in range(1,n_images + 1)]\n",
        "    fig = plt.figure()\n",
        "    for n, (image, title) in enumerate(zip(images, titles)):\n",
        "        a = fig.add_subplot(cols, np.ceil(n_images/float(cols)), n + 1)\n",
        "        if image.ndim == 2:\n",
        "            plt.gray()\n",
        "        plt.imshow(image)\n",
        "        a.set_title(title)\n",
        "    fig.set_size_inches(np.array(fig.get_size_inches()) * n_images/2)\n",
        "    plt.show()\n",
        "    \n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SFxRgeTWGthx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "bdcdc457-639f-4cce-c1ba-38c706dbb4aa"
      },
      "cell_type": "code",
      "source": [
        "tst = iter(testdataloader)\n",
        "images, labels = next(tst)\n",
        "\n",
        "image = Variable(images).cuda()\n",
        "output = encoder(image)\n",
        "output = decoder(output)\n",
        "\n",
        "input_img = image.cpu().detach()\n",
        "output_img = output.cpu().detach()\n",
        "\n",
        "# inp = input_img.data.numpy()\n",
        "# out = output_img.data.numpy()\n",
        "\n",
        "imshow(torchvision.utils.make_grid(input_img))\n",
        "\n",
        "\n",
        "# input_img = image.cpu().detach()\n",
        "# output_img = output.cpu().detach()\n",
        "\n",
        "# def imshow(img):\n",
        "#     img = img / 2 + 0.5     # unnormalize\n",
        "#     npimg = img.numpy()\n",
        "#     plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    \n",
        "# imshow(torchvision.utils.make_grid(images))\n",
        "\n",
        "# plt.title(\"Input\", fontsize=20)\n",
        "# plt.imshow(inp[0],cmap='jet')\n",
        "# plt.show()\n",
        "\n",
        "# plt.title(\"Output\", fontsize=20)\n",
        "# plt.imshow(out[0],cmap=\"jet\")\n",
        "# plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hc4zSoL5fOMBGNvRbVTdk5sAYZcMpJFe5VultiNpVVrGWmAoHFxoO1yMzpmnZ\nQcp2lLjB+YGHLsfC0WkxV5klRCexsLwq46+Y/qu5GjN8fyd1WOgeFETIa1Jr1FC4Thd3kvNty5bD\nLQB4ZfnOgb2z2FqX9f6lV78hY5UlxfeqJRnv8dFZaELDnq0KZ2MQcU+xcrhMFSSZPMNu/xK8gKkf\ny4fDw1HcEglpERAw+PMyo6ewLcYDncPiuucEQ3nJoQ1taEMb2tD+XtsNj3zLDfF49x8UmHCQAPvm\nDwMAxhONrfMXAGwf+5WlZdzzwI8AAPYdlOOt5k9cwNPPShQ7Up3Gwop4mEZRxnfdwh3sEqbY2ljH\nKKnwxtHKco1xnkMcJSnWNlmOwZKeWrVSeEIxPbyzl69gYkS83CN7a7v69+nf+/dQ9MyNBFm1FuDw\nvEDjd98mZBnHwrakmtbQJvJT2975COFmzzewhymB8DE2YheHCTiEmj3HAVi2EdLT3mpvYotlJp2W\nRBhJf1CwGcbGmjhyWKIV15TK6G1Ya6c9+rUn2ES7kPwcsCzrwtLC9uEIdOlGGnVU6A36/J3r2HAM\nAcIJCnUah+L62vaxtEGCCbH6cq0J0CuPu/2CTBJSnq9eq+N77zoBAOhRsSwMQ1y6JLDp2bNnpa2p\nxsX1Ae548H34T0++BIdlWcYqlRJStjfJKE2nskJBTHkllKbEy26TwLTa6kKRWBfzwAdPOYi35Pem\nht33XLT5vAPXKaQbzThG/QimvqM1YF8joMxyqdreOdgG1TIKS7AK99lEQtC6IK/stNKozFVvc6uA\n1quBjHmpXsbBm0Wla+mSQP2Ly2uYoZTkW24/jjnWBms+k5TkutVTK1i9RLnP+QncfO+t0t6xMgaE\nfutUifOnRmG5HF8+z+Uzq5g7KrD0gEdNOpYuJAGTXGFtVaD+zXV5z0vWtc8NALRyYVs8dKDVwiTn\nk6lNh53AJfpkSpVSrVHlIR5llsckqUZHWHmIPKsoXSvV5V3M4hRtnmWetKS9U/U6bNbsuG4Al2Sv\noC4R1UBvouQY+UlGR5baLq8xRzdGMWx795JsEyK29Dah0MhhWlDFemNZKKJcE3JaShXKf5URGZOw\n5iJrkGC3RuJQlCMjuaqXJcgdQVyigPXciY1qSfpzYD/ld9M2HJK0WrFCTkjdMcQ2pMjMUaAsc9LY\nHfrWKtVCMrVer6Nek3bW+ExGGjU8+5SsPe4FUxOtt9c/S+Zpoz4Gh8/b9x3EJKx2t1izb0XFkaCm\njFenHaQ5U2K6AptzK2jI+6IyGxYJWQY6h2VD+UbiNUCh9pC9Mdh5GPkObWhDG9rQhnaD7YZHvjbL\nCRaW5cDvO+66GxV6YXbnKjKPUmB8AAAgAElEQVQqQjiMws5d7uD+EVGmQllyVbVKHwE9s5JXRkBS\nhCFK7ZmdwUuMdjxPvKJ2p4P5OYm2j94k0efGxiaq9E4XllagmKNtjoiX22pvFsIWpTK92E4fr17q\n8N4WmP4tbNAPEdPjd5nL6bSAMv+d3SwiA6GOYdFj9b1SkWLNTB5YKTRGJSovhDeMSH8O2J5XsCqM\nv5VD4wJLsK6uiMLXxvo6BgPj3TKiGsSFyMTeuSnsm5NxrXhmOugiyt5p32SetFyqQROZiFIenD0y\nBp9jHTMiXe2GsNn2Gj3oNEuKIyJtO4By5HO/J959nLSxsbFRtAMQMCCmelanN0BM9fi5CXlOYyPT\nRanSxqZEYWPNAN9zu4hxXCFxrTWwcfqK5K0ubyWYn7q2f45roVSTudjlwQmOYyFjpOmoHBaJRjnJ\nXspO4XDemPxtEvdRotCCwwjXdWy4/LssTYvCfEN4cUsKebat4AYAbm7BpZ5urFWhhhWYxFmWFrl7\nU25mAVDXeXaBibxyVRwYbkhlylIoMTd/6BaJgL/xlSdwiupwt739VkQUDXFb8p0xTR1mjOCWY0cA\nABNHpuGSONfr9zBBIQ6vwcNNEmC0JP05+02JsC9fWsb9NwlqkVssZdOAprBEkrWQUyTFlNfkandJ\nx9r6FlauyPy8/fgxBJyLpiSk7LsyXgCajGqgfHgWBRI4n1s5sM4jTu1yDaWKPIvRaZksbmcdfQrx\nd9ZkLrlhhgHzoKnlYItn+G2SYLfa6mFvU6JPg8RleQ7XkCNZdua57jUiO8YMauRoXeQxc0PWLP7P\nqOvx35yzGWykDlXkGAGvdEIEPDKz7zLSG62hvk/Qjf3z+zEzJ2ukPUpy6lcfQ0SBj+XLlwEAV04+\njaUpecZttw5nWRCBJssdM51BG/EgRsPZrkNegJuPHkO1asrMKkXO12apkOUAmyxp1IygA9dDl+vZ\nalvuNxhEqDWkX44XQBu5sL7R9M62yVGZOSIygxHjSJO0IAmmRJxst4ogkH9XSZBTuVMc0JBmecHj\nSfHGSo1u+ObrEuoKCTdGUQKXjOJypY6KUZ+iKHbVifCZ/+t3AAA/9MGPyDV6S/B8I9OWYv6gyJ6t\nbAg8FXZ7mJ6UBP0GlWCiOMbBwwJvHzosm3Dr2WfQ40Rp9/pIDWuOm2ez2UCmZaNtjJAdGKew+cJe\nWVjB1G3X9u8DP/pjiMjsrew4fabEjc0oO7bb7UJk3nUCOCTZaDL0BkkMTZKCqXtzHRdTo0A/zuC6\nCsra3qgBIdqEJByYWtmRZrMgjQS2tGdrvYUrVy8AAA7PH4ZtTq/hm2srdV22c5uOkc4tlElUK1HA\nfu/cISS8z+qSLKxr6+uYmqKiDCXpelvryAn9NEam4PtC/AhZMt1P2wgqMkeypMv2ZIXcqOvZSAIZ\no3vulM316P5ZhDzb8/xZac/Zl1/CfXfLoj43J4vKpecvIuHGNbiO0o7n2fACowYk9yu5AVLWEXba\nMTLiVUFDNv6pSg2msNZAvwoKtmHCmhpM59pXzQjXm803s3XBxjWMSw924WBFVlowWB3C4BmygiWq\nOFdsdf1zpqdIGjnfDwt4MYm2T8uxfOnX3qMHAACLFy5icY2Q+WwJ6ylJdDy7t5YJ/D5SquLwg+8B\nAIzOjqI1ECeoqzYQEbr3Frhp9nJ0SyQ2EY488pabEIzLZrhOdnU/sVHlwuvbKfi4i42pS2LaTvvP\nX/oKZkdl4W3Ualij82lIbPvmJlEv8x3j3M5zBxtt+buUHDZnfBZzs3fId1sRFkhES3nYba1cgU8p\nynaHhzeUagi5KWRJgo0VGYMXXxFnIExVcQKa2pFeSulMGLazrVXRx51PMKCz4UAVtbrm9dQ7nnWe\nA7nS1/7esdCms5WyRrhy9Dbc9K73AgDG9sh7aVUr8AnVpwDSTObLOsllB+/5Xrx9n6yfJ594EgDw\nqa8/jscuCMO8VmvgnQdlw9YMALL1q8VmayRjs+ssLDPT08XZ25ZtFQdtGKHATAO2Q8c+ohOoyqhT\nT6DLeZbrDBZh55WNVfjm/HFC/WmYwlPcQI3qV9aHy41Wa4Ueg5KUrrSbaQSaugVGfUzZhXubQyPj\nIRcpdus+fDsbws5DG9rQhja0od1gu+GRr2IpR59KL2F/ANfUTK5nAD10F0IOmmnaePWUqAotXDkj\nF+kv4OKVCwCAt0zfgz37BfudXRFoqHfmIkZ9nq3KkqSzZ89jZlYi5C0ewZVkeUGfz7WCItmhz8hX\nWVnh4VRYqoF8DJ4STyteW9rVvzzJt6Meflb1KsXRZQMqK/WTDBdY1uF5Jeybl9rN85clev+r//Ql\nJCRaBPTcyoGP//N/+yg+/Qd/jEa9XsBnb3mLhN8T4yM4tFf6aOo1bWUVEKeBQAeTo5idoTbvnplC\nwalPwlClVML1jqZ0mTKYmJxFwFKZtTWWmfQ6hasaMqpsTExjD8l0tYZEuPXxSaxvSIST5Q6MHr0h\nbvX7XcQJyU70JD3PQeDL+Ls6xiTPo50ggSRwLUyMyLOvsz5x/dIlXDwr4zs9KnOgtfwEXEL58XUI\nZY6VwabiVsB5urWygY2u1DeuLl7BSE1guFuPS1TtBqWixjbJzBmoupgDRtHHsqzt85i1Ls4LNrXe\noopm/pZh2I7aVCfXxTM1v3dtH+52eaT8zraL82p3WndTIsBepwdeBi0qpOksw+ScvEMWEZhb77sd\nJ0JRkbLtBIM1ieammOIpG1HgfhdL587w7/agTsKKnQWIWMbibZqDLcpYW5Bnf5jlcRF8hB3qVxMa\nb/fWERHGnW6Wi3pPk4qanZrY1b8XLq1jz34hNY40arBZKlY5JCmrer2KDmv1I74PWZ5jjSVTJWrB\nN5vTqPJYuv76BTi2tO3ZZ74JAFhfX8GBPTKfIiq1ObaDeoWaw+ub2ByYuluJkHOdYKkj87tJZKVk\nAWBEZV62LMuRs9+lHX3zTSrKUkXkbAhMFqxiEU9tDW1SUZw3fdtD5ZAgfeO3SZmcf+AgVljb+sIr\n8v6uLK9gQMJpp7uFDZ7Nu0li4d33fQ/e+q/eCQCovl3u8fR99+FP/vb/BQCstRcxWRM06N79kobo\nt7uwiF45MEcp7o58bdsuUiVKqwJBSwrdcmB6WiL0kzyEIw1tjI/LPJiZlEVkZKSGKtfpKBtgQDTM\nJTqglQvXI6pG1CdNdVHCaSsLGY9FzamCVis3kG9xvWdZpe9Wi/cty3IMuIh1B/JzbFcPr2/DyHdo\nQxva0IY2tBtsN/5IQSNkQc9sZnwMZUaFf/P8WYyk8vmRURP1ZfAc8T5XVy7IJaJN7KNHawc+ynWJ\nqsanxDta3+iixVyvkSudnJyEwwg75BFncZJiYIgvWVbkYMLCK7IwNk5xB8rVeCqEzxxgpneXPPzH\nv/wCchIoLJJyql4ZNUZrB45IGyfGqhibEU99dHwSAQkvW6ckh/LCqcsYmCJ8c2423a0nvv4sDu/b\nj/vuuRMAMFahMpDtFEXtMfuYZhH6psSIJ4OUygGaTfEQl5eWsWaOIWQua2p6EmXmS3baCFEE23YQ\nkcZvjincWN9Cm8QHI1Ji5zYuXhXxi3pbvMdGowmbeeIojKE4lr7LqVgpo6RNKZJhkuSolIiI6AR7\nx6TtZeYFe+0tpCRIGT7H/PxhnDotuaejR4VEhCzFwoKQiCRvOHpN/5RShR53zjx7p9PB6qogHFub\nV/HK818HAJx+7nEAwOHDx3Hg8M0yPuNkcFlq++g0c5IMUOSyAAWn0PjePrlq+5hDavE69nbubsdR\nlcZ0lheRRKFXptJifl/TNz7PmbkphHx2GRGKOIywuSREtckDImQzMjaKyoY8k+jyAvYwYkgsRgE8\npWp2to6EkWlyeQWrRnvYtlFjNFihCILjebBYQlQnZ2NtvYX4gkRcelTmRdnzYJtT3F0XEdeMAzdJ\nSdzBfXt39W9qfAw+S3yW11oFIlBtyr2jONlWpuJB6ZudFUSMPqfHhRfgOSW0roo4SLyxiCYJYjcd\nFhTguTjB2Izc3zyPKI7gVuXeg9U1tImcxeRIhHFakCXLzO/6jioQjIjPK0nzQvt9Z+TrEEOLLRRw\nmiLyYOttfsGWSuEanXeePNS4+QSS/dL2r6/KOG9deAI5hW5eJDH10rkzKFM4YmKkhsV1WRMiJWP1\nwDvfgV6PZVcVWQce+KEfw9dOitrchctncfKKELF8Pm/lN1DjXBtR3zryjdJ8+1QpjUKQJC+UuzQm\nSfyan5N1/9z5l4txmdzH+2U+NMd8pFbDKksWFREpR1mwHKPLb1CoMjIKmudIC7U/owluVRWyknzY\npnBL2SphwKMbu2GCTk/eiT5LrQ7s6uH17cYTrji5GpRga9ZKUFyo2rqCtU3p1HhNmlbxXGSsKbyw\ncAEAMDXSwP7DFJdPgK8/Lczpq4sCK9WqI3BZ73ryzCXe2SqO8zKTvdsboMla2lQrLC4L+cKIvzu2\nRrlMdRPDqE7WkfXkPlOTu+t8n3r2RZRYyxhFbX7Xwr3fezcA4OJVmaDri8CtPBrPKwXoc8N36Yjc\needtCMnq9bgxHTkoE+9ffeRfYna8iTpl8nKS1y4vrWJlkzKYa7KY9ro9bPEgAgObuJ5T1A5nqUbC\nRdgI3N+KW9Bo7O6b2VT7g7gQZjdEiCyz4JCBbo4u8/waxsdFGanK5x2UAjR4b8f1CrKYJhEqTRM0\nSBazLCMiH8MxLOOoiwbpnzolfJhFiMkKHnAsyrUGLvI4v5fOfoHPY4Ak5NmylHLc1UfOzyCQNt50\n7CYcvlmg/H5nCSefEXH/Z78hdYePfuUiTr30IgDg6M1C1Dly7GY0R8j0JVQq54Nu0zS2/03IOs8K\nAp6xPFPFmc35dfnn2FZJKyBpp1Bq22kBnS1vzUepLs/CkMAc28HmgjgYkzMCP2e2QtqW8U02+1jJ\nrp2fddYA1ysByqzLDPspoj4PA8myghjVpfNsO26RVvKosDTXGC1qac+8LBDoyNQkItbfdgcxbC5T\nJSqIxXq3stwDtx1DjbKuT3/zNI4flTTOFJXukiRDyOMBfRIhg2oN02z7KFMTSZKgvSBrRtZroTEm\nzvf4lDgl47NTqDVIuGqb99vD+jLPRrYtuGTrmsL3SrUMy5CmyGSv1gIMCFMaOdAsTeHyeTZ39E3x\nOSmdFw6c5oaSK10crDCINXLDrudBIxu1Bk4+/wIAYIuph9HxKaSjssZlHHvbs9DvkMhWGoFL1vDN\nt8oxgve++x0ICa86XWnjbXfehwff8wMAgEf+w+9Bc1197oysxzXHxYS9LbELAKXrkJLa/V4xj21Y\nBRxrsieua6HEmt/77hU1xFrgY42HLbzwtDjZJ587j71zPG87SJFTxcpUDzieB4vzqljPQ40sMgy8\nZJs0yXeoO+jB82SsWoTQB5mHKKFcaGdQOLPqOmcVfzsbws5DG9rQhja0od1gu/F1vox0piepjwwL\nOaHfmb3z+Aaj2y0lyXRt99AYF4+rQV1YN6jhACPfamMMv/vp3wcA9Hmd9mADfRJ4DJo5PeIi3BBI\nt+eb61Vw+mUhcy0vr6LNsqNmU75Ur1RhkwDhMnlv969ioiKfNQIFQw0ytnr5IkZHxavfu1e85uO3\nHYHLaO3kNwW2nAoCVOkNr6wtolIX72qsLhHFDz/0QKHP2mhQu5THd42NlnH+4qtobYnn3W6Jx9pp\n97HFOsINRnZpkhQ0fnMQumVbaNQV+9rECCN430T5pTK6hM522tiEPLM8yVGlvnBOmr9rBZiktq4i\nccYLSkWEHZBoYjtWEe0qWxWKXqYkp9/rFqU2BorWlka/JVHs1QuvYoOYYpOlBlNjzeIIOQO5aseH\nU5aoZpWHIczNTKDGSMjoe++0PM+3D/JmOZRlKdj03ptjc7j/nfJMDx8WFOKrX/5bnD8vUHbvWc6/\n9hZOkNwyNycRk2M7yFKj750jp5esdyhTKaPba6pRLKuA9XO9XXJm4M4sz4u6GV383Xa0vNN6JM6k\ncQKCBIXGcZZpOERR+tTsDhpVODzk/a3vfAeeZMT/2Dfk54mjQuLJEheddXlvGs0G9k4J0jHodbG+\nRbUxRpywNZbXJcIu1wQd2n/4GBTRiHlGgBc2VuDUZS71whjnXxVC1/mXRdB/9sD9u/o3P9rA4opE\nn4MoQw65vnmenuujz7fVEP6qo82CSOmyLth3PIwQ1l5ftuEymnYIPzuVKhIeKtKoGYTGQo/KaDN7\n9qA1YGmfQabitDjatDQi7/KePXuL1Fg/2Y4Gr1ejnZsQMN0m6JmPYpUjJkFsdOoYwlDGcGtSnsPT\n5y4WCMfoqPzd+HgdV5iCiom2VOt1WEx9Teyfx7vuEqTu3Q/9E/lsz37EEYlv7GsY9eExQj5xy+1Y\nOiMQ9PpA5kNvZBQnTogq4URf1qXNF76+q39ZFhbvv1JWoSoYEGXxfRcJjwVtNOU9f/Ddb8fp0y8B\nAFa/KmtD0slQ98d4zTYU0zimQigoleDVZE4zUwLbBgaM/uMIhVqdxfeyN+jBZg1ypOT9HnRXgZSa\n9XDQZBrDtXen6r6d3fDN14T79RFZyNPMgc8i8KPz+/CNp2Vw2q6wZHPVwdQeWfxeOiV5tre+47/H\n45Q67PXaSGIp7l5Zusy7WOhScN4hzDFibWBPSTar1qpsuKk9gqlJ2SizLC3qe0OeztFzfaQ5pQ5D\ngcQm3QFmmd+J0sGuzffqKyfRJmz6Q+/95wCAhx56N774NwJ9ThLanSxXUGJOM1A5plhjV+PPoBwg\n5QQwG5ipQ15avYpLK8uImV9zKGBRq41ikpuQYQwCgEsGsKn/tG0LNU7Cer0Gm5X7XS7Qy8trCCmn\nudPK3MySMC5EGZp12YzyVBcylyUyWbWyYdmmbtZM6m1JRG0B2jAaKS2YZn20KSNoJqdraXRbsrAu\nLixgiotIk7mnfpwjp6Zlym/pTGPPXtn4jvE84zuOH8Qr52SOzM/O7OqfsmxY5sxThxC9rbcl8pAX\n8ohHjt7GfltYXPwTAMDmmmzyr0YtLF+Vg0MOHRFRlZtvuQ2T3Jgcx0eaUD4xNfyBrICOr5H2LGqI\ntz8rAGjLLhZhI1UKtZ1L3GkxhVYq5SoSvhM5xQNK9SrKFXF2TQ1wnmW4SofnSLmGe04Iv+DpZ2SB\n7VPgoFRqIDBiCJYqDrjwfRf7Dxxg24z0oo05Vjks8u/OnHoJR28RaPPQqKRhNp5cLdi2CWysUx61\nMSLP++ChQ0D32v5VlcYM5/SyDtEn/B0aZnOWF+IiG2T12vUqxlivHlBfoLOxWdSU21YJMaU+/aaM\nmQ5DaL5bpmbVdV1MUpgnzy10eDhMnyeLLa1voUSHsVyZ4f0C1MmhsPjebGy2MF7bLc6vC0b8zvp7\nGfMk8LHRlDVs9MgR9JnzPLMubZg6fhsuXZC5mLENWsXosw+3nhDW/kMPPYQjBw8AEMdglMGRSdWt\nbbQAQrZGuOQPPvO7ePRPZe7fNjmPMJW/3eTOdvz4Cdz/nu8HADjL8rwfO/n8rv45NlDiGhV4PgLu\nlg6d7yxNsbkpc3FlRRzd4zcfxZ4D0sZ/UpEjru65/S7UqjxbW9WwsSnvuqZ8ZJJl0Ey9aeayc+jC\nk9HKLqpxjAxlOEgRm8NRDA8hA5pMsTmxBdB56vVeMym/g72uzfcTn/gEnn76aaRpip/+6Z/GiRMn\n8HM/93PIsgwTExP41V/9VXjeGzvRYWhDG9rQhja0f6z2HTffJ554Aq+++ioeeeQRbG5u4v3vfz/u\nu+8+fPjDH8b3f//349d+7dfw2c9+Fh/+8Idf1w0NzDMyLl5fqhyEZEAG1TqaZCde4lmu9999C8Ku\neCHlmhCiFq9ewZlXXpHvZzEMibRHqLU2NoNWi7KHJIYcO3oCTz0nsNUzp0S15v4Hf6BQ1zp35lVs\nEQYy3l446GL/lHjTpYr83ehoHdohcy/eTWwJ+z2cuF28yXe9+10AgLHmGN527wMAtklENddHnWNh\ne0FxzJ6RhcsRo0Vvr05kwJyFefDYrZjcexQbrNOsNYWekWS6qP8zUoZ5niMk5NWlZ6bzrJBPvLy4\nWET6CaOFLMtQruyGUHpEBmqlWiEAv8I66XZrCzlF9w+TXdwcHYftmqiRXnOWI+ZRif24h9AoysTS\nF5Ul0Dx/uEJvuNkcRcmTyMxRGk0iDw1KQcZRhD7vHUdkmqsUI0QRymTWXrl8sTgP9ZZjR7BKUpox\nS6kiLWIIZZ4CcjPBcl3AuzEjh71zB3CAEd5Ty1IPnKYaqytCcltlNHzq1POYZ83zoUNHMDUlJK4a\nyX1QLsKYsDTnlet5BcScY4d0oJFJk/+Sr++QG7SvA10a3nS5WkV9jDAaa2E9z8LaFWl7ZVwiuPbC\nIgKO/xMvncbbbhcY8v0/+qMylhcvsK0JgpqRawRqVUYreYIFnsvscW7naQKH6YqpvfI8W+s9rC0J\nqnSGEoIz0wdwZUmur6se9t/EGviTQqxZurKGeUaNxtxcY6QkczYoNTFK2VgTNbqej0ZT7n1xSfra\n6vVwjFUIL5GUtLa4gluIVlhuHd1NomqvSMSvHB9VSs32jFRklqHDOfvqwgrOXxDC1uKG9GeQZLDI\nNs/NWcsa8A0KyLXw0soavN5uxMmgHpayiuMiE75/m34VLxDm7r74CsojrKcfk6iw3evj4qI8B1NW\nHGxsobcpbf/o//pjAIAP/viPFzKXOtXod40CITUCtMirAsDn/uTPAACP/8Efo8RKiUHXweyUPKfZ\nvYKS3Pv2BzFJ+Nujap3f2F2jXatU4Jmj/CynOBzCYxRaqjXQIGLY5/GAY9MTuGlcUh+nvymEx+NH\nj+HlVyTKPzA/C4/KVYstYXTnSiPk2mPWpRyA2UBcr4TUnN5qyFNWXEjbuqwFR2ojIJqT9PvYIFF3\nqyP7z/zbdnXxuvYdCVd33303fv3Xfx2AnDgxGAzw5JNP4t3vfjcA4MEHH8Tjjz/++u42tKENbWhD\nG9rQvnPka9t2UW7z2c9+Fg888AC++tWvFjDz2NgYVldXv90lrrHcCPGPihfRG2Tos8zEtq1C5P+V\nk5KXbfVzVCtSDzsn5Wq4+MpFXF2QiOK+++5Bn1FcjQpWo7PzuLQhUe6AJAGvMor6hOQA31KTe6yu\nruPCRVGu6fVjbLXkOpM8ZrChF7C/Kgn8yTq9I9UrFJgqSuG1KrMHb7oDH/qJ/wEA0OfRZC+fWUbO\nOuGA+eBEK2xsGTerj4zEJaYckSMqDqy3l8UjXVhZwcH3PYAvP/Y88jBFhWSQc69K5HD+0qWC7DQ6\nLu2Oowgt5szWeRSazrIip6GsvNCgbjJ3HAQ+Bt3XZrN5VCOA9bUVnGVEkNErbI6MYGZG6lwNiSOJ\nQ+Ss32uTcDEY9JGxRMi2dFEGYKLcoFJCibmekM81R44KSQ+2UvBIijA5bNdziyMUDWFCIUfCWr0r\nRjO414JDFMHkdnaarXLYJqrk9aCSHaSmHSVC/CwIAtRYrrJTt9dErIqC/Z3NFTxLRbSTzz2FUZba\nTE/LnJyeOYAgIOluTKKFialpIaVBdGtTI+DOPHCW50VzFCN/neXQ+c7IWKzMZ5xmCiMsq7GIEoRx\niJWrLPNhdJ0mHZRmJJ+/4Wb42nPPAgB+kJrAmvnMrc1N+CVG0nGM2Wm5tu872CKB0Rx8orIEy2be\nEI0oVQIMelScIwry5WdfxYW+zP1q00VjTNo+dxPLfaamQP377f55ATJG95utDpQl89/ns4kzCykP\n/AjZ78tnruDEcSkP6zIPPF6vYZTR/5Vzl/HMc3J0aWNKPltfWcPUhKwza6z7vLS6hlZPUKGrV1cw\n6EvjDOEKlo0mIz/F0KreqACMhkeo1BRnp9CKd5dRmRPodQ4knJ85I/uZe+7DC8uCPnUW24i3+H1P\nxv7sK6cQU0FMc86PNUbgjhA9asjzWlxqYYOR22AQFhr0Bj2qVoJizk9PS/9vu/UO9Elemzx4BONH\nBTGo88AT2wI6LDcboa52zsh8p/l2gJJvSthqGGP+fJr11COj4yhXpO3jU9Le02dewPQemZ9jkzzq\n8NA+vHjqpWLIDFpp91miluc76uZZV6wAh2tPbgNgzjzmYR7KtZCQkEVqAwbtHlbXZS7FGyEGJINd\n7xjWb2dKv7Zy/1vYF7/4RfzWb/0WPv3pT+O9731vEe1evHgRP//zP48/+qM/+pbfXVlZweTk5Btq\n2NCGNrShDW1o/1DtdRGuHn30Ufzmb/4mfvu3fxu1Wg3lchlhGCIIAiwvL3/HjfWTn/wkHn74YTz8\n8MN46EHJHXUz8XS2OtsHSNu2xvnzFwAAX3vsKQDA/P552DxFvFEVD8XPB/jcX/0VAOBt978Vq6uC\nuR85Ipqix46fwBe/JiURJ09KLuett+4roqzMlfzBCydP4+VXJB+gLK84vP3W41LGtF+dw/E9LMjn\nKUtR2CmK4gdpipXxH72mr0lwEDfdKkzYdTIO4zjZLqRn7lPnVpGbU9DIyGQ2pSeS9mB+h6pfa+vL\n+NT//k/xz37h38HKgSa9X5ND3Vjf2D4lmlFqlAyQMlebxaag3kE5MOxCCz6FJXwyhr3AKtjM2aZh\nkAOnWjx+8eoCKvQqjx2T/O7o+CTK9PRDlnlFgz4Ssgv7FEYolwPUmZur+H7BcnSYa82yBClLObpd\n8cSDHX+nk7QogPcso61ro0vNbFPwvr66hjWypjsUD9jc2kKlXMEjv/87+D9+9deRUCzF2Dvvvx9j\n+yVfr1P5nafLyHeeHmUUq5gTHnQ7+IN//3sAgC99URjtlbIHl8/B5PUrQQCPqESmc/QpAGDEVQaD\nFCm1gscnpMzmxInjOHJYor2JiUnUGaX4JYlGNFw5ygYoclWwPPRWJO/1taceLfq2Wdrgve0if7Zw\nWngTcUWhvSo56jSUfu09MIUSSyhGxyZw+nFBiO7mUXNvufUI3vWBn8Zn/vXPwy8zz1ivIe5TrSrP\nMEIBm5zPdmFhCQNGw+rlUnQAACAASURBVKVmUIyp0XY24iCfe/IJ6DGJBsPeBhJHItW7HpASo9nx\nKfRe6mGnHT//PNZ5mtilxWUcvUvm5QGWfMEfBUJp2yunZU34/b/8Mn7iA+8HAByalrmd9doo85ld\nOnMBqwuC6j3wfRLxXz7zCk5z3M5Q7/rUygZiw2lo9TBBRKpEvslCawPjVOE7PCO/u/OuW9Ccvxnv\n+6mfw7/9xY8CAP7wP/xxUf71wz/5U0Xfmr/xb6RtaYY2Twm794M/AQAI7roX//b/+VMAwItPPl+o\nvtUn5H5nTr+ApCvvhl+W537w1ttRbkh//+l/+5PSrqPH0B2Y4w6BEZY+uiRJRGEXVb7zTT7vk888\nUxzX6TYbiEwZlMkdh32kRMb6LZl/X/i/fxtv/8AHsNPe+eD3YopKghPj46jz3o6zrYRHzSREmcyf\nz/7FHyKnxn7dAX7s/b+Ar37lM/jTP5F9QVkxZrh2X16X59XNIyBgJQYrAizkhXKg0jlyzfeyz3x9\nGCKJpV9+LN/tnu3BWpMXzk0cxIan48jPe/+bf1b07eGHH8a3su+4+XY6HXziE5/AZz7zGTRJ7Hnr\nW9+Kz3/+83jf+96HL3zhC3j729/+nS5T2LkzQprYd0Qk+QIrRk7quhMEhbJQjTV01XodN90kL9IX\nv/A5AEC/tYTymECcZ66sYG6vwNLzxyTR73sODu6Tz7YIi7x06lXkhACvbMqC1x5kCOkEtLf6mCQE\neHFdIIfRuSbWjVoNoYetNIV25KFGeYTX0pKe/eY38PwLslApisTZtltIWxpFKMCl6hHgeFbR7+2a\nXB8WiSq2ls/qHmU0KxNI7AxhZiTb5IpeuYyEkFefUF6cRlCmjtCcB5xpZCR29DoRylRhmmiQPl8O\nwL3umlKq0QkZ85HxyUKG0WG7O90uuuYlZw1tksSFapMRw/cDD7bZzPIUPcKXISH2rc0NrG+wXpOL\nwc03H4PLuScyjTzAgdBw1OvgCsvMDIkqjmP0SYhpbcki6dkOOl15eb/0N3+DB+59C64xlSLnBqBT\nn2Obgz4ZlO0VkK7NEqnnnnkaXXOGMAlglxdXUSdc5zmGbDRAvUoyl2vDcyiXyQMjbKuH9U3ZAC+c\nP8mxuIxnvsH54AWYm5OSqVnKks7MzmF2Sv5dqcrcUCULytpNliuRpNYJM5x/Wepme4SAK+UJJPTZ\nugMjEbof50gcam90sOeEkMU+9yXZ0DtRG+/6ADC5Zy8ingdZLgeFGltra6s417rEch7LLcEvsbzJ\nNlBghojzM+J8njt4GF06Ki1LY8QcpMB3cTlcRxXmPRJTtoUyJUj37duHgOmFlMQ4y8uQm4MvuPBe\nWVjFpz7zhwCAH37onQCA8WYNpRUZg9bVLaAj329fEJLWnvoYVinnevqcbDyq28fYJKVFKxWU+D66\nRgUujtGhxGs2SXUx10eVqYCZAj4dwcrSCl5rpqQpSjLsfac4AXf/5P8IAHjq0hXUJyRN4VZehdYm\n5cN1oNMBzGeR9OvVc2cwR50Ec9xemMYF4aoUVIqjVr/wub8AADz/wjOY4HP4r977gwCAQ8duhTMl\njmJnaxN9pg0ilinGsRyuAACPfeVvAQCXF6/s6t89d7+1IJ8praFY89tj6uGJJ78KTQesMS7PuBWu\nYrMlYzVFAtzL50/Bbsg63e9voJdyPTO13nCg+UzMmdCWVnCLQyokHShm1iiNnMGPHvBcX7eEyJZ1\ny4YN26Sl8jd2pOB33Hw/97nPYXNzEz/7sz9bfPYrv/Ir+MVf/EU88sgjmJ2dxY/8yI+8oZsObWhD\nG9rQhvaP2b7j5vvBD34QH/zgB3d9/ru/+7tv6obfPCPeyr5b7wEA5OhBGXJLrtEmRLi1JV752Ogd\n+IGHHgQA3HG7JPT/+E//DIqCBI3GCPbMSmK+ShjWTnsYnZauzcyLN9IqBXjmmxKRLnZZVO020JgR\nYsb44ca2TjG9n5d1BWeWxEPySHwZhCF6bG6a2/j+1yDuj375P6PfFi/Xc8VLK5VrMENta3MYtlXo\njDqeQuAT3qaqixeU4VSkbQG1RX3LBfCDeObrz0IFqoA+zaHo4SAsSEa5YUwoXcAqRU2W76JJAkOj\n4qBKeNF35TuuSqCy1zBagKLMIQj84vi3jOQfW2k4JEAZ3kEQeBhQjH1AFa5BRyJ9ALBcq9CofZlE\niYsXLiBlUbshRczOTGOUKl+Dfh8DCphvMVJc31zHgOiJEYno9/vF0ZFGrabsOFgkUe/l06d3Rb5J\nGhUlRCql2IYyx90DGik4DQrd4nAQ4dhRQXHuvEPUfJ5+/kU88ZQo+WxRVCJLY0zOSJRw//33w+Fz\nvnBRVNeeeOJx3HpcRCbq7Ovy0hKWKU6QJDGmKdIxP3+Afc3RI0nGpCtcp4Iw3u2BGyGbxdXLuHBa\nyIi33X0rANGz7jDtUSMcGQ5ijBE2vnT5AmaolTx/l7TxzAWJYNY6LRzaL78Luz2kJAdNTu/BwhXp\n2yZRDQ85UkYHmxvymV/2oY3aF7FzL1DoUeBj7/w+7D8uTMurmxKJd8N4V+QblEoFSWiw2Uaf5MmE\n+ugZWmjxgIxLl6TtjmWJeASAR/788wCARqOGaRJ+JmwXFlETcwRqfbKBVaJKObWmI52gT/RD2zZK\nXD9mqWY10agVZKeEQh+dzgATfG/LVH8bGatjY3EZr7V+StJdeQSl/VJe8/knJa221NpEk+31gwCK\n4790VcY+jHrwfLOmSCRYrjfhMtK0iGDFcYyU67BrA3/55/8RAPD7v/PvpF8qhWJa6uRzAtv/1Ef+\nZxw9JnNIwZa0F7BNoOu18OgXZVyfe+IxAMCos5tiJMJLRuhCFcI8fUbqjz75Bay3KN5Sp4511i7K\nIcMtQTefP/MUerm0AU6OpVUSzSLOq4pbHGSSKUN2teHkLDuKQwxCuWdMBbA0yoBQvuNRuKleb6AT\nU0u+PYDLS1nZG1NrvuEKV6+0ZJNZy4i9uyGs2OSJ7EIObpZMy7e/9U4E7N38fmHZ/eB//SF89s/+\nWq6z1MJii2okocBpHlJsUJnmzEWeuRsn0BOyeY9MyaaYQxenFeVBGTlVTxJO4FbmIqCiUUA1qp7q\nIyE0LDDDtQvd1EQdiwN5EbNMNof66Cgc3qe9JhOl0+4h4SaTp1GRuyvMcuGWJjlGAmGm6v9r71tj\nLKvKNp+1r+dep07dq6vvdEMjiAhMBAPiJSRiRmfIiDHpISYyagzEmBjhMyTO/BFF/GH0h8JAYsTk\n0zBf/Jh8Rv0IRlGbdriIdjfdRdPV9+qqOnXq3PbZZ9/Wmh/rXetUdxXXGYrGrCfBak+dOmfttdde\n67087/M6AP4Hntz3OxS8gu70o7rTgAuAWKTMI/Usz9G9hGtlGeLcXCpjZkrmDws5mccGAIvyHY7N\nUCXx/d7yYEizs1Iw/T3vuRx5kphTw7bAwImNu7AoDayg3UJEykqqU0mWpdhxyTYAwNj4qGTsAnDp\nMK8OVeDTZ6v0dT/q4/ARmcfsBl2d11XqUFwIBGS09ULVYSRATJubkqk8ubCkm0xk6zQfEEKsknuU\nP5jFQDYFOEs1uzhPFQA33vxRLQGpZPF2v+8/4AqS57OU3QOm5UF37NipJfq27ZL8gOktlyJPYUgl\nJyqEQKOxTPPGMU7ynqo22HYcWBQTz6hmN7EYOFt7bUqKtNtqolyQc62E4H1foDYsD7P5upy/II6w\nbacMaQ+NDeOVlyU34jLqkGNR2DwWMXrEIq4UXHRS6uiT9FEgY7jelOshXFlBhcZecJWhlmGYuml1\nKJ9XDHqo0oExNDGOpUg+T92UagvEWkEf23MBkqlM+n3ds7jbkPebVxLdCGF5SW7kV2ybwhAxjU+f\nkfvE0koLx8m4i4oljNEh1cvJDzx88jiOLkjHgJHyXNsG4kg1lACWqDZWdRGbqdV07WpCOaJjx05i\nlORYMwqvDpcLWMvBB2Ja57mxIfzxBelAPPE/fwYAuOqa9+GSq6hPr+9rfodKOzmODYu0DK64RjYl\n2HbJZcjT3qFSX2ma6pTX0uJZ/Pp/y1reHN2n2siENnCPHZXG278+/s/41G2fBSCNiWVymEB8hj//\n7t/xt/3y0PWF6ohUWnN92ar1yrMMAaWv5k6cpNds+L48L1StcbdZR2NZPhtpLA/cE0sv6WoHTwwh\nOCfvQ0R7w6YdFbiqh4KtDD6Axaq5SwbmKj4KSRmnEdIeVVLQIe7nHXiUU58PI900wnbe3OFrGisY\nGBgYGBhsMDbc8z3SlOf9v/5Rhi7et3UUkx71Z3UdTE1K635qVHp7O3fMAGQ1zZOa0qP//G947q8y\nTBn1Y12SqZioIouR+fLvMyJXOMgjJXM4tSjE6wCqAW4/tiAoXqpYdjbnEGRNq+Cjyy0tAh4na+u6\nRNLDUFFa5h1SlkqyLi7bI8MzYlp6P4tLdSwSG7fbzHQvSBU2FVkfRUd6CZddJckuZyl065eBMO4i\nJGKDYk37ro8iWa9V8ibGhquYmpZzeskmSQoZ9210yTJuNJZgUxi4UJTWXKmcxwjVoZ6dO6GvLSEP\nud9twlKhYcXOth1kRK56+WXJLuy0mpqAo/SpHdsGp/CilXJdw6hCnBYDekT6CennqVOndSibWYAg\nL6IXy/ltNpsIiHnq0velSaL7MwcUOkzDAJnumbvWOwzDEDaFSB3Soo5FjBRUX5ty/fdKqUgIIKV7\nxhShjWeY3iIbL4CUcBhnsGh9zp1sIIz5eX9THtquP3OlRf1HHQfFyjYaLkOjJT2PswsNGoOAT+pw\nSt2VlRz0V9Y2xVCeUMF3ccPHpPLaZXskgevU8is43aYw2svyO8JegA5FVMZKI1jmcq2+dFB6PTe9\nR3pbo6UKOuSBVGo1MKrhbvViXbROJeUoFssokIayCk36vgfOyFvzaYw9jh1TMsq17PSx0pLf7RJL\nPg3VPRwgzTK0KKpRKpTg0oR0yPN1vMFa3TYj01S7twrMn5WfnSOlqz2jE7BVy8okQZWYt4sU2jxw\negEnm6QUJ+Rn264L1yY9YstFm9Z3QGHYbj/CBEU6Cptk6qBeb2DusIzmCEphbKoN44hq3r0KGeR9\n6PMEJ0/N0ffIa2l3OlpzYXi4illqTKOaP/iFEgrD0ruvVOXeEwQ91Kg/7upKFUWinD34AlrETq6S\netnKSkOnmBSD/8ALz2E3pVwmZ3bocRyjKNXsoQPw6eaPUb11MXd+ugAAOlGAJWqKMXd8DifI4+2q\n+5kfRT6v9OLldTV4B3O0N6WejKzUm03kbDm28dIkxmry2o6ck2v2738/hZHN8veWIv55HipUX+/n\nK6DbiCwm5b0oBrrk2SakU+/GKORJT3+oiJV6c801vREYz9fAwMDAwGCDseGeb5cs9Sefl97R7CvH\n8PFrJO195/QQ5o5JZaubiAySc12d3P7Fr2Xt7/OHzqJHpSBwcrAoL6HKRCyWai82ozxkxC0kmVI1\nodIGuFqJyHEsrZhUKJA3gQzEQ0FGVrzsjiItIa9cBXB+acDy2dPIEmnJh2Rp906dRI0IF2OkIuVG\nPeTJKgxtASGU+z7wzHphneZCklzes0fWoH7jv34Gy80VRJTTBF23Y9nIk0U8SnnearGIjD7zXF1a\nlEfq82BkiVfGR5Cn1nEFygnXRkdQorzjauQppxGHXZ0DZ5Sjt2wLFnmdFVLxyrm2rg20aTyFXA4p\nlTS8fPgwWg1pYbeoE0wmMrieqt+V3+d7HhhdV68fYpHyoD3Ks9mWjWEiCqnWbb2wo7vYcO3tDloY\nqnKG1fjDH36HViq7rhQpp5lFPSTkkSZZjIzyeGrdJGmi15hNrdv6UYYsU7WDpIzm+KiRHnGpVEWS\nDVoFyvEwTQZRrQMZs2DR8+I4nm4xqcYuBDTJiBGBhBUsWH3pRdirnKjapIwsTO3ajfcReWp4VN7j\nSs2FR+k6h8qhlhdScC7vyckT86gWqO6T8s6LIeVfeQab8phZP0IaK4JTTuv1eqrZe5piirrlEC0A\n3aCDJn1Wn6JQYTPFUihJUWJ0AowIZD7lCy1/LaFsubGMFSornJnejCHq9HOC8s3N+Xls3S7z1WPb\n5PXXT76EM4clj2FrVUaFbO6ioErlbBvtjsxnc1LFGhkaRU/Itaw6h0VxAkFRsCBLkBJ/gdG+tBD0\nMEnPFqOOPUsL5yAiGb2bp5KuieER7N61c821dclHiroB+Jic6+1bZT4+Y0x79Pl8XkdmbMpVD9XG\nMDy5Tc6lmt8gwAx5/2qt9Xo9vf4WFhbgUAStSB5roVRCl0r32hQF6HQaOHpY6ipPbdmmCaAnjx8H\nAKRhD1XaZ3KaOLFWfe1ffvkvWDgnc+79qK8jQJoIFSUIAlXHL6MOnpXHlhEZETxWp8hAM49CSUZW\nyqN5OEy+Pr2Z1MnagKW0DOj4cD0HHnmxllsGh9xTczlSzysKLJ+jSAdFNHrdLhyLSHK1qiZpdjvn\n156/Hjb88B0hgkNjRd6o+ZUm/kwND7JkK0B9OMcm5eJgto+/PCtv8L89JVW1Il4AiL2pFg8AZHQY\nCS40+Uf3PhUMLm2OKikP29OhFtt2dG2xTZ9piwQZhQq5okJkHFOTciMqV4aA3vmH7+RUDaeJTZlF\nSqIwxRwJfreI/GABCIj5GaSJrkFU4VCLMcQkIv78H6V4w820+dxqAeFQWYdvFVu8H/fRIpayCmmf\nOLyAekgLl1qK5cdrGCZRAb9SgE1s5wLVpvqFIpi9dmkoMlyWcs02V2OIoo4OO+dpni3XRUgPbNSQ\nLOOTvS64koIUQpM8VKjfzdmgdY04VtKMIfrEQuz3e7ptQE6RWMIYCYkchES4CsNQP8RK9i21bAg6\n9Fw3v+b6cm4BiU3GApeD8P0KuDK8OIel2o/RfeI8XXUY0mEvEi2GrzY8xmxwxYpEDEeJthA5h1mW\nJnOldJglSaoNQsuy1xzOABBTyFKQQdi3Ad+Wxsn09Fb9vpCaZpzunkGcSMLR1u0yND4zMYpLp2Ut\nvU2Tn/caiIhgEnX6aLfkvX0v9fHNEWmr2e9hjO7d6aU6zlAIWrhF7JiUB1qZxFeYbSMkoReHjIpu\nt6ONsYmSDBMeCl7GgTmpB7BjaxkFKjpPiEx06sRJTFHfVgULFqbGSVLSihG05Th8ujetRhMLTB5y\n3mYZ+i1NTWPr+yXhbZxCs40zSzh3ShovJTePISLB8SLNfd5Fiea/RZUF9V6AHq1V9BOAUjJ5S63p\nHBIKyc5TWmNhuYWYUhJ/fkHuf1u2b8VW6v+8ur5+MVNNQzgCuicip4QdBo1TuICumXboQBkam8Lm\nbTK9MDos54dBIE/h3/l5+VwKLuCTgZxxrmVqbTIWhqpVpFzOS0r9mXudNo6Ts3TJ/Al0lcTmyZM0\n3r6ukOhROgLe2ufuwF9fgKXanVoWErqGfkBtRsMIrhICIoMm53qojMkzolySe9mO6hXIuXQGxAkC\nyH3PK5JTxT0wIu+qNquu5+lwvJsbQrsr101E+gP5oofRTXJtdE4QMThJ0O3Iz67WRjA0Ir+/s05T\njNeCCTsbGBgYGBhsMDbc81WepkuU7rTvYW5BWhFR8BJuer+0rPNVaZ22+hy/3/8sACCk0GySJvCJ\nwMM512QlBZs5KrqoeTW+7YApl4p+Mr+gyzscx0FC3kNHtQrjAhGpm6hG3pNToyhRXV7Y6awxX7bs\n3oI2hUiC0xTLA0OfwkEN+jyPOYjFoJH6hQQgtkpy++W/yZrRU50Y+Obd4I//EkIIZGSBdyl8fU6E\nOEoqM6fJ0uwVHJS3yJKGie3SE8pVK3oOYFsolVT9nww/W64PsU5Ytk2lBL1OE4tnSRlHNStPI11j\nrOZRcAHLVo3U5fXL8D6Fld1BM3hVlhEGMaJIzn+HCEYiBYoVIsFZFgQ1645I2D5NY7TIg1Seb8Yz\n7X1yMQh1qfrk9STQeRqhS5KTBVs15QYyqDKRVAuup1RSAysdqAqpGuvU0fWumYpOMAtcDKRDhaAy\niL6qT87074VS2UIGlYYQENrzVWPnQsCO1XdTffNwGVOb5X2cxsDzXT5Xp3FnOHRYElW2L0iFphuu\nvw6jVbkGto7O0DzbOEUh2817xrF4Ws7L0aMy9VMdluHjM/0mOuSmnTx5GkeILDM+MoFRSt+MkWcx\nXK3g1Lz8fYW84WqtiiCQ0YaltkxBNIIOWkQuBGMIaQ7PHZOlhHkusEZaDpYm4kWCARQCHSFltEKl\niNN1eT37/iyv/5oPXIfUluvquQNSVazEHKTkhQ2Pj6FAYUq7RfdEDIhzFbrf1XIBXIdvQ91qsFQk\n9TLbRkJlOlEg7/vkaBWbqO57+04pi3vo4AFMEdFxbNMufWVtVcJiW3D69IyVaF0wFz3yOEu1EUxv\nlxEM1dxg92V7cOlumbaaoUiEY8n6agDwlWwrZ5p8WsyXYVFppFr7U5s2YWxC3vNDL8rUTC/q4tyC\n9JxnD/4NXZL3XKKG93GaIlAbJGkawFu7r6RhDxFFRJI41hGrHIX/8wVL/7lF+0QS9tChfV+1ES3E\nQItkUle8BLkxItaSpoGfASF15FARTSEYbEoJOp4D1XO0T++LkxA+EfCUXChvcSSUWux2O/Co7LBI\nqYU3CuP5GhgYGBgYbDA23PNVOUJVFsTtnBYlX+hGeP6ItKRu7UkLpCM6OLMireAceWhpz0afPJ1C\nIQ9HtaDT+TMbFlNEF1KUshzdCN0lr7mbZIhTaaXm83mdH1bebtCPUSKSzDARTeI0xmFSCHJ5hmsG\nzgUAoDJcwxiJn8+T58sArZIUUV4wEdBEqGydsheh/hBAQt5cQLrFVqsFO+rjLP39X8lKO+pwBNR8\noriZLOjpTRghTWafyE8xBAR5g75jw3ZU3pt+Oo5WvlmNc8clSU7wQcmNyqc6vqvb3ykPzXN9FCjH\nrV7jnGslnW43QRwpoQzVMDwDp5yZR7rHE5umdZOF9soKUrKShcodg6GnSgPoNSHEoN0e/cO1mNZk\n7vcvbAYJnDp1AC/Py88uUo7OESky7Wu6uqyIU77e8y39byX6kSXQxC6Vs2VM6HwxLKYFOZSVH0Ux\neHZ+jtpizkAEhg/umQqKMAAJlaFkNTnPm668HEPKAF+1rHqUp6vkCpg9LtfRiTmZ++22A1x3gyQ9\n1qjl2+ToFhTzkttwcuU4+Iz80G6OSDeB1NJu+HV0KHcZjpXhOJIItNLtIlVLiAbcXmliZEKuxZDu\n50qrBYvyimeIp/Dcy3MYvVrmKT1m4fSs5FCUFBFSrCVcpWkGQer7CysdpTWD7UPyeiwuUCYVuRVK\nzx4/fBzD9KyeDqicjAE5io5YIoVFzS6GSaO7kXVRIWWlmivnJ+MMfSr76/suWE1yJ5S+d8YzBKRT\nrqIarm2jTCWJRUe11fTAw7WtPEGRNjABl9psVvJyjB0biNsyKtFoLEuVDwAhKXIdeeklnDsp75XS\nknYdFy7xPCwVHUoz/e9WfRGc8q4e6b7Pzs7CJs7HIimFRUmEDims/eWPf0BEpX9RKOfCsS1NohO0\n3yud5dVo1OuyXyIkubJAz57vqecgQUplhxFpRYftttaNVgTNvJWB5+RzUO820W/JucgRGdFPOFId\nBCPyFO/hbF966vlaF5FQ+tSkdJdkummJS3ue6CdQKi5hp6uqJd+057vhh6+mdypZQtsFJ0ZoZrmY\nW5Sb4qO/kE0UPnLztZijziKBYojCgqtUkDwPBdrgvLx8+MJOMAh90my7OUcfMup3tm1rhnTY6+p/\nq0OoOlzDCEn6LVHNXrN+Ds0TRDLYsX3N5eVzRU1ccCnEkiWDPpKpVnPhg81xnaaOHNAbeJfGdZgO\nmL+KCIf7CzhIhsNyRS64kc3bMbVd1kdWp0hyrliCRZtjoubc8WBT2N/xPL3Z6wOVMc2sXQ2by42B\nZ3xAmlJ/azmwSDpThfyjLEKaEFtUnP8dgAz1K5k7mzY8RwhkqQo7Eekp76OxLA2MoNOBSw+wrreO\nIt3jVhGhGGMD1rCSxXRsdEn6sxe01lyfJXJaAYdRL2axSugdNoclBmtHjsFR+52+fsFsPQliwLIa\nsDhtGyl9ZkKfx20XQjWcUI+IxfRmyjAYh6DNOnUtVKiGe+ZKma5xmI/mrKyhx8yEvrY89VNFGsMi\nucKFc5Jc8uQvn0ZlSF7PLmqgUHAqmClTMwyL4wiXByCTjwO8iA6RCkOSI8LU6DjGU/mGoNFGhwiQ\nJUHqY3EfDm36RVKwWhECc6cluerwcflcoZDHxCZJPHrx98/g5mulWth1N14PAHj6qd8Cip9IyOXz\niGlDXOn0UKXDKSIyUrvV1JKgwySzyBKBV6huecinsPv4JHqBXCOCx+DU1MRT7NZCETEZ+y4ZRkGr\nC0Ujcso5TSJUfdCTNEWcH5CZANmfWY3n2CFJKJ2ojWIbhYZVwgqANk5ybh4BGY/zJ2QIPvR9nDkp\nr+Hc4gICCtcL2hMFoA9N/UQzWxMqNYNeCL3wLKS6McNOUhVkzMHSkhzVpil5j186vABO9cStleVB\nzT9TXcAElB4rp850QucDB7BdBo/0GHwLsCilEzXlfhNHXS1PG7flfsLiFA7tKQVfphYsy4JL970k\nfBTJ2MqoUQaCEA4dutyjtBBjqDO5r7ojDeSILa06vbEsh5iIX2Fb/vT7Ajna9zhLESRkBJRM2NnA\nwMDAwOCixsaXGhEBQoX9gjCGZ0trI005LPLIfv8XmdSfO3sWzUBa1o0uUc9joEhlNynn8P2BFwcA\nuXymrT2HtJkzWLpfqOoHK0SGjMIrcRJr+r3S4K2NTiGmcElE4ZfQ98DJslXt8FYjyVIEVLdYpp6l\n/SDSFm9GlmYm1P8ALFvzMRCCQZB1GljSzH+aNLB/0VvAcsGCMyG9g6kZ6aFsHxvDyJAcu0XzE0Cg\nz1Qts5yTXM5HjnqOOl4OOdJ59S9oa3gh+KoaV1WXLLhSAxIDz5nezywLmfIQ6T74vj+wxG174PyT\nh5glfWQUeospS9NIWAAADj1JREFUjBiGgdZu5mkKRmGmPhEuhBAqi6E/jzGm/610dUUcYWVZhlrD\ndTzfNI2QETEmJhJbmsYAlR1ZjvRYAMAiLyHmmW5VqcLGnFvwaB0rp5mLVEcJmAVw1fxAhZqF0GRE\nXYtsc0CFt5mFhNZtQuHK2qU7sWmbXAN9asDwyuFnkSdLHDODa3Op3CJNAZdC1FuJNHX60Dk8/e9S\nM7hAmt6FYg7FvPyb8aEdcAtyXZ2oS4+r3ZPjqlQnsNKSkalOvIT+opzXQi+HhMvoSzNHJEO/jJiU\nvVa6MpJ0pttCg8INWVne76mRPJaor7cTc2y5hEoAHempV0tDwAWiQosL8/CL1MigksfkqBxvTIRA\nl9kYpn62Sqzbr5S1brdPnm2OCe2SCMbRJ+12h17M530weg76FDpPgnBQ2563tGpZTnlPnouQSEFc\npZJ4ihDyPo1U5bhHh2so0R622vN1qSWj5/jgNE5Vonau3UFAPbM938cYeaUBPUMpz9YJ9Qroujf6\nydNUR7NSEetSzQMHXgQAXLr7CkxPys8+QZE/WfZHe5g1SO/oHzbTtc4eRV7YOvrHBduBIPJav9dD\nSKHsuKfqa2P9nKgYsCUGQVRL6TtkAowiA0XPAyNyWkoetJMKrXrHrdUlR/Ra1AHP032ylf45h1Al\nrB2anwRIaE8WzEGfwu39QUz7DWHDD98+HVgqJxNliZZmS+2BdKCVlwvu+NklWLSI00ROcpoOatuC\nINDhRXUIFz0XeQpBW7SJ+jlf9xVV9aNLjQY4xa8c18JwRR5IkzVpIExO1tAkdqIqLO+2mqiSFGJ9\nqQ5g9LzrS7IINoU0hsfk5yUlDykxdOkHEs4h6PC1+GDhqtyoYAxQoVgStEgoZBftGMPO6gSGKbdU\nqsjbWCrY8Ck/1KdFGiODoMPUdlXuaCA24XqeDscraUbbtgcNBlahr2o0HUeHj5SwhOW4uhuJuh+2\nZQ9yx/STWZbuiZumqRYlT2i8dj9EQuG4jDaNYjSiNwaLMURU74lVzRG4OH+8aZpqoQAVIm4sLCIh\nJjXDOg+KLUNgAGDRPXRdC1DdSoQFm/gJmkzPMjChmlhQfrBS0/kzJRe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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa0552c1780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "CtRp6qFfyP4t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "3acf972e-ddff-4737-b487-16a45ce5971e"
      },
      "cell_type": "code",
      "source": [
        "imshow(torchvision.utils.make_grid(output_img))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/XPkNqEhXFCVKNDlefvFllBakGqhM21q1Ci8jrwlKStrrUidOJs2iYPvIjks/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\nz8M3vvENjI2NJfu61dvhw4dx4MABFIt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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa0551e4be0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "cfe7bdc4-e4d5-4264-89b1-9e47481704de",
        "id": "wwFZnfUw4V2R"
      },
      "cell_type": "code",
      "source": [
        "i = 1\n",
        "number_images_show = 1\n",
        "for image, label in testdataloader:\n",
        "  image = Variable(image).cuda()\n",
        "  output = encoder(image)\n",
        "  output = decoder(output)\n",
        "  \n",
        "#   print(image.shape)\n",
        "  image = image / 2 + 0.5     # unnormalize\n",
        "  output = output / 2 + 0.5     # unnormalize\n",
        "  \n",
        "  input_img = image.cpu().detach().numpy()\n",
        "  output_img = output.cpu().detach().numpy()\n",
        "  \n",
        "\n",
        "#   plt.imshow(np.transpose(output_img[0], (1, 2, 0)))\n",
        "  \n",
        "  \n",
        "  f, axarr = plt.subplots(1, 2)\n",
        "  axarr[0].imshow(np.transpose(input_img[0], (1, 2, 0)))\n",
        "  axarr[1].imshow(np.transpose(output_img[0], (1, 2, 0)))\n",
        "  plt.show()\n",
        "  \n",
        "#   imshow(torchvision.utils.make_grid(input_img))\n",
        "  \n",
        "#   imshow(torchvision.utils.make_grid(output_img))\n",
        "#   input_img = image[0].cpu().detach().numpy()\n",
        "#   output_img = output[0].cpu().detach().numpy()\n",
        "#   img = np.concatenate((input_img, output_img), axis=0)\n",
        "#   show_images(img, cols = 8, titles = None)\n",
        "  \n",
        "  i+=1\n",
        "  if i > number_images_show:\n",
        "    break"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([8, 3, 32, 32])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JKL8vQ0G5S9H0UrlbGgBkZsnHVVAg33umK7Ufi+lNbiMWGdLAcuOS23CLyzkW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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa054d0a3c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {},
        "id": "MEO7_XGF4V2V"
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}