BotTraining.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vitorfrois/GooGooBot/blob/main/BotTraining.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cl96sdncecqQ"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms \n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import time \n",
        "import copy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZmBfqbiypu-o",
        "outputId": "30391626-c544-4501-f70c-2b96352d4117"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
        "\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "13ea8d36c0cd46e9a046e857e21d0151",
            "c7efc417685b41f8a9fe1a3569a40d85",
            "b16aa9fdeabc4d1ea9d493e5262fb049",
            "e987121c1fe7456190eda7c23c78cf48",
            "f5f7a5993aae4f0784fb0fc201512b43",
            "e6eca398c8114147ac9aa9773a06aded",
            "000ae5bb44c5470989455cd429f86a9f",
            "12bfc6c858cc4a98933fe27fa9899f24",
            "beda929e8d894b9cb561804fa9cddfe2",
            "9461be3edd444190a344039a482c4624",
            "84274074c3de40be8fdf8304e9094ddc"
          ]
        },
        "id": "TTEicoUlkEjK",
        "outputId": "3ec897b9-16f8-4ca0-8fa5-b4c4105f5c8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13ea8d36c0cd46e9a046e857e21d0151"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "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 = 16\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=transform)                       \n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
        "                                          shuffle=True, num_workers=2)\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
        "                                         shuffle=False, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "id": "XCH1Vjz_kJAx",
        "outputId": "1da33114-2908-49dd-89e2-1fa695541cdc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bird  truck deer  cat   cat   dog   frog  car   truck dog   dog   horse ship  dog   frog  car  \n"
          ]
        }
      ],
      "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",
        "    plt.show()\n",
        "\n",
        "\n",
        "# get some random training images\n",
        "dataiter = iter(trainloader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print(labels)\n",
        "# print((images[0]-0.5)*2)\n",
        "# print labels\n",
        "print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "60c763799c9c4fa89e4a992e73f9ac8c",
            "327492b17c2448eda360acc482b1f33d",
            "b32199d2773a4c5895f21093e07605ac",
            "fd8f285c77f84957980e7f19f347406c",
            "b42ff004c85c41eabe655e7d3c7fc887",
            "fff4185e6cbc4598962714baea644f79",
            "b677c174c1af4aedb7a5936564c2809b",
            "079c7f49a1e54dd1a4d6fa49695158f9",
            "bf6b8003173e4d9cae2c6edbd562d067",
            "daf93e5c73444c6bbf16b2d97b550bc5",
            "27979e1e93a94be5968bb016da4cee04"
          ]
        },
        "id": "3zRyPLtakR40",
        "outputId": "7475ac05-3732-4c11-bbd7-69d63e823694"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/44.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60c763799c9c4fa89e4a992e73f9ac8c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "do = nn.Dropout(0.25)\n",
        "\n",
        "net = torchvision.models.resnet18(weights='DEFAULT')\n",
        "# for param in net.parameters():\n",
        "#     param.requires_grad = False\n",
        "num_ftrs = net.fc.in_features\n",
        "net.fc = nn.Sequential(\n",
        "     nn.Dropout(0.5),\n",
        "     nn.Linear(num_ftrs, 10)\n",
        ")\n",
        "\n",
        "net = net.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Dv6lamkkdyl"
      },
      "outputs": [],
      "source": [
        "PATH = './cifar_net.pth'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "emmu1nm1kbKA"
      },
      "outputs": [],
      "source": [
        "def train_loop(model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "    \n",
        "    model.train() \n",
        "\n",
        "    best_acc = 0.0\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "    \n",
        "    for epoch in range(num_epochs):\n",
        "      print(f\"Epoch {epoch+1}\\n-------------------------------\")\n",
        "      total = 0\n",
        "      running_loss = 0.0\n",
        "      correct = 0\n",
        "      for i, data in enumerate(trainloader, 0):\n",
        "          # get the inputs; data is a list of [inputs, labels]\n",
        "          inputs, labels = data[0].to(device), data[1].to(device)\n",
        "\n",
        "          # zero the parameter gradients\n",
        "          optimizer.zero_grad()\n",
        "\n",
        "          # forward + backward + optimize\n",
        "          outputs = model(inputs)\n",
        "          #compute acc\n",
        "          _, predicted = torch.max(outputs.data, 1)\n",
        "          total += labels.size(0)\n",
        "          correct += (predicted == labels).sum().item()\n",
        "\n",
        "          #compute loss\n",
        "          loss = criterion(outputs, labels)\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "\n",
        "          # print statistics\n",
        "          running_loss += loss.item()\n",
        "\n",
        "          n = 500\n",
        "          if i % n == n-1:    # print every 2000 mini-batches\n",
        "            print(f'[{i + 1:5d}] loss: {running_loss / n:.3f}')\n",
        "            running_loss = 0.0\n",
        "\n",
        "          \n",
        "      acc = correct / total\n",
        "      print(f'Train accuracy: {100 * acc:.3f} %')\n",
        "      scheduler.step() #will not use scheduler intended to overfit\n",
        "      epoch_acc = test_loop(net)\n",
        "      print(f'Current Learning Rate: {scheduler.get_last_lr()[0]:.7f}')\n",
        "      if epoch_acc > best_acc:\n",
        "          best_acc = epoch_acc\n",
        "          torch.save(model.state_dict(), PATH)\n",
        "          best_model_weights = model.state_dict()\n",
        "\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n",
        "    print(f'Best val Acc: {best_acc:4f}')\n",
        "\n",
        "\n",
        "    # load best model weights\n",
        "    return best_model_weights\n",
        "\n",
        "def test_loop(model):\n",
        "  net.eval()\n",
        "  correct = 0\n",
        "  total = 0\n",
        "  # since we're not training, we don't need to calculate the gradients for our outputs\n",
        "  with torch.no_grad():\n",
        "      for data in testloader:\n",
        "          inputs, labels = data[0].to(device), data[1].to(device)\n",
        "          # calculate outputs by running images through the network\n",
        "          outputs = net(inputs)\n",
        "          # the class with the highest energy is what we choose as prediction\n",
        "          _, predicted = torch.max(outputs.data, 1)\n",
        "          total += labels.size(0)\n",
        "          correct += (predicted == labels).sum().item()\n",
        "  acc = correct / total\n",
        "  print(f'Test accuracy: {100 * acc:.3f} %')\n",
        "  return acc\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6LN5yrV0kXz3"
      },
      "outputs": [],
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
        "# optimizer = optim.Adam(net.parameters(), lr=0.001) \n",
        "scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.93)\n",
        "# scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1) #83% + lr = 0.001"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "hB9-me-em-6L",
        "outputId": "3bae02d1-2345-4e3d-df80-05ecb793d96d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "[  500] loss: 1.747\n",
            "[ 1000] loss: 1.344\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-b1469d6dc1cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbest_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Done!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-7-ebdd95de9997>\u001b[0m in \u001b[0;36mtrain_loop\u001b[0;34m(model, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[1;32m     29\u001b[0m           \u001b[0;31m#compute loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m           \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m           \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m           \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    485\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m             )\n\u001b[0;32m--> 487\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    488\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    198\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "epochs = 1\n",
        "best_weights = train_loop(net, criterion, optimizer, scheduler, epochs)\n",
        "  \n",
        "print(\"Done!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nc8lbH6XkrKl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "outputId": "e1c6e189-558f-48a1-824a-5e4defc33a78"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GroundTruth:  cat   ship  ship  plane frog  frog  car   frog  cat   car   plane truck dog   horse truck ship \n"
          ]
        }
      ],
      "source": [
        "dataiter = iter(testloader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "# print images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print('GroundTruth: ', ' '.join(f'{classes[labels[j]]:5s}' for j in range(16)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GpaSaQ3IlPps"
      },
      "outputs": [],
      "source": [
        "# PATH2 = './cifar_net2.pth'\n",
        "# torch.save(best_weights, PATH)\n",
        "# net = net.to(device)\n",
        "net.load_state_dict(torch.load(PATH))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cb5uK41xWPdk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce0afc15-044e-45ae-9b8d-07d276df3cda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for class: plane is 59.7 %\n",
            "Accuracy for class: car   is 70.7 %\n",
            "Accuracy for class: bird  is 65.6 %\n",
            "Accuracy for class: cat   is 21.0 %\n",
            "Accuracy for class: deer  is 56.1 %\n",
            "Accuracy for class: dog   is 59.4 %\n",
            "Accuracy for class: frog  is 68.1 %\n",
            "Accuracy for class: horse is 67.1 %\n",
            "Accuracy for class: ship  is 67.3 %\n",
            "Accuracy for class: truck is 73.7 %\n"
          ]
        }
      ],
      "source": [
        "# prepare to count predictions for each class\n",
        "correct_pred = {classname: 0 for classname in classes}\n",
        "total_pred = {classname: 0 for classname in classes}\n",
        "\n",
        "# again no gradients needed\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data[0].to(device), data[1].to(device)\n",
        "        outputs = net(images)\n",
        "        # print(outputs)\n",
        "        _, predictions = torch.max(outputs, 1)\n",
        "        # print(predictions)\n",
        "        # collect the correct predictions for each class\n",
        "        for label, prediction in zip(labels, predictions):\n",
        "            if label == prediction:\n",
        "                correct_pred[classes[label]] += 1\n",
        "            total_pred[classes[label]] += 1\n",
        "\n",
        "\n",
        "# print accuracy for each class\n",
        "for classname, correct_count in correct_pred.items():\n",
        "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
        "    print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data[0].size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5cqcxmt91ED",
        "outputId": "3e4ebd4c-06e7-4f65-d895-f7d47ff7030c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([16, 3, 32, 32])\n"
          ]
        }
      ]
    },
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      "metadata": {
        "id": "jxIfZZka97xg"
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    }
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