07_CV_LeNet5_PyTorch_p2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGBUEdKeTwCv"
      },
      "source": [
        "# **Building a Convolutional Neural Network (CNN)**\n",
        "\n",
        "- 🌟[CNN Explainer](https://poloclub.github.io/cnn-explainer/)\n",
        "\n",
        "- 🌟[Deep Lizard](https://deeplizard.com/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KER4hXzb6WCU"
      },
      "source": [
        "## LeNet -5 Architecture in pytorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "116b62cbaeb0406bb9f67449f6197c4e",
            "62020a1926134f6c851f75e121f8eeff"
          ]
        },
        "id": "eZdKOo-64u_w",
        "outputId": "86a3962f-bb3f-4247-eccf-8ba03b1d9631"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.2.1+cu121\n",
            "0.17.1+cu121\n",
            "Number of classes: 10\n",
            "Using device: cpu\n",
            "LeNet5(\n",
            "  (conv_stack): Sequential(\n",
            "    (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
            "    (1): ReLU()\n",
            "    (2): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
            "    (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
            "    (4): ReLU()\n",
            "    (5): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
            "    (6): Conv2d(16, 120, kernel_size=(5, 5), stride=(1, 1))\n",
            "    (7): ReLU()\n",
            "  )\n",
            "  (linear_stack): Sequential(\n",
            "    (0): Linear(in_features=120, out_features=84, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=84, out_features=10, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "116b62cbaeb0406bb9f67449f6197c4e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 0\n",
            "--------\n",
            "Train loss: 1.60895 | Train acc: 48.98%\n",
            "Test loss: 0.44816 | Test acc: 86.13%\n",
            "\n",
            "Epoch: 1\n",
            "--------\n",
            "Train loss: 0.34061 | Train acc: 89.70%\n",
            "Test loss: 0.25557 | Test acc: 91.99%\n",
            "\n",
            "Epoch: 2\n",
            "--------\n",
            "Train loss: 0.22259 | Train acc: 93.16%\n",
            "Test loss: 0.17125 | Test acc: 94.66%\n",
            "\n",
            "Epoch: 3\n",
            "--------\n",
            "Train loss: 0.16252 | Train acc: 95.02%\n",
            "Test loss: 0.12970 | Test acc: 96.13%\n",
            "\n",
            "Epoch: 4\n",
            "--------\n",
            "Train loss: 0.12751 | Train acc: 96.06%\n",
            "Test loss: 0.10473 | Test acc: 96.74%\n",
            "\n",
            "Epoch: 5\n",
            "--------\n",
            "Train loss: 0.10579 | Train acc: 96.76%\n",
            "Test loss: 0.09134 | Test acc: 96.97%\n",
            "\n",
            "Epoch: 6\n",
            "--------\n",
            "Train loss: 0.09013 | Train acc: 97.26%\n",
            "Test loss: 0.08181 | Test acc: 97.53%\n",
            "\n",
            "Epoch: 7\n",
            "--------\n",
            "Train loss: 0.07948 | Train acc: 97.51%\n",
            "Test loss: 0.08484 | Test acc: 97.46%\n",
            "\n",
            "Epoch: 8\n",
            "--------\n",
            "Train loss: 0.07179 | Train acc: 97.75%\n",
            "Test loss: 0.07691 | Test acc: 97.62%\n",
            "\n",
            "Epoch: 9\n",
            "--------\n",
            "Train loss: 0.06626 | Train acc: 97.94%\n",
            "Test loss: 0.06862 | Test acc: 97.72%\n",
            "\n",
            "Train time on cpu: 338.101 seconds\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "62020a1926134f6c851f75e121f8eeff",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'model_name': 'LeNet5', 'model_loss': 0.06861866265535355, 'model_acc': 97.72364217252397}\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "import matplotlib.pyplot as plt\n",
        "from timeit import default_timer as timer\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Check versions\n",
        "print(torch.__version__)\n",
        "print(torchvision.__version__)\n",
        "\n",
        "# Set seed for reproducibility\n",
        "torch.manual_seed(42)\n",
        "\n",
        "# ------------------------------Datasets--------------------------------------\n",
        "# MNIST dataset will be used instead of FashionMNIST for LeNet-5\n",
        "train_data = datasets.MNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        "    target_transform=None\n",
        ")\n",
        "\n",
        "test_data = datasets.MNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        "    target_transform=None\n",
        ")\n",
        "\n",
        "# Get class names\n",
        "class_names = train_data.classes\n",
        "print(f\"Number of classes: {len(class_names)}\")\n",
        "\n",
        "# ------------------------------DataLoader------------------------------------\n",
        "BATCH_SIZE = 32\n",
        "train_dataloader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\n",
        "test_dataloader = DataLoader(dataset=test_data, batch_size=BATCH_SIZE, shuffle=False)\n",
        "\n",
        "# Set device\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(f\"Using device: {device}\")\n",
        "\n",
        "# -----------------------------------MODEL-----------------------------------\n",
        "# Define the LeNet-5 model architecture\n",
        "class LeNet5(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(LeNet5, self).__init__()\n",
        "        self.conv_stack = nn.Sequential(\n",
        "            nn.Conv2d(1, 6, kernel_size=5, padding=2), # C1\n",
        "            nn.ReLU(),\n",
        "            nn.AvgPool2d(kernel_size=2, stride=2),     # S2\n",
        "            nn.Conv2d(6, 16, kernel_size=5),           # C3\n",
        "            nn.ReLU(),\n",
        "            nn.AvgPool2d(kernel_size=2, stride=2),     # S4\n",
        "            nn.Conv2d(16, 120, kernel_size=5),         # C5\n",
        "            nn.ReLU()\n",
        "        )\n",
        "        self.linear_stack = nn.Sequential(\n",
        "            nn.Linear(120, 84),                        # F6\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(84, 10)                          # Output layer\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv_stack(x)\n",
        "        x = torch.flatten(x, 1) # Flatten the tensor\n",
        "        x = self.linear_stack(x)\n",
        "        return x\n",
        "\n",
        "# Create an instance of the LeNet-5 model\n",
        "model = LeNet5().to(device)\n",
        "print(model)\n",
        "\n",
        "# ------------------------------Loss and Optimizer-----------------------------\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params=model.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "# Define accuracy function\n",
        "def accuracy_fn(y_true, y_pred):\n",
        "    correct = torch.eq(y_true, y_pred).sum().item()\n",
        "    acc = (correct / len(y_pred)) * 100\n",
        "    return acc\n",
        "\n",
        "# Initialize lists to hold metrics\n",
        "train_losses = []\n",
        "test_losses = []\n",
        "train_accuracies = []\n",
        "test_accuracies = []\n",
        "\n",
        "\n",
        "# Functionizing training and evaluation/testing loops\n",
        "### ------------------------------Training loop---------------------------------\n",
        "def train_step(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               optimizer: torch.optim.Optimizer,\n",
        "               accuracy_fn,\n",
        "               device: torch.device = device):\n",
        "\n",
        "    train_loss, train_acc = 0, 0\n",
        "\n",
        "    # Put the model into training mode\n",
        "    model.train()\n",
        "\n",
        "    # looping through the training batches\n",
        "    for batchs, (X, y) in enumerate(data_loader):\n",
        "        # Put the data on the target device\n",
        "        X, y = X.to(device), y.to(device)\n",
        "\n",
        "        # 1. Forward pass(output the raw logits from the model)\n",
        "        y_pred = model(X)\n",
        "\n",
        "        # 2. Calculate the loss and accuracy(per batch)\n",
        "        loss = loss_fn(y_pred, y)\n",
        "        train_loss += loss  # accumulate train loss\n",
        "        train_acc += accuracy_fn(y_true=y, y_pred=torch.argmax(y_pred, dim=1))\n",
        "\n",
        "        # 3. Optimizer zero grad\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # 4. Loss backward\n",
        "        loss.backward()\n",
        "\n",
        "        # 5. Optimizer step\n",
        "        optimizer.step()\n",
        "\n",
        "    # Divide total train loss and acc by length of train dataloader\n",
        "    train_loss /= len(data_loader)\n",
        "    train_acc /= len(data_loader)\n",
        "    print(f\"Train loss: {train_loss:.5f} | Train acc: {train_acc:.2f}%\")\n",
        "\n",
        "    # Return the loss and accuracy\n",
        "    return train_loss.item(), train_acc\n",
        "\n",
        "\n",
        "### ----------------------------Testing loop------------------------------------\n",
        "def test_step(model: torch.nn.Module,\n",
        "              data_loader: torch.utils.data.DataLoader,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              accuracy_fn,\n",
        "              device: torch.device = device):\n",
        "\n",
        "    test_loss, test_acc = 0, 0\n",
        "\n",
        "    # Put the model in  eval mode\n",
        "    model.eval()\n",
        "\n",
        "    # Turn on the inference mode context manager\n",
        "    with torch.inference_mode():\n",
        "        for X, y in data_loader:\n",
        "            # Send the data to the target device\n",
        "            X, y = X.to(device), y.to(device)\n",
        "\n",
        "            # 1. Forward pass(Output the raw logits)\n",
        "            test_pred = model(X)\n",
        "\n",
        "            # 2. Calculate the loss/acc\n",
        "            test_loss += loss_fn(test_pred, y)\n",
        "            test_acc += accuracy_fn(y_true=y, y_pred=torch.argmax(test_pred, dim=1)) # go from logits -> prediction labels\n",
        "\n",
        "        ## Adjust metrics and print out\n",
        "        test_loss /= len(data_loader)\n",
        "        test_acc /= len(data_loader)\n",
        "        print(f\"Test loss: {test_loss:.5f} | Test acc: {test_acc:.2f}%\\n\")\n",
        "\n",
        "    # Return the loss and accuracy\n",
        "    return test_loss.item(), test_acc\n",
        "\n",
        "\n",
        "### ---------------------------Evaluation loop----------------------------------\n",
        "\n",
        "def print_train_time(start:float,\n",
        "                     end: float,\n",
        "                     device: torch.device=None):\n",
        "    total_time = end - start\n",
        "    print(f\"Train time on {device}: {total_time:.3f} seconds\")\n",
        "    return total_time\n",
        "\n",
        "# Set epoch\n",
        "epochs = 10\n",
        "\n",
        "start_timer = timer() # <<<-start timer->>>\n",
        "\n",
        "# Create the optimiztion and evaluation loop using train_step() and test_step()\n",
        "for epoch in tqdm(range(epochs)):\n",
        "    print(f\"Epoch: {epoch}\\n--------\")\n",
        "\n",
        "    train_loss, train_acc = train_step(model=model,\n",
        "                                       data_loader=train_dataloader,\n",
        "                                       loss_fn=loss_fn,\n",
        "                                       optimizer=optimizer,\n",
        "                                       accuracy_fn=accuracy_fn,\n",
        "                                       device=device)\n",
        "\n",
        "    test_loss, test_acc = test_step(model=model,\n",
        "                                     data_loader=test_dataloader,\n",
        "                                     loss_fn=loss_fn,\n",
        "                                     accuracy_fn=accuracy_fn,\n",
        "                                     device=device)\n",
        "\n",
        "    # Append the values to the lists\n",
        "    train_losses.append(train_loss)\n",
        "    train_accuracies.append(train_acc)\n",
        "    test_losses.append(test_loss)\n",
        "    test_accuracies.append(test_acc)\n",
        "\n",
        "end_timer = timer() # <<<-End Timer->>>\n",
        "total_train_time_model_1 = print_train_time(start=start_timer,\n",
        "                                            end=end_timer,\n",
        "                                            device=device)\n",
        "\n",
        "\n",
        "### ------------------------------Predictions-----------------------------------\n",
        "def eval_model(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               accuracy_fn):\n",
        "    loss, acc = 0, 0\n",
        "    model.eval()\n",
        "    with torch.inference_mode():\n",
        "        for X, y in tqdm(data_loader):\n",
        "            # Make our data device agnostic\n",
        "            X, y = X.to(device), y.to(device)\n",
        "            # Make predictions\n",
        "            y_pred = model(X)\n",
        "\n",
        "            # Accumulate the loss and acc values per batch\n",
        "            loss += loss_fn(y_pred, y)\n",
        "            acc += accuracy_fn(y_true=y,\n",
        "                               y_pred=y_pred.argmax(dim=1))\n",
        "\n",
        "        # Scale loss and acc to find the average loss/acc per batch\n",
        "        loss /= len(data_loader)\n",
        "        acc /= len(data_loader)\n",
        "\n",
        "    return {\"model_name\": model.__class__.__name__,\n",
        "            \"model_loss\": loss.item(),\n",
        "            \"model_acc\": acc}\n",
        "\n",
        "model_1_results = eval_model(model=model,\n",
        "                             data_loader=test_dataloader,\n",
        "                             loss_fn=loss_fn,\n",
        "                             accuracy_fn=accuracy_fn)\n",
        "print(model_1_results)\n",
        "\n",
        "\n",
        "### -------------------------------visualization-------------------------------\n",
        "print(\"\\n\")\n",
        "\n",
        "# Plotting training and testing metrics\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(range(epochs), train_losses, label=\"Training Loss\", marker='.')\n",
        "plt.plot(range(epochs), test_losses, label=\"Test Loss\", marker='.')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Training and Test Loss\")\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(range(epochs), train_accuracies, label=\"Training Accuracy\", marker=\".\")\n",
        "plt.plot(range(epochs), test_accuracies, label=\"Test Accuracy\", marker=\".\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Training and Test Accuracy\")\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_model(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               accuracy_fn,\n",
        "               device: torch.device = device):\n",
        "    loss, acc = 0, 0\n",
        "    model.eval()\n",
        "    with torch.inference_mode():\n",
        "        # Initialize a counter\n",
        "        counter = 0\n",
        "        for X, y in tqdm(data_loader):\n",
        "            # Make our data device agnostic\n",
        "            X, y = X.to(device), y.to(device)\n",
        "            # Make predictions\n",
        "            y_pred = model(X)\n",
        "\n",
        "            # Visualize the first image in the batch with its prediction\n",
        "            if counter < 10:  # Check if the counter is less than 10\n",
        "                plt.imshow(X[0].cpu().squeeze(), cmap='gray')\n",
        "                plt.title(f\"Predicted: {class_names[y_pred[0].argmax(dim=0).item()]}\")\n",
        "                plt.show()\n",
        "                counter += 1  # Increment the counter\n",
        "\n",
        "            # Accumulate the loss and acc values per batch\n",
        "            loss += loss_fn(y_pred, y)\n",
        "            acc += accuracy_fn(y_true=y,\n",
        "                               y_pred=y_pred.argmax(dim=1))\n",
        "\n",
        "            # Break the loop if 10 images have been visualized\n",
        "            if counter >= 10:\n",
        "                break\n",
        "\n",
        "        # Scale loss and acc to find the average loss/acc per batch\n",
        "        loss /= len(data_loader)\n",
        "        acc /= len(data_loader)\n",
        "\n",
        "    return {\"model_name\": model.__class__.__name__,\n",
        "            \"model_loss\": loss.item(),\n",
        "            \"model_acc\": acc}\n",
        "\n",
        "# Example usage\n",
        "model_1_results = eval_model(model=model,\n",
        "                             data_loader=test_dataloader,\n",
        "                             loss_fn=loss_fn,\n",
        "                             accuracy_fn=accuracy_fn)\n",
        "print(model_1_results)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "ad9c1bafce7741c6b7e6d599a6f65c7d",
            "80095e77788848bb881c1a4d1b93bb34",
            "8da273fc00c14731857eb25abdce4631",
            "603af0a7c5bd479b91c018857cfd23e1",
            "9ff520b9520348aabb777419f087115d",
            "911b7416277847539559829e97fdb6f2",
            "fd5c0a5ec1a940aea4e3acb927865a20",
            "cde171b59b814d838e41a322f3095dd3",
            "88315b44b17a405596c711d38f6c824e",
            "537d4387c91344948350c1122e74475e",
            "a1b042956fdf4d6ba385480ee8c50ca8"
          ]
        },
        "id": "WbaSjQDr7q4I",
        "outputId": "ed4e02bb-f568-441c-ffcb-1f7702314a9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad9c1bafce7741c6b7e6d599a6f65c7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAG0CAYAAAB0cfPUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAtdElEQVR4nO3dfVRVdb7H8c8BDgSHg6AMKqSgEtKk4lPaA6aJTda1skfLaa5TYS21utk0tSanFMu52WRjd+o2rcKUW+kgN1PLnh8Mx0rLh1QqKzUdHDINhEARYd8/zuLcjueAbDzw4+H9Wsu12r+9f3v/zrd9zoe9zz57OyzLsgQAQCsLMT0AAEDnRAABAIwggAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQChzXI4HBozZoxP25w5c+RwOPTBBx8YGZNd7W28p+KDDz6Qw+HQnDlzTA8F7QQB1Mk5HA6ff6GhoYqPj9fYsWP10ksvmR5eiwgUbO3BokWLNHHiRKWmpiomJkYul0tnnnmmpk6dqq+++qrFt79nzx45HA799re/bfFtoXMIMz0AtA2zZ8+WJNXU1OjLL7/UypUr9f777+vTTz/V448/bnh0/+/222/X9ddfr969e5seSqt74YUX9K9//UsjR45Ujx49FBISoh07duj5559XXl6eXnnlFV1yySWmhwk0GQEESfI7bfLuu+/qoosu0sKFC3XnnXcqJSXFyLhOFB8fr/j4eNPDMGLNmjU67bTT/Nrffvtt/epXv9Lvfvc7AgjtCqfgEFBWVpbS09NlWZY2btwoyff7jJdeekkjR45UdHS0TzhVVVXpP//zPzV48GC5XC5FR0fr3HPP1dKlSwNu59ixY3rooYfUr18/RUREqE+fPvrjH/+o6urqgMs39p3Kl19+qZtvvlkpKSmKiIhQQkKCRo0apaefflqStHjxYjkcDknS2rVrfU49nhjAn3zyia655hr16NFD4eHh6tWrl2677Tbt378/4Lg+++wzjR8/Xm63WzExMRo3bpw++uijxkpsW6DwkaSLLrpIsbGx+uabb4K6vZ+bM2eO+vTpI0lasmSJT+0WL17st/yWLVv0b//2b4qNjVVUVJRGjx6t9evXB1xvsPcpSXrzzTd16aWXKj4+XhEREerXr59+//vfq6ys7FRLgSDiCAgNqn9SR/2Hdr0FCxbo7bff1mWXXaYLL7xQhw8fliSVlZVp7Nix2rx5s4YOHaqbb75ZdXV1evPNNzV58mTt2LFDDz/8sM/6r7vuOq1cuVL9+vXT7bffrmPHjmnRokXatm2brbG+9tpruvbaa1VdXa3x48frhhtuUFlZmbZu3apHH31U06ZN0+DBgzV79mzl5OQoOTnZ57uMn38ntGjRIt16662KiIjQ5Zdfrl69eunrr7/Wc889p9WrV+vjjz/2OQW4fv16jRs3TseOHdNVV12l1NRUbdmyRWPGjNHYsWMDjnfOnDnKycnR7NmzT/lL+3Xr1qmsrExDhw49pfU0ZsyYMSorK9MTTzyhjIwMTZw40Ttv8ODBPst++umnevTRR3XuuecqOztbe/fu1f/+7/8qKytLW7ZsUf/+/f3WH6x9SpJycnI0Z84cde3aVRMmTFBCQoI+//xzPfbYY1qzZo0++ugjxcTEBL1GaAYLnZokK9Bu8Pbbb1sOh8NyOBzWnj17LMuyrNmzZ1uSrKioKGvTpk1+faZMmWJJsubPn+/TfuTIEeviiy+2HA6HtXnzZm/7iy++aEmyzjnnHOvIkSPe9kOHDll9+/a1JFmjR4/2WVf9GN5//31v2w8//GDFxMRYTqfT+uCDD/zGtW/fPr/XfOJ663311VeW0+m0+vXrZ/3zn//0mffOO+9YISEh1sSJE71tdXV1Vv/+/S1J1iuvvOKz/MKFC731/fl4f/46Zs+eHXAcjVm+fLk1e/Zs695777UmTpxohYeHW127drXWr19ve1127N6925JkTZkyJeD8999/3/t6n3/+eZ95f/vb3yxJ1rRp03zag71Pvffee5Yk69xzz7VKS0t9+jz//POWJOuuu+5q8mtGyyKAOrn6D4zZs2dbs2fPtu6//37r6quvtkJDQy1J1syZM73L1n9YBHoDHzx40AoNDbWGDx8ecDtbtmyxJFm///3vvW3jxo2zJFnvvfee3/L1HxZNCaDHHnvMkmTdeeedTX7NDQXQXXfdZUmyXn311YDzJ06caIWGhlrl5eWWZVnWunXrLEnWBRdc4Lfs8ePHrX79+gUMoB9++MH64osvrB9++KFJY/65SZMmef+/SbLOOOMMa+PGjbbXY1dTA+j888/3m3fs2DErLCzMGjZsmE97sPepiRMnWpKs7du3B+wzePBg6xe/+EVDLxGtjFNwkOQ5bSF5TrfFxsZq1KhRuuWWW3TjjTf6LTtixAi/to0bN6q2trbB34HU1NRIkr744gtv26ZNmxQSEqLMzEy/5e1cJv3xxx9LUlC+gK//3mbt2rXe775+7sCBA6qtrdXOnTs1bNgwbdq0SZI0evRov2VDQ0OVmZmpb7/91m/eqVxMsWzZMi1btkzl5eXavn27cnJydP755+uZZ55p0iXSr7zyirZs2eLTNnjwYJ/Taqdi+PDhfm1Op1Pdu3dXaWlpwD7B2qc++ugjOZ1OLV++XMuXL/frc+zYMf3www86dOiQunXr1tSXhBZCAEHS/3/f0xQ9evTwazt06JAkz4dGoA/uej/99JP3vw8fPqyuXbvK6XQ2aRsNqf9iOSkpqcl9GlL/Ov785z83ulz966j/rqJ79+4Bl7PzOuyKiYnReeedp9WrV2v48OGaNm2axo0bp9NPP73Rfq+88oqWLFni0zZlypSgBVBsbGzA9rCwMNXW1gacF6x96tChQzp+/Lj3D6rG+hBA5nEVHGw78aIESerSpYskaebMmbI8p3YD/nv//fd9+vz444/ev2R/rqSkpMnjqf/AKy4utvlK/NW/jsOHDzf6OuqPeOqX//777wOuz87raK7w8HBlZWXp6NGj3qPBxixevNjv9QS6kq01BXOfiouLa3R5y7KUnJzcaq8NDSOAEBQjRoxQSEiICgsLm9xn6NChqqur07p16/zm2bl1zTnnnCNJev3115u0fEhISIN/idevq6mvo/7Ks7Vr1/rNq62tDfjaWkJ9+IaFtdxJjdDQUElqsHbB1px96pxzzlFpaal27NjRgiNDsBBACIqEhAT9+te/1qeffqqHHnoo4IfUt99+q927d3unb7rpJknSrFmzdPToUW/7jz/+6HdpbWOmTJmimJgYPf300/rwww/95v/zn//0me7WrZv27dsXcF233367nE6nZs6cqZ07d/rNP3bsmM8H4nnnnaf+/fvrww8/1MqVK32WffLJJwN+/yNJBw8e1JdffqmDBw+e9PVJnlNLu3btCjjv1Vdf1YoVKxQdHR3wu6hgiYuLk8Ph0N69e1tsGz/XnH1q5syZkqSpU6cG/M1WZWVlk44S0Tr4DghB8+STT+rrr7/Wgw8+qP/5n/9RZmamunfvrv379+uLL77Qxo0btXTpUu8PGm+44Qb9/e9/16pVqzRgwABdccUVqqmpUUFBgc4+++wGP7xPFB8fr5deeknXXHONLrzwQl1yySUaNGiQysvL9fnnn2vfvn0+H1JZWVlatmyZLrvsMg0dOlROp1MXXHCBLrjgAqWnp2vRokW6+eabddZZZ2n8+PFKS0tTTU2N9u7dq8LCQv3iF7/Ql19+Kclz6ig3N1cXXXSRrr76ap/fAb377rsaP3683njjjYC1svM7oH379mnYsGEaPny4+vfvr6SkJJWVlWnLli36+OOP5XQ69dxzzykuLq5JNWuO6OhojRw5UoWFhfr1r3+ttLQ0hYaG6vLLL9egQYNaZJt296msrCw98sgj+sMf/qAzzjhDl156qfr06aOffvpJ3333ndauXavMzMyA/09gQEtfZoe2TQ38DiiQQJdAn6i6utr661//ap177rlWTEyMFR4ebvXq1csaO3as9Ze//MU6ePCg3/I5OTlWnz59rPDwcCs5Odm6//77raNHjzb5Mux627dvt37zm99YiYmJltPptBISEqwLLrjAeuaZZ3yW+/77760bbrjBSkhIsEJCQgL+Hufzzz+3pkyZYvXu3dsKDw+34uLirLPOOsu69dZbrXfffddv259++ql18cUXW9HR0VZ0dLSVlZVlrV+/vsHx2v0d0I8//mjNmjXLyszMtHr06GE5nU4rKirKSk9Pt2677TarqKioSes5VV9//bU1YcIEq2vXrpbD4fD5zU/9ZdgNvabk5GQrOTnZp60l9inLsqzCwkLr2muvtXr27Gk5nU4rPj7eysjIsGbOnNkql6yjaRyWZePyJwAAgoTvgAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIxos7fimTx5svd2J/VcLpcKCws1atQoVVZWGhqZedTBgzp4UAcP6uDRFuqQnp6ul1566aTLtdidEN544w2tXr1aZWVlSk5O1s0336zU1NQm9x86dKg2b97s0+Z2u1VeXq6YmBhVVFQEe8jtBnXwoA4e1MGDOni0hToMGTLE+7DGxrTIKbj169crLy9P11xzjebPn6/k5GTNmzfP+/AuAABaJIBeffVVZWVl6cILL9Tpp5+uqVOnKjw83OfBUQCAzi3o3wEdP35cu3bt8nm8b0hIiAYOHBjw+So1NTU+T8R0OByKjIyUy+WS2+32WbZ++sT2zoY6eFAHD+rgQR082kIdXC5Xk5YLegCVl5errq7O77nwsbGxAR8QtWLFChUUFHin+/Tpo/nz5zf6FMRgPHq5I6AOHtTBgzp4UAeP9lAH41fBXXnllZowYYJ3uv7Z8KNGjdLWrVt9lnW73SouLlZSUlKn/5KROlCHetTBgzp4tIU6ZGRkNOlR6kEPoJiYGIWEhKisrMynvayszO+oSJKcTqecTqdfe2VlZYPFq6io6NQ7WD3q4EEdPKiDB3XwMFmHpl7+HfSLEMLCwtS3b19t377d21ZXV6ft27crLS0t2JsDALRTLXIKbsKECXrqqafUt29fpaamas2aNaqurtaYMWNaYnMAgHaoRQLovPPOU3l5ufLz81VWVqaUlBTdf//9AU/BAQA6pxa7CGH8+PEaP358S60eANDOcTNSAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADAizPQA0H7dcssttvs899xzQR9HeXl50Ndpx9dff227T3PqsGrVqoDtLpdLkpSWlqbKykqfeV9++aXt7QCthSMgAIARBBAAwIign4LLz89XQUGBT1tiYqIWLlwY7E0BANqxFvkOqFevXnrggQe80yEhHGgBAHy1SACFhIQoNja2JVYNAOggWiSASkpKdNttt8npdCotLU2TJ09WfHx8wGVrampUU1PjnXY4HIqMjJTL5ZLb7fZZtn76xPbOpq3U4bTTTjO6/baiOUf4ERERtvvUX+3WUHug+ab3kdbUVt4XprWFOjS0r57IYVmWFcwNb968WUePHlViYqJKS0tVUFCgH3/8UQsWLFBkZKTf8id+Z9SnTx/Nnz8/mEMCALRBQQ+gE1VWVmr69OmaMmWKxo4d6ze/oSOgUaNGaevWrT7Lut1uFRcXKykpSRUVFS057DatrdTh3//93233efLJJ1tgJGZ9++23tvssWbLEdp81a9YEbHe5XFq7dq1Gjx7t9zugnTt32t5Oe9VW3hemtYU6ZGRkqLCw8KTLtfgPUV0ulxITE1VSUhJwvtPplNPp9GuvrKxssHgVFRWdegerZ7oOR48eNbbttqSurs52n+rqatt9TgyXQPNPXKYzvk9Mvy/aCpN1ONm+Wq/FL087evSoSkpKuCgBAOAj6EdAeXl5Gj58uOLj41VaWqr8/HyFhIQoMzMz2JsCALRjQQ+gH3/8UU888YQqKioUExOj9PR0zZs3TzExMcHeFACgHQt6AN11113BXiVa2OrVq5vV76KLLrLdJ9jXvDgcjqCv067U1FTbfR555BHbfbp37x6wPTw8XJL029/+VseOHfOZ97vf/c72doDWwi0KAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMCIFn8gHVrXmDFjbPcJ9KTapqi/CaYd27Zts93nu+++C9geFhamSy65RG+88YaOHz9ue72BzJs3z3afAQMG2O7z7LPP2u5zxx13NDr/tttu82vbvHmz7e1I0gsvvNCsfoAdHAEBAIwggAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARhBAAAAjCCAAgBEEEADACO6G3cE0527TK1eubNa23G637T633nqr7T7/+te/Gtx+eXm5Jk2apIqKCtvrDZaYmJhW2U5YWONv10Dz4+LiWmo4wCnjCAgAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjOBmpB3MoUOHbPeZPHlyC4wEwXb8+PEG5zmdzoDzy8vLW3JIwCnhCAgAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjOBmpMDPuN1u232uvfbaFhiJv//+7/8O2B4eHq5p06bpueee07Fjx3zmLVmypDWGBjQLR0AAACMIIACAEbZPwRUVFWnVqlXavXu3SktLdc8992jEiBHe+ZZlKT8/X++++64qKyuVnp6u7Oxs9ezZM6gDBwC0b7aPgKqrq5WSkqJbbrkl4PyVK1fq9ddf19SpU/WnP/1JERERmjdvnt+5aQBA52Y7gIYMGaLrr7/e56innmVZWrNmja666iqdffbZSk5O1u23367S0lJt3LgxKAMGAHQMQb0K7sCBAyorK9OgQYO8bVFRUUpNTdXOnTt1/vnn+/WpqalRTU2Nd9rhcCgyMlIul8vviqT66eZcqdSRUAePlqhDc9bldDqDtv3GhIeHN7r9QOPoTPsI7wuPtlAHl8vVpOWCGkBlZWWSpC5duvi0d+nSxTvvRCtWrFBBQYF3uk+fPpo/f74KCwsb3E5xcfEpj7UjoA4enaUO06ZNa3R+dna27T4dUWfZH06mPdTB+O+ArrzySk2YMME77XA4JEmjRo3S1q1bfZZ1u90qLi5WUlKSKioqWnWcbQl18GiJOjTnr8Z58+bZ7jNlyhTbff72t78FbHc6ncrOztZzzz3nczZBku677z7b22mveF94tIU6ZGRkNHoQUS+oARQbGytJOnz4sOLi4rzthw8fVkpKSsA+Tqcz4KmDysrKBotXUVHRqXewetTBw3QdTvzQbyknu5CnpqbGb5nOuH+Y3h/aCpN1qKysbNJyQf0dUEJCgmJjY7Vt2zZvW1VVlb755hulpaUFc1MAgHbO9hHQ0aNHVVJS4p0+cOCA9uzZo+joaMXHx+vSSy/Vyy+/rJ49eyohIUHLli1TXFyczj777KAOHADQvtkOoG+//VY5OTne6by8PEnS6NGjNWPGDF1xxRWqrq7WM888o6qqKqWnp+v+++9v8AoeAEDnZDuAzjrrLOXn5zc43+FwaNKkSZo0adIpDQw4Feecc06z+r355pu2+7TW5a6ffPJJwPbIyEhJ0qeffqojR460yliAYOBecAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADDC+CO50bk057Ec06ZNC9geERHhnV9dXe0zb/78+fYHp+aNrzmKi4tt99m8eXPAdpfLJUnaunVrk59ECbQFHAEBAIwggAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARhBAAAAjCCAAgBHcjBTNlpycbLvP+vXrbffp2bNno/MfeeQR2+s0LSkpyXafNWvWBGx3OBySpIKCAlmW5TPv/vvvtz84ScuWLWtWP8AOjoAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAhuRopmCwuzv/uc7MaiaFhKSkqj8wPdHPbFF19s1rbuu+8+231uuukm2322bNliuw86Do6AAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIbkaKZquurrbdJ5g3nwwJCVFGRoY+//xz1dXVBWWdDz30kO0+FRUVQdn2yfzhD38I2B4aGqrRo0frww8/VG1trc+8Cy+8sFnbysjIsN1n9erVtvtMnDjRdp/PPvvMdh+0TRwBAQCMIIAAAEbYPgVXVFSkVatWaffu3SotLdU999yjESNGeOc/9dRTWrt2rU+fjIwMzZo169RHCwDoMGwHUHV1tVJSUjR27Fg99thjAZcZPHiwpk+f/v8bacaDywAAHZvtZBgyZIiGDBnS+ErDwhQbG9vcMQEAOoEWOTQpKipSdna2XC6XBgwYoOuvv15utzvgsjU1NaqpqfFOOxwORUZGyuVy+fWpn25oXZ1FW6lDdHS07T4hIcH72rF+XcFcZ2RkpO0+J1551lJCQ0MbbW9ofmtxOBy2+7hcLtt9Gtrv28r7wrS2UIem/n91WJZlNXcj1113nd93QP/4xz8UERGhhIQElZSUaOnSpTrttNM0b968gB8U+fn5Kigo8E736dNH8+fPb+6QAADtRNCPgM4//3zvf/fu3VvJycm64447tGPHDg0cONBv+SuvvFITJkzwTtf/FTVq1Cht3brVZ1m3263i4mIlJSW12m8v2qK2UoekpCTbfZYtWxa07YeEhGjgwIHatm1b0H4H9Oijj9ru89NPPwVl2ydz9913B2wPDQ1VZmam1q1b53c0dsEFF7TG0CRJ+/fvt91n8uTJtvts3rw5YHtbeV+Y1hbqkJGRocLCwpMu1+JXB3Tv3l1ut1slJSUBA8jpdMrpdPq1V1ZWNli8ioqKTr2D1TNdh+Z88AYrKE5cZ7DWe+TIEdt9qqqqgrLtkznZqb7a2tpWOx0YSHNOplRWVtruc7J93vT7oq0wWYem/n9t8d8BHTp0SD/99JPi4uJaelMAgHbE9hHQ0aNHVVJS4p0+cOCA9uzZo+joaEVHR2v58uUaOXKkYmNj9f333+uFF15Qjx49mnVrDwBAx2U7gL799lvl5OR4p/Py8iRJo0eP1tSpU7V3716tXbtWlZWV6tq1qwYNGqRJkyYFPM0GAOi8TukquJY0dOhQvy8b3W63ysvLFRMT06nP8VIHj85Wh/POOy9ge1RUlN5++21ddNFFft9H3Xnnnc3a1nXXXdesfnbt27fPdp+srKyA7dHR0dq8ebOGDBni9/3kN99806zxtUdt4X0xZMgQbdq06aTLcS84AIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGNHiT0QFEBzr168P2O52uyVJn3zyid/djz/55JNmbSsyMtJ2n8suu8x2n169etnuk5CQELA9KipKkhQfH+/973qd6W7Y7QlHQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBDcjBTqw2traZvW74oorbPd5+eWXbfeZOHGi7T4vvPBCwHaHwyFJevbZZ2VZls+8X/3qV7a3I3ET05bGERAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGMHNSAEExVtvvWW7T3NuRpqSktLo/OTkZL+2/v37296OxM1IWxpHQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBDcjBeAnPT3ddp9Zs2a1wEjQkXEEBAAwggACABhh6xTcihUrtGHDBhUXFys8PFxpaWm68cYblZiY6F3m2LFjysvL0/r161VTU6OMjAxlZ2crNjY22GMHALRjto6AioqKdPHFF2vevHn64x//qNraWj388MM6evSod5klS5bos88+0913362cnByVlpZqwYIFQR84AKB9sxVAs2bN0pgxY9SrVy+lpKRoxowZOnjwoHbt2iVJqqqq0nvvvacpU6ZowIAB6tu3r6ZPn66vvvpKO3fubJEXAABon07pKriqqipJUnR0tCRp165dqq2t1cCBA73LJCUlKT4+Xjt37lRaWprfOmpqalRTU+OddjgcioyMlMvlktvt9lm2fvrE9s6GOnhQB4+WqIPL5bLdx+FwBG37wRYZGdmsfu1x32oL74um7j/NDqC6ujotXrxY/fv3V+/evSVJZWVlCgsL89t4ly5dVFZWFnA9K1asUEFBgXe6T58+mj9/vgoLCxvcdnFxcXOH3aFQBw/q4EEdPAIF4fLlyw2MxKz2sD80O4Byc3O1b98+zZ0795QGcOWVV2rChAne6fqdZ9SoUdq6davPsm63W8XFxUpKSlJFRcUpbbc9ow4e1MGjJeoQ6GzFyaxcudJ2n59fwBQMDodDlmX5tV933XXNWt+bb755qkNqdW3hfZGRkdHoQUS9ZgVQbm6uNm3apJycHHXr1s3bHhsbq+PHj6uystLnKOjw4cMNXgXndDrldDr92isrKxssXkVFRaf+wKlHHTyog0cw61BZWWm7T6AP/rbiyJEjzerXnvcrk++Lpu4/ti5CsCxLubm52rBhgx588EElJCT4zO/bt69CQ0O1bds2b9v+/ft18ODBZv1FBQDouGwdAeXm5mrdunW69957FRkZ6f1eJyoqSuHh4YqKitLYsWOVl5en6OhoRUVFadGiRUpLSyOAAAA+bAXQW2+9JUmaM2eOT/v06dM1ZswYSdKUKVPkcDi0YMECHT9+3PtDVAAAfs5WAOXn5590mfDwcGVnZxM6QCPqf7pgx7BhwwK2R0VFSZIyMzO9P42od/nll9sfnJr3pX1SUlKztmXX4cOHG5wXGxsbcP6hQ4dackhoJu4FBwAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACOa/UhuoC1LT09vVr+QEPt/k9111122+zRnfJmZmY3Of+2112yv07RvvvnGdp8//vGPAdsjIyO1ePFi3XXXXX5PQP3444+bNT60LI6AAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIbkaKZgsNDbXdJzEx0XafuXPnBmx3Op2SpKefflo1NTU+837zm9/Y3o7UvJuRdkQ//PCD7T4PPPCA7T5Lly613aeioiJgu9vt1uLFi/Xyyy83uAzaFt5tAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARhBAAAAjCCAAgBEEEADACAIIAGAENyNFs7ndbtt9Jk2aZLvP6aefHrC9/maoiYmJqq2t9ZnX1m8qWlxcbLvPE088EbA9IiJCDz/8sB544AFVV1f7zDuxLk31l7/8pVn9ADva9rsUANBhEUAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAMq40aMmSIJcnnn9vttizLstxut9+8zvSPOlAH6kAd2nIdhgwZ0qTPeY6AAABGEEAAACNsPQ9oxYoV2rBhg4qLixUeHq60tDTdeOONSkxM9C4zZ84cFRUV+fQbN26cbr311uCMGADQIdgKoKKiIl188cXq16+famtrtXTpUj388MN6/PHHddppp3mXy8rK8nnwWHh4ePBGDADoEGwF0KxZs3ymZ8yYoezsbO3atUu//OUvve0RERGKjY0NygABAB3TKT2Su6qqSpIUHR3t015YWKjCwkLFxsZq2LBhuvrqqxURERFwHTU1NaqpqfFOOxwORUZGyuVy+T3yuX66OY+C7kiogwd18KAOHtTBoy3UweVyNWk5h2VZVnM2UFdXp0cffVSVlZV66KGHvO3vvPOO4uPj1bVrV3333Xd68cUXlZqaqnvuuSfgevLz81VQUOCd7tOnj+bPn9+cIQEA2pFmB9Czzz6rLVu2aO7cuerWrVuDy23fvl1z587Vf/3Xf6lHjx5+8xs6Aho1apS2bt3qs6zb7VZxcbGSkpJUUVHRnGF3CNTBgzp4UAcP6uDRFuqQkZGhwsLCky7XrFNwubm52rRpk3JychoNH0lKTU2VJJWUlAQMIKfTKafT6ddeWVnZYPEqKio69Q5Wjzp4UAcP6uBBHTxM1qGysrJJy9n6HZBlWcrNzdWGDRv04IMPKiEh4aR99uzZI0mKi4uzsykAQAdn6wgoNzdX69at07333qvIyEiVlZVJkqKiohQeHq6SkhKtW7dOQ4cOVXR0tPbu3aslS5bozDPPVHJyckuMHwDQTtkKoLfeekuS58emPzd9+nSNGTNGYWFh2rZtm9asWaPq6mp169ZNI0eO1FVXXRW0AQMAOgZbAZSfn9/o/Pj4eOXk5JzSgAAAnQP3ggMAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGBFmegANSU9P92tzuVySpIyMDFVWVrb2kNoM6uBBHTyogwd18GgLdQj0+R2Iw7Isq4XHAgCAn3Z1Cu7IkSO67777dOTIEdNDMYo6eFAHD+rgQR082lMd2lUAWZal3bt3q7MftFEHD+rgQR08qINHe6pDuwogAEDHQQABAIxoVwHkdDp1zTXXyOl0mh6KUdTBgzp4UAcP6uDRnurAVXAAACPa1REQAKDjIIAAAEYQQAAAIwggAIARBBAAwIg2ezPSE73xxhtavXq1ysrKlJycrJtvvlmpqammh9Wq8vPzVVBQ4NOWmJiohQsXmhlQKykqKtKqVau0e/dulZaW6p577tGIESO88y3LUn5+vt59911VVlYqPT1d2dnZ6tmzp8FRB9/J6vDUU09p7dq1Pn0yMjI0a9as1h5qi1mxYoU2bNig4uJihYeHKy0tTTfeeKMSExO9yxw7dkx5eXlav369ampqlJGRoezsbMXGxpobeJA1pQ5z5sxRUVGRT79x48bp1ltvbe3hNqhdBND69euVl5enqVOn6owzztBrr72mefPmaeHCherSpYvp4bWqXr166YEHHvBOh4R0/IPY6upqpaSkaOzYsXrsscf85q9cuVKvv/66ZsyYoYSEBP3973/XvHnz9Pjjjys8PNzAiFvGyeogSYMHD9b06dO902Fh7eIt3mRFRUW6+OKL1a9fP9XW1mrp0qV6+OGH9fjjj+u0006TJC1ZskSbNm3S3XffraioKOXm5mrBggV66KGHDI8+eJpSB0nKysrSpEmTvNNt7f3QLvbOV199VVlZWbrwwgslSVOnTtWmTZv0/vvva+LEiWYH18pCQkI61F9yTTFkyBANGTIk4DzLsrRmzRpdddVVOvvssyVJt99+u6ZOnaqNGzfq/PPPb82htqjG6lAvLCysQ+8fJx7NzZgxQ9nZ2dq1a5d++ctfqqqqSu+9957+4z/+QwMGDJAkTZ8+XTNnztTOnTuVlpZmYthBd7I61IuIiGjT+0ObD6Djx49r165dPkETEhKigQMHaufOneYGZkhJSYluu+02OZ1OpaWlafLkyYqPjzc9LGMOHDigsrIyDRo0yNsWFRWl1NRU7dy5s0MFUFMUFRUpOztbLpdLAwYM0PXXXy+32216WC2mqqpKkhQdHS1J2rVrl2prazVw4EDvMklJSYqPj+9QAXSiE+tQr7CwUIWFhYqNjdWwYcN09dVXKyIiwsQQA2rzAVReXq66ujq/FI+NjdX+/fvNDMqQM844Q9OnT1diYqJKS0tVUFCgBx98UAsWLFBkZKTp4RlRVlYmSX6nYrt06eKd11kMHjxYI0eOVEJCgkpKSrR06VL96U9/0rx58zrkqdq6ujotXrxY/fv3V+/evSV59oewsDDvQ9nqdeT9IVAdJCkzM1Px8fHq2rWrvvvuO7344ovav3+/7rnnHoOj9dXmAwj/7+enX5KTk72B9NFHH2ns2LEGR4a24OdHe71791ZycrLuuOMO7dixw+eIoKPIzc3Vvn37NHfuXNNDMaqhOowbN877371791ZcXJzmzp2rkpIS9ejRo7WHGVCb/7MoJiZGISEhfn+9lJWVtelzm63B5XIpMTFRJSUlpodiTP0+cPjwYZ/2w4cPd/r9o3v37nK73R1y/8jNzdWmTZs0e/ZsdevWzdseGxur48eP+z2KuqPuDw3VIZD6q4bb0v7Q5gMoLCxMffv21fbt271tdXV12r59e4c9n9tUR48eVUlJSYd8YzVVQkKCYmNjtW3bNm9bVVWVvvnmm06/fxw6dEg//fST4uLiTA8laCzLUm5urjZs2KAHH3xQCQkJPvP79u2r0NBQn/1h//79OnjwYIfaH05Wh0D27NkjSW1qf2gXp+AmTJigp556Sn379lVqaqrWrFmj6upqjRkzxvTQWlVeXp6GDx+u+Ph4lZaWKj8/XyEhIcrMzDQ9tBZVH7T1Dhw4oD179ig6Olrx8fG69NJL9fLLL6tnz55KSEjQsmXLFBcX570qrqNorA7R0dFavny5Ro4cqdjYWH3//fd64YUX1KNHD2VkZBgcdXDl5uZq3bp1uvfeexUZGek9MxIVFaXw8HBFRUVp7NixysvLU3R0tKKiorRo0SKlpaV1qAA6WR1KSkq0bt06DR06VNHR0dq7d6+WLFmiM888U8nJyWYH/zPt5nEMb7zxhlatWqWysjKlpKTopptu0hlnnGF6WK1q4cKF+uKLL1RRUaGYmBilp6fr+uuvbzPnc1vKjh07lJOT49c+evRozZgxw/tD1HfeeUdVVVVKT0/XLbfc4vOjvI6gsTpMnTpVf/7zn7V7925VVlaqa9euGjRokCZNmtShjpCvu+66gO3Tp0/3/kFa/0PUf/zjHzp+/HiH/CHqyepw8OBB/fWvf9W+fftUXV2tbt26acSIEbrqqqsUFRXVyqNtWLsJIABAx9LmvwMCAHRMBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgxP8BpefGTkC9qKcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'model_name': 'LeNet5', 'model_loss': 0.0011037873337045312, 'model_acc': 3.1749201277955272}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def eval_model(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               accuracy_fn,\n",
        "               device: torch.device = device):\n",
        "    loss, acc = 0, 0\n",
        "    model.eval()\n",
        "    with torch.inference_mode():\n",
        "        # Initialize a counter\n",
        "        counter = 0\n",
        "        # Set up the figure for a 3x3 grid\n",
        "        fig, axes = plt.subplots(3, 3, figsize=(10, 10))\n",
        "        fig.subplots_adjust(hspace=0.5, wspace=0.5)\n",
        "        # Flatten the axes array for easy indexing\n",
        "        axes = axes.flatten()\n",
        "        for X, y in tqdm(data_loader):\n",
        "            # Make our data device agnostic\n",
        "            X, y = X.to(device), y.to(device)\n",
        "            # Make predictions\n",
        "            y_pred = model(X)\n",
        "\n",
        "            # Visualize the images in a 3x3 grid with their predictions\n",
        "            if counter < 9:  # Check if the counter is less than 9\n",
        "                ax = axes[counter]\n",
        "                ax.imshow(X[0].cpu().squeeze(), cmap='gray')\n",
        "                ax.axis('off')  # Turn off the axis\n",
        "                ax.set_title(f\"Pred: {class_names[y_pred[0].argmax(dim=0).item()]}\", fontsize=10)\n",
        "                counter += 1  # Increment the counter\n",
        "\n",
        "            # Accumulate the loss and acc values per batch\n",
        "            loss += loss_fn(y_pred, y)\n",
        "            acc += accuracy_fn(y_true=y,\n",
        "                               y_pred=y_pred.argmax(dim=1))\n",
        "\n",
        "            # Break the loop if 9 images have been visualized\n",
        "            if counter >= 9:\n",
        "                break\n",
        "\n",
        "        # Show the plot\n",
        "        plt.show()\n",
        "\n",
        "        # Scale loss and acc to find the average loss/acc per batch\n",
        "        loss /= len(data_loader)\n",
        "        acc /= len(data_loader)\n",
        "\n",
        "    return {\"model_name\": model.__class__.__name__,\n",
        "            \"model_loss\": loss.item(),\n",
        "            \"model_acc\": acc}\n",
        "\n",
        "# Example usage\n",
        "model_1_results = eval_model(model=model,\n",
        "                             data_loader=test_dataloader,\n",
        "                             loss_fn=loss_fn,\n",
        "                             accuracy_fn=accuracy_fn)\n",
        "print(model_1_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 875,
          "referenced_widgets": [
            "461459a7b8e64c2a8df05a00a5b0a6ff",
            "78414da7492a47f8893fcad07bc4d77c",
            "8e80ffa88c804bf48cb2f988469fbf03",
            "c4983cb3713b41b784c30e54677ebe30",
            "2067f58e523447e8b6d8894c2ff86c19",
            "c076bd9036d64a909c3001939e17fd29",
            "e8e2e374597b415a9bfec50e0b382965",
            "63319a4942df4fd19b234d95ec86bdc4",
            "bfe3cb7a2c0646b5a728b67c972d598f",
            "4de6d5c736114adba8ad7b55e801c0b5",
            "9d5e383a92e3485eb10e2fc4155ccf61"
          ]
        },
        "id": "HTudl5hO9ely",
        "outputId": "43595eae-1274-4115-da5c-df6577c58647"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "461459a7b8e64c2a8df05a00a5b0a6ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'model_name': 'LeNet5', 'model_loss': 0.0010499318595975637, 'model_acc': 2.8554313099041533}\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "ad9c1bafce7741c6b7e6d599a6f65c7d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_80095e77788848bb881c1a4d1b93bb34",
              "IPY_MODEL_8da273fc00c14731857eb25abdce4631",
              "IPY_MODEL_603af0a7c5bd479b91c018857cfd23e1"
            ],
            "layout": "IPY_MODEL_9ff520b9520348aabb777419f087115d"
          }
        },
        "80095e77788848bb881c1a4d1b93bb34": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_911b7416277847539559829e97fdb6f2",
            "placeholder": "​",
            "style": "IPY_MODEL_fd5c0a5ec1a940aea4e3acb927865a20",
            "value": "  3%"
          }
        },
        "8da273fc00c14731857eb25abdce4631": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "danger",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cde171b59b814d838e41a322f3095dd3",
            "max": 313,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_88315b44b17a405596c711d38f6c824e",
            "value": 9
          }
        },
        "603af0a7c5bd479b91c018857cfd23e1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_537d4387c91344948350c1122e74475e",
            "placeholder": "​",
            "style": "IPY_MODEL_a1b042956fdf4d6ba385480ee8c50ca8",
            "value": " 9/313 [00:04&lt;02:25,  2.09it/s]"
          }
        },
        "9ff520b9520348aabb777419f087115d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "911b7416277847539559829e97fdb6f2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "fd5c0a5ec1a940aea4e3acb927865a20": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "cde171b59b814d838e41a322f3095dd3": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "88315b44b17a405596c711d38f6c824e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "537d4387c91344948350c1122e74475e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a1b042956fdf4d6ba385480ee8c50ca8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "461459a7b8e64c2a8df05a00a5b0a6ff": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_78414da7492a47f8893fcad07bc4d77c",
              "IPY_MODEL_8e80ffa88c804bf48cb2f988469fbf03",
              "IPY_MODEL_c4983cb3713b41b784c30e54677ebe30"
            ],
            "layout": "IPY_MODEL_2067f58e523447e8b6d8894c2ff86c19"
          }
        },
        "78414da7492a47f8893fcad07bc4d77c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c076bd9036d64a909c3001939e17fd29",
            "placeholder": "​",
            "style": "IPY_MODEL_e8e2e374597b415a9bfec50e0b382965",
            "value": "  3%"
          }
        },
        "8e80ffa88c804bf48cb2f988469fbf03": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "danger",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_63319a4942df4fd19b234d95ec86bdc4",
            "max": 313,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_bfe3cb7a2c0646b5a728b67c972d598f",
            "value": 8
          }
        },
        "c4983cb3713b41b784c30e54677ebe30": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4de6d5c736114adba8ad7b55e801c0b5",
            "placeholder": "​",
            "style": "IPY_MODEL_9d5e383a92e3485eb10e2fc4155ccf61",
            "value": " 8/313 [00:00&lt;00:11, 26.67it/s]"
          }
        },
        "2067f58e523447e8b6d8894c2ff86c19": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c076bd9036d64a909c3001939e17fd29": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e8e2e374597b415a9bfec50e0b382965": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "63319a4942df4fd19b234d95ec86bdc4": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bfe3cb7a2c0646b5a728b67c972d598f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "4de6d5c736114adba8ad7b55e801c0b5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9d5e383a92e3485eb10e2fc4155ccf61": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}