Spatial_Transformer_Network.ipynb

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        "<a href=\"https://colab.research.google.com/github/rajesh-bhat/data-ai-summit-2020/blob/main/Spatial_Transformer_Network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "\n",
        "Spatial Transformer Networks \n",
        "============================\n",
        "\n",
        "Paper link: https://arxiv.org/abs/1506.02025\n",
        "\n",
        "Spatial Transformer Networks are a generalization of differentiable\n",
        "attention to any spatial transformation. Spatial transformer networks\n",
        "(STN for short) allow a neural network to learn how to perform spatial\n",
        "transformations on the input image in order to enhance the geometric\n",
        "invariance of the model.\n",
        "\n",
        "It can crop a region of interest, scale and correct the orientation of an image. It can be a useful mechanism because CNNs\n",
        "are not invariant to rotation and scale and more general affine\n",
        "transformations.\n",
        "\n",
        "One of the best things about STN is the ability to simply plug it into\n",
        "any existing CNN with very little modification."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXVHpA1SNktY"
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      "source": [
        "from __future__ import print_function\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "plt.ion()   # interactive mode\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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        "id": "vndfrVLkNktY"
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      "source": [
        "Loading the data\n",
        "----------------\n",
        "\n",
        "Dataset: MNIST. \n",
        "\n",
        "**S**patial **T**ransformer **N**etwork is augmented with standard convolutional network.\n",
        "\n"
      ]
    },
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      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Training dataset\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST(root='.', \n",
        "                   train=True, \n",
        "                   download=True,\n",
        "                   transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                   ])), \n",
        "                   batch_size=64, \n",
        "                   shuffle=True, \n",
        "                   num_workers=4)\n",
        "\n",
        "# Test dataset\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST(root='.', \n",
        "                   train=False, \n",
        "                   transform=transforms.Compose([\n",
        "                        transforms.ToTensor(),\n",
        "                        transforms.Normalize((0.1307,), (0.3081,))])\n",
        "                   ), \n",
        "                   batch_size=64, \n",
        "                   shuffle=True, \n",
        "                   num_workers=4)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
          "text": [
            "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "model_id": "02e93f60fbd24f9faba2a4516237e6f7",
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        {
          "output_type": "stream",
          "text": [
            "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:480: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VnsyRk3rNktY"
      },
      "source": [
        "Depicting spatial transformer networks\n",
        "--------------------------------------\n",
        "\n",
        "Spatial transformer networks boils down to three main components :\n",
        "\n",
        "-  The localization network is a regular CNN which regresses the\n",
        "   transformation parameters. The transformation is never learned\n",
        "   explicitly from this dataset, instead the network learns automatically\n",
        "   the spatial transformations that enhances the global accuracy.\n",
        "\n",
        "-  The grid generator generates a grid of coordinates in the input\n",
        "   image corresponding to each pixel from the output image.\n",
        "   \n",
        "-  The sampler uses the parameters of the transformation and applies\n",
        "   it to the input image.\n",
        "\n",
        "\n",
        "Note::\n",
        "   We need the latest version of PyTorch that contains\n",
        "   affine_grid and grid_sample modules.\n",
        "\n",
        "\n"
      ]
    },
    {
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      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
        "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
        "        self.conv2_drop = nn.Dropout2d()\n",
        "        self.fc1 = nn.Linear(320, 50)\n",
        "        self.fc2 = nn.Linear(50, 10)\n",
        "\n",
        "        # Spatial transformer localization-network\n",
        "        self.localization = nn.Sequential(\n",
        "            nn.Conv2d(1, 8, kernel_size=7),\n",
        "            nn.MaxPool2d(2, stride=2),\n",
        "            nn.ReLU(True),\n",
        "            nn.Conv2d(8, 10, kernel_size=5),\n",
        "            nn.MaxPool2d(2, stride=2),\n",
        "            nn.ReLU(True)\n",
        "        )\n",
        "\n",
        "        # Regressor for the 3 * 2 affine matrix\n",
        "        self.fc_loc = nn.Sequential(\n",
        "            nn.Linear(10 * 3 * 3, 32),\n",
        "            nn.ReLU(True),\n",
        "            nn.Linear(32, 3 * 2)\n",
        "        )\n",
        "\n",
        "        # Initialize the weights/bias with identity transformation\n",
        "        self.fc_loc[2].weight.data.zero_()\n",
        "        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))\n",
        "\n",
        "    # Spatial transformer network forward function\n",
        "    def stn(self, x):\n",
        "\n",
        "        xs = self.localization(x)\n",
        "        xs = xs.view(-1, 10 * 3 * 3)\n",
        "        theta = self.fc_loc(xs)\n",
        "        theta = theta.view(-1, 2, 3)\n",
        "\n",
        "        grid = F.affine_grid(theta, x.size())\n",
        "        x = F.grid_sample(x, grid)\n",
        "\n",
        "        return x\n",
        "\n",
        "    def forward(self, x):\n",
        "        \n",
        "        # transform the input\n",
        "        x = self.stn(x)\n",
        "\n",
        "        # Perform the usual forward pass\n",
        "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
        "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
        "        x = x.view(-1, 320)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.dropout(x, training=self.training)\n",
        "        x = self.fc2(x)\n",
        "        return F.log_softmax(x, dim=1)\n",
        "\n",
        "\n",
        "model = Net().to(device)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "og88xmphNktY"
      },
      "source": [
        "Training the model\n",
        "------------------\n",
        "\n",
        "Now, let's use the SGD algorithm to train the model. The network is\n",
        "learning the classification task in a supervised way. In the same time\n",
        "the model is learning STN automatically in an end-to-end fashion.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8McejPO7NktY"
      },
      "source": [
        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "def train(epoch):\n",
        "\n",
        "    model.train()\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "\n",
        "        data, target = data.to(device), target.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "\n",
        "        loss = F.nll_loss(output, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if batch_idx % 500 == 0:\n",
        "\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, \n",
        "                batch_idx * len(data), \n",
        "                len(train_loader.dataset),\n",
        "                100. * batch_idx / len(train_loader), \n",
        "                loss.item()))\n",
        "            \n",
        "\n",
        "def test():\n",
        "\n",
        "    with torch.no_grad():\n",
        "\n",
        "        model.eval()\n",
        "        test_loss = 0\n",
        "        correct = 0\n",
        "\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "\n",
        "            # sum up batch loss\n",
        "            test_loss += F.nll_loss(output, target, size_average=False).item()\n",
        "\n",
        "            # get the index of the max log-probability\n",
        "            pred = output.max(1, keepdim=True)[1]\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "        test_loss /= len(test_loader.dataset)\n",
        "\n",
        "        print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'\n",
        "              .format(test_loss, correct, len(test_loader.dataset),\n",
        "                      100. * correct / len(test_loader.dataset)))"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x1uyszJMNktY"
      },
      "source": [
        "Visualizing the STN results\n",
        "---------------------------\n",
        "\n",
        "Now, we will inspect the results of our learned visual attention\n",
        "mechanism.\n",
        "\n",
        "We define a small helper function in order to visualize the\n",
        "transformations while training.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HberUfJqNktY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da197f0d-b1fc-4523-9d91-acf7f7e0ab5d"
      },
      "source": [
        "def convert_image_np(inp):\n",
        "    \"\"\"Convert a Tensor to numpy image.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    return inp\n",
        "\n",
        "# We want to visualize the output of the spatial transformers layer\n",
        "# after the training, we visualize a batch of input images and\n",
        "# the corresponding transformed batch using STN.\n",
        "\n",
        "\n",
        "def visualize_stn():\n",
        "    with torch.no_grad():\n",
        "        # Get a batch of training data\n",
        "        data = next(iter(test_loader))[0].to(device)\n",
        "\n",
        "        input_tensor = data.cpu()\n",
        "        transformed_input_tensor = model.stn(data).cpu()\n",
        "\n",
        "        in_grid = convert_image_np(\n",
        "            torchvision.utils.make_grid(input_tensor))\n",
        "\n",
        "        out_grid = convert_image_np(\n",
        "            torchvision.utils.make_grid(transformed_input_tensor))\n",
        "\n",
        "        # Plot the results side-by-side\n",
        "        f, axarr = plt.subplots(1, 2)\n",
        "        axarr[0].imshow(in_grid)\n",
        "        axarr[0].set_title('Dataset Images')\n",
        "\n",
        "        axarr[1].imshow(out_grid)\n",
        "        axarr[1].set_title('Transformed Images')\n",
        "\n",
        "for epoch in range(1, 20 + 1):\n",
        "    train(epoch)\n",
        "    test()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3448: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
            "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3385: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
            "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.394568\n",
            "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 0.883609\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:44: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
            "  warnings.warn(warning.format(ret))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.1947, Accuracy: 9436/10000 (94%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.494130\n",
            "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 0.372085\n",
            "\n",
            "Test set: Average loss: 0.1284, Accuracy: 9627/10000 (96%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.355028\n",
            "Train Epoch: 3 [32000/60000 (53%)]\tLoss: 0.145589\n",
            "\n",
            "Test set: Average loss: 0.1309, Accuracy: 9606/10000 (96%)\n",
            "\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.332673\n",
            "Train Epoch: 4 [32000/60000 (53%)]\tLoss: 0.359551\n",
            "\n",
            "Test set: Average loss: 0.0755, Accuracy: 9762/10000 (98%)\n",
            "\n",
            "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.156891\n",
            "Train Epoch: 5 [32000/60000 (53%)]\tLoss: 0.242139\n",
            "\n",
            "Test set: Average loss: 0.0780, Accuracy: 9766/10000 (98%)\n",
            "\n",
            "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.175181\n",
            "Train Epoch: 6 [32000/60000 (53%)]\tLoss: 0.129067\n",
            "\n",
            "Test set: Average loss: 0.0700, Accuracy: 9778/10000 (98%)\n",
            "\n",
            "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.220498\n",
            "Train Epoch: 7 [32000/60000 (53%)]\tLoss: 0.140814\n",
            "\n",
            "Test set: Average loss: 0.0600, Accuracy: 9808/10000 (98%)\n",
            "\n",
            "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.086380\n",
            "Train Epoch: 8 [32000/60000 (53%)]\tLoss: 0.279432\n",
            "\n",
            "Test set: Average loss: 0.0545, Accuracy: 9829/10000 (98%)\n",
            "\n",
            "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.050502\n",
            "Train Epoch: 9 [32000/60000 (53%)]\tLoss: 0.074961\n",
            "\n",
            "Test set: Average loss: 0.0539, Accuracy: 9839/10000 (98%)\n",
            "\n",
            "Train Epoch: 10 [0/60000 (0%)]\tLoss: 0.181364\n",
            "Train Epoch: 10 [32000/60000 (53%)]\tLoss: 0.177498\n",
            "\n",
            "Test set: Average loss: 0.0479, Accuracy: 9839/10000 (98%)\n",
            "\n",
            "Train Epoch: 11 [0/60000 (0%)]\tLoss: 0.045086\n",
            "Train Epoch: 11 [32000/60000 (53%)]\tLoss: 0.192149\n",
            "\n",
            "Test set: Average loss: 0.0516, Accuracy: 9837/10000 (98%)\n",
            "\n",
            "Train Epoch: 12 [0/60000 (0%)]\tLoss: 0.069308\n",
            "Train Epoch: 12 [32000/60000 (53%)]\tLoss: 0.039538\n",
            "\n",
            "Test set: Average loss: 0.0428, Accuracy: 9868/10000 (99%)\n",
            "\n",
            "Train Epoch: 13 [0/60000 (0%)]\tLoss: 0.111726\n",
            "Train Epoch: 13 [32000/60000 (53%)]\tLoss: 0.076047\n",
            "\n",
            "Test set: Average loss: 0.0481, Accuracy: 9853/10000 (99%)\n",
            "\n",
            "Train Epoch: 14 [0/60000 (0%)]\tLoss: 0.100424\n",
            "Train Epoch: 14 [32000/60000 (53%)]\tLoss: 0.070628\n",
            "\n",
            "Test set: Average loss: 0.0458, Accuracy: 9869/10000 (99%)\n",
            "\n",
            "Train Epoch: 15 [0/60000 (0%)]\tLoss: 0.224777\n",
            "Train Epoch: 15 [32000/60000 (53%)]\tLoss: 0.260115\n",
            "\n",
            "Test set: Average loss: 0.0463, Accuracy: 9849/10000 (98%)\n",
            "\n",
            "Train Epoch: 16 [0/60000 (0%)]\tLoss: 0.087925\n",
            "Train Epoch: 16 [32000/60000 (53%)]\tLoss: 0.238637\n",
            "\n",
            "Test set: Average loss: 0.0484, Accuracy: 9857/10000 (99%)\n",
            "\n",
            "Train Epoch: 17 [0/60000 (0%)]\tLoss: 0.184325\n",
            "Train Epoch: 17 [32000/60000 (53%)]\tLoss: 0.230042\n",
            "\n",
            "Test set: Average loss: 0.0422, Accuracy: 9869/10000 (99%)\n",
            "\n",
            "Train Epoch: 18 [0/60000 (0%)]\tLoss: 0.108696\n",
            "Train Epoch: 18 [32000/60000 (53%)]\tLoss: 0.021353\n",
            "\n",
            "Test set: Average loss: 0.0399, Accuracy: 9875/10000 (99%)\n",
            "\n",
            "Train Epoch: 19 [0/60000 (0%)]\tLoss: 0.157331\n",
            "Train Epoch: 19 [32000/60000 (53%)]\tLoss: 0.132738\n",
            "\n",
            "Test set: Average loss: 0.0380, Accuracy: 9877/10000 (99%)\n",
            "\n",
            "Train Epoch: 20 [0/60000 (0%)]\tLoss: 0.202853\n",
            "Train Epoch: 20 [32000/60000 (53%)]\tLoss: 0.155560\n",
            "\n",
            "Test set: Average loss: 0.0373, Accuracy: 9891/10000 (99%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "loLAEChylSCs",
        "outputId": "1211d428-bffc-41a0-a094-629cfbf0856f"
      },
      "source": [
        "# Visualize the STN transformation on some input batch\n",
        "visualize_stn()\n",
        "\n",
        "plt.ioff()\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3448: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
            "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n",
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3385: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
            "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcSMBoZ3mo3f"
      },
      "source": [
        ""
      ],
      "execution_count": 6,
      "outputs": []
    }
  ]
}