ResNet_with_attention.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "metadata": {
        "id": "nkyU-uS7YpGf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd440e77-600b-4eda-9957-e8e802576b00"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/gdrive/MyDrive/train.zip"
      ],
      "metadata": {
        "id": "jbRC16nnchzW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e398e113-d8d1-4c50-85ad-8a52dea3fb8a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/gdrive/MyDrive/train.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "train_df = pd.read_csv(\"Train.csv\")\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KtYvX6fXvx0M",
        "outputId": "bf40230c-7f3d-4342-cef4-ac4a330c5a39"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Image_id  Label\n",
              "0      id_004wknd7qd.jpg  blast\n",
              "1  id_004wknd7qd_rgn.jpg  blast\n",
              "2      id_005sitfgr2.jpg  brown\n",
              "3  id_005sitfgr2_rgn.jpg  brown\n",
              "4      id_00stp9t6m6.jpg  blast"
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              "\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>id_004wknd7qd.jpg</td>\n",
              "      <td>blast</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_004wknd7qd_rgn.jpg</td>\n",
              "      <td>blast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_005sitfgr2.jpg</td>\n",
              "      <td>brown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_005sitfgr2_rgn.jpg</td>\n",
              "      <td>brown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_00stp9t6m6.jpg</td>\n",
              "      <td>blast</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a9e6962-fedb-4d4d-beff-b28a11b15444')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
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          },
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    {
      "cell_type": "code",
      "source": [
        "train_df.drop(axis = 0, index = [i for i in range(1,train_df.shape[0],2)], inplace = True)\n",
        "train_df = train_df.reset_index()\n",
        "train_df.drop('index',axis = 1, inplace = True)\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cZqRMSdB0XaQ",
        "outputId": "1c6759fd-a1a4-48c8-a241-32f307808d19"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Image_id    Label\n",
              "0  id_004wknd7qd.jpg    blast\n",
              "1  id_005sitfgr2.jpg    brown\n",
              "2  id_00stp9t6m6.jpg    blast\n",
              "3  id_012zxewnhx.jpg    blast\n",
              "4  id_0186qwq2at.jpg  healthy"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f9e424ba-fbde-4682-9828-85bd383c2bbd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_004wknd7qd.jpg</td>\n",
              "      <td>blast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_005sitfgr2.jpg</td>\n",
              "      <td>brown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_00stp9t6m6.jpg</td>\n",
              "      <td>blast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_012zxewnhx.jpg</td>\n",
              "      <td>blast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_0186qwq2at.jpg</td>\n",
              "      <td>healthy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9e424ba-fbde-4682-9828-85bd383c2bbd')\"\n",
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              "              style=\"display:none;\">\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f9e424ba-fbde-4682-9828-85bd383c2bbd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f9e424ba-fbde-4682-9828-85bd383c2bbd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "  "
            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['Label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19XtQ8hYaniv",
        "outputId": "872c4485-7d9e-4ef3-b12a-97db0780ab43"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "blast      1494\n",
              "brown       766\n",
              "healthy     410\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['Label_int'] = train_df['Label'].replace(['blast','brown','healthy'], [0,1,2])\n",
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "nEtIGT_kwoi5",
        "outputId": "234fe3c4-c8ab-4972-d720-86426cbf72bc"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Image_id    Label  Label_int\n",
              "0     id_004wknd7qd.jpg    blast          0\n",
              "1     id_005sitfgr2.jpg    brown          1\n",
              "2     id_00stp9t6m6.jpg    blast          0\n",
              "3     id_012zxewnhx.jpg    blast          0\n",
              "4     id_0186qwq2at.jpg  healthy          2\n",
              "...                 ...      ...        ...\n",
              "2665  id_zydzdp046u.jpg  healthy          2\n",
              "2666  id_zyoowbqcm3.jpg  healthy          2\n",
              "2667  id_zz6gzk7p97.jpg    brown          1\n",
              "2668  id_zz8ca2p67e.jpg    blast          0\n",
              "2669  id_zzt8y9q0x0.jpg    brown          1\n",
              "\n",
              "[2670 rows x 3 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Image_id</th>\n",
              "      <th>Label</th>\n",
              "      <th>Label_int</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_004wknd7qd.jpg</td>\n",
              "      <td>blast</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_005sitfgr2.jpg</td>\n",
              "      <td>brown</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_00stp9t6m6.jpg</td>\n",
              "      <td>blast</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_012zxewnhx.jpg</td>\n",
              "      <td>blast</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_0186qwq2at.jpg</td>\n",
              "      <td>healthy</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2665</th>\n",
              "      <td>id_zydzdp046u.jpg</td>\n",
              "      <td>healthy</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2666</th>\n",
              "      <td>id_zyoowbqcm3.jpg</td>\n",
              "      <td>healthy</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2667</th>\n",
              "      <td>id_zz6gzk7p97.jpg</td>\n",
              "      <td>brown</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2668</th>\n",
              "      <td>id_zz8ca2p67e.jpg</td>\n",
              "      <td>blast</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2669</th>\n",
              "      <td>id_zzt8y9q0x0.jpg</td>\n",
              "      <td>brown</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2670 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7b9950f-fbd2-4cb5-b498-b0af05614351')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c7b9950f-fbd2-4cb5-b498-b0af05614351 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c7b9950f-fbd2-4cb5-b498-b0af05614351');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(train_df['Image_id'],train_df['Label_int'], test_size=0.3, random_state=1234)\n"
      ],
      "metadata": {
        "id": "a8c5Qs4bv-ly"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import transforms\n",
        "\n",
        "train_transform = transforms.Compose([\n",
        "        transforms.RandomVerticalFlip(),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.RandomRotation(degrees=(0,180)),\n",
        "        transforms.Resize(224),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "\n",
        "    ])\n",
        "\n",
        "val_transform = transforms.Compose([\n",
        "        transforms.Resize(224),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "    ])"
      ],
      "metadata": {
        "id": "1ddzSqQnZlI1"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision.io import read_image\n",
        "import cv2\n",
        "from PIL import Image\n",
        "\n",
        "class Rice_Dataset(Dataset):\n",
        "\n",
        "    def __init__(self, images, labels, img_dir,transform):\n",
        "\n",
        "        self.labels = labels\n",
        "        self.img_dir = img_dir\n",
        "        self.images = images\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "\n",
        "        return len(self.images)\n",
        "\n",
        "    def __getitem__(self,idx):\n",
        "\n",
        "        img_path = os.path.join(self.img_dir, self.images[idx])\n",
        "        img = cv2.imread(img_path)\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "        img = Image.fromarray(img)\n",
        "        img = self.transform(img)\n",
        "        label = torch.tensor(self.labels[idx])\n",
        "\n",
        "        return img, label"
      ],
      "metadata": {
        "id": "bAsWz4ltY71U"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = Rice_Dataset(X_train.values, y_train.values, \"/content/train/\",transform = train_transform)\n",
        "valid_dataset = Rice_Dataset(X_val.values, y_val.values, \"/content/train/\",transform = val_transform)"
      ],
      "metadata": {
        "id": "PJt0kpVXeKmX"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=150, shuffle=True)\n",
        "valid_dataloader = DataLoader(valid_dataset, batch_size = 150, shuffle = False)"
      ],
      "metadata": {
        "id": "CUIeMGadetR_"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import torchvision\n",
        "train_features, train_labels = next(iter(train_dataloader))\n",
        "print(f\"Feature batch shape: {train_features.size()}\")\n",
        "print(f\"Labels batch shape: {train_labels.size()}\")\n",
        "\n",
        "# fig,axs = plt.subplots(3,3)\n",
        "# plt.subplots_adjust(wspace=0.5, hspace=0.4)\n",
        "# for i in range(3):\n",
        "\n",
        "#     for j in range(3):\n",
        "\n",
        "#         img = train_features[i + j + i*2].squeeze()\n",
        "#         label = train_labels[i + j + i*2 ]\n",
        "#         if label == 0:\n",
        "\n",
        "#             label = \"blast\"\n",
        "\n",
        "#         elif label == 1:\n",
        "\n",
        "#             label = \"brown\"\n",
        "\n",
        "#         else:\n",
        "\n",
        "#             label = \"healthy\"\n",
        "\n",
        "#         axs[i,j].imshow(img);\n",
        "#         axs[i,j].set_title(f\"{label}\");\n",
        "#         axs[i,j].axis('off')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3JknDXfe8_W",
        "outputId": "cd2e9b4e-72a3-4517-d3b4-913fff22ae98"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature batch shape: torch.Size([150, 3, 224, 224])\n",
            "Labels batch shape: torch.Size([150])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Building The Model**"
      ],
      "metadata": {
        "id": "djj_bcI3Pxsc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class Attn(nn.Module):\n",
        "    def __init__(self, in_features,up_factor):\n",
        "\n",
        "        super(Attn, self).__init__()\n",
        "        self.up_factor = up_factor\n",
        "        self.op = nn.Conv2d(in_channels=in_features, out_channels=1, kernel_size=1, padding=0, bias=False)\n",
        "\n",
        "    def forward(self, l, g):\n",
        "        N, C, W, H = l.size()\n",
        "        g = F.interpolate(g, scale_factor = self.up_factor, mode = 'bilinear', align_corners = False)\n",
        "        #print(l.size(),g.size())\n",
        "        c = self.op(l+g) # batch_sizex1xWxH\n",
        "\n",
        "        a = F.softmax(c.view(N,1,-1), dim=2).view(N,1,W,H)\n",
        "        g = torch.mul(a.expand_as(l), l)\n",
        "        g = g.view(N,C,-1).sum(dim=2) # batch_sizexC\n",
        "\n",
        "        return a, g\n"
      ],
      "metadata": {
        "id": "2bIQSrvH4ges"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ProjectorBlock(nn.Module):\n",
        "    def __init__(self, in_features, out_features):\n",
        "\n",
        "        super(ProjectorBlock, self).__init__()\n",
        "\n",
        "        self.op = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=1, padding=0, bias=False)\n",
        "\n",
        "    def forward(self, inputs):\n",
        "\n",
        "        return self.op(inputs)"
      ],
      "metadata": {
        "id": "uKiCZwCi_DbB"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torchvision.models as models\n",
        "import torch\n",
        "\n",
        "class Attn_ResNet34(nn.Module):\n",
        "\n",
        "    def __init__(self,num_classes):\n",
        "\n",
        "        super(Attn_ResNet34, self).__init__()\n",
        "\n",
        "        base_model = models.resnet34(weights = \"IMAGENET1K_V1\")\n",
        "\n",
        "        layers = list(base_model.children())\n",
        "\n",
        "        self.conv1 = nn.Sequential(*layers[0:3])\n",
        "\n",
        "        self.conv2 = nn.Sequential(*layers[3:5])\n",
        "\n",
        "        self.conv3 = layers[5]\n",
        "\n",
        "        self.conv4 = layers[6]\n",
        "\n",
        "        self.conv5 = layers[7]\n",
        "\n",
        "        self.drp1 = nn.Dropout(0.3)\n",
        "        self.drp2 = nn.Dropout(0.4)\n",
        "\n",
        "        self.pool  = nn.AdaptiveAvgPool2d((1,1))\n",
        "\n",
        "        self.cls = nn.Linear(in_features = 512*3, out_features = num_classes, bias = True)\n",
        "\n",
        "        self.projector1 = ProjectorBlock(128,512)\n",
        "        self.projector2 = ProjectorBlock(256,512)\n",
        "\n",
        "        self.attn1 = Attn(512,4)\n",
        "        self.attn2 = Attn(512,2)\n",
        "\n",
        "    def forward(self,x):\n",
        "\n",
        "        x = self.conv1(x)\n",
        "        x = self.conv2(x)\n",
        "        l1 = self.conv3(x)\n",
        "        l2 = self.conv4(l1)\n",
        "        x = self.conv5(l2)\n",
        "\n",
        "        N, __, __, __ = x.size()\n",
        "        g = self.pool(x).view(N,512)\n",
        "\n",
        "        #print(x.size(),g.size(),l1.size(),l2.size())\n",
        "\n",
        "        a1,g1 = self.attn1(self.projector1(l1),x)\n",
        "        a2,g2 = self.attn2(self.projector2(l2),x)\n",
        "\n",
        "        g = torch.cat((g,g1,g2), dim = 1)\n",
        "\n",
        "        g = self.drp1(g)\n",
        "        g = self.drp2(g)\n",
        "\n",
        "        x = self.cls(g)\n",
        "\n",
        "        x = F.softmax(x,dim = 1)\n",
        "\n",
        "        return x,a1,a2\n"
      ],
      "metadata": {
        "id": "AmnK5fqT_L86"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = Attn_ResNet34(3)"
      ],
      "metadata": {
        "id": "6TpimP_mzdfg"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "import numpy as np\n",
        "\n",
        "class_labels = np.unique(y_train.values)\n",
        "class_weights = compute_class_weight(class_weight = 'balanced', classes = class_labels, y = y_train.values)\n",
        "class_weights = torch.from_numpy(class_weights).float()\n",
        "criterion = nn.CrossEntropyLoss(class_weights)\n",
        "optimizer = optim.Adam(x.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "MQqPhkemyFRs"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss = []\n",
        "val_loss = []\n",
        "best_loss = 1000\n",
        "\n",
        "for epoch in range(10):  # loop over the dataset multiple times\n",
        "\n",
        "    Average_train_loss_per_epoch = 0.0\n",
        "    Average_val_loss_per_epoch = 0.0\n",
        "\n",
        "    for i, data in enumerate(train_dataloader, 0):\n",
        "\n",
        "        running_train_loss_per_batch = 0.0\n",
        "        # get the inputs; data is a list of [inputs, labels]\n",
        "        inputs, labels = data\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        outputs,_,_ = x(inputs)\n",
        "\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # print statistics\n",
        "        running_train_loss_per_batch += loss.item()\n",
        "        Average_train_loss_per_epoch += running_train_loss_per_batch\n",
        "\n",
        "        print(f'[{epoch + 1}, {i+1:3d}] loss: {running_train_loss_per_batch}')\n",
        "\n",
        "    print(\"\\n\")\n",
        "    print(f'epoch[{epoch + 1}] loss: {Average_train_loss_per_epoch / len(train_dataloader)}')\n",
        "    train_loss.append(Average_train_loss_per_epoch / len(train_dataloader))\n",
        "    print(\"\\n\")\n",
        "\n",
        "    with torch.no_grad():\n",
        "\n",
        "        for i, test_data in enumerate(valid_dataloader,0):\n",
        "\n",
        "            running_val_loss_per_batch = 0.0\n",
        "\n",
        "            val_inputs,val_labels = test_data\n",
        "\n",
        "            y_val,_,_ = x(val_inputs)\n",
        "\n",
        "            loss = criterion(y_val,val_labels)\n",
        "\n",
        "            running_val_loss_per_batch+=loss.item()\n",
        "            Average_val_loss_per_epoch+=running_val_loss_per_batch\n",
        "\n",
        "    val_loss_per_epoch = Average_val_loss_per_epoch / len(valid_dataloader)\n",
        "    print(f'epoch[{epoch + 1}] val_loss: {val_loss_per_epoch}')\n",
        "    val_loss.append(val_loss_per_epoch)\n",
        "    if(val_loss_per_epoch < best_loss):\n",
        "\n",
        "      torch.save(x.state_dict(),\"./rice.h5\")\n",
        "      best_loss = val_loss_per_epoch\n",
        "\n",
        "\n",
        "print('Finished Training')"
      ],
      "metadata": {
        "id": "-4q-YYCHyK-7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71854c9d-4ddf-4fc9-fac2-741b69b4b15d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1,   1] loss: 1.114514708518982\n",
            "[1,   2] loss: 0.9591074585914612\n",
            "[1,   3] loss: 0.9233496189117432\n",
            "[1,   4] loss: 0.9070815443992615\n",
            "[1,   5] loss: 0.9386276006698608\n",
            "[1,   6] loss: 0.9133873581886292\n",
            "[1,   7] loss: 0.8661696314811707\n",
            "[1,   8] loss: 0.8296011686325073\n",
            "[1,   9] loss: 0.8638337254524231\n",
            "[1,  10] loss: 0.8666138052940369\n",
            "[1,  11] loss: 0.8495779037475586\n",
            "[1,  12] loss: 0.8339512944221497\n",
            "[1,  13] loss: 0.8634063005447388\n",
            "\n",
            "\n",
            "epoch[1] loss: 0.9022478552965018\n",
            "\n",
            "\n",
            "epoch[1] val_loss: 0.8578185737133026\n",
            "[2,   1] loss: 0.8160419464111328\n",
            "[2,   2] loss: 0.8405736088752747\n",
            "[2,   3] loss: 0.8351431488990784\n",
            "[2,   4] loss: 0.8364018797874451\n",
            "[2,   5] loss: 0.8166177272796631\n",
            "[2,   6] loss: 0.79070645570755\n",
            "[2,   7] loss: 0.8253620862960815\n",
            "[2,   8] loss: 0.8707500696182251\n",
            "[2,   9] loss: 0.8409362435340881\n",
            "[2,  10] loss: 0.789943277835846\n",
            "[2,  11] loss: 0.8319344520568848\n",
            "[2,  12] loss: 0.8544171452522278\n",
            "[2,  13] loss: 0.8549278378486633\n",
            "\n",
            "\n",
            "epoch[2] loss: 0.831058144569397\n",
            "\n",
            "\n",
            "epoch[2] val_loss: 0.8553231855233511\n",
            "[3,   1] loss: 0.8224384188652039\n",
            "[3,   2] loss: 0.8471508026123047\n",
            "[3,   3] loss: 0.8228552937507629\n",
            "[3,   4] loss: 0.7853908538818359\n",
            "[3,   5] loss: 0.852426290512085\n",
            "[3,   6] loss: 0.8009373545646667\n",
            "[3,   7] loss: 0.8557320833206177\n",
            "[3,   8] loss: 0.7999908924102783\n",
            "[3,   9] loss: 0.7958164215087891\n",
            "[3,  10] loss: 0.8711337447166443\n",
            "[3,  11] loss: 0.8533519506454468\n",
            "[3,  12] loss: 0.7734690308570862\n",
            "[3,  13] loss: 0.7933815121650696\n",
            "\n",
            "\n",
            "epoch[3] loss: 0.8210826653700608\n",
            "\n",
            "\n",
            "epoch[3] val_loss: 0.8267538646856943\n",
            "[4,   1] loss: 0.8119980096817017\n",
            "[4,   2] loss: 0.7627063393592834\n",
            "[4,   3] loss: 0.8135910630226135\n",
            "[4,   4] loss: 0.7913917303085327\n",
            "[4,   5] loss: 0.7242054343223572\n",
            "[4,   6] loss: 0.8454806208610535\n",
            "[4,   7] loss: 0.778749942779541\n",
            "[4,   8] loss: 0.7961368560791016\n",
            "[4,   9] loss: 0.7697954177856445\n",
            "[4,  10] loss: 0.7659100890159607\n",
            "[4,  11] loss: 0.7167152166366577\n",
            "[4,  12] loss: 0.7605142593383789\n",
            "[4,  13] loss: 0.823569655418396\n",
            "\n",
            "\n",
            "epoch[4] loss: 0.7815972795853248\n",
            "\n",
            "\n",
            "epoch[4] val_loss: 0.85013148188591\n",
            "[5,   1] loss: 0.778266429901123\n",
            "[5,   2] loss: 0.801917314529419\n",
            "[5,   3] loss: 0.805355966091156\n",
            "[5,   4] loss: 0.7570527791976929\n",
            "[5,   5] loss: 0.7893005609512329\n",
            "[5,   6] loss: 0.7455057501792908\n",
            "[5,   7] loss: 0.76971036195755\n",
            "[5,   8] loss: 0.7521893978118896\n",
            "[5,   9] loss: 0.7404734492301941\n",
            "[5,  10] loss: 0.7463524341583252\n",
            "[5,  11] loss: 0.7751957774162292\n",
            "[5,  12] loss: 0.7508025765419006\n",
            "[5,  13] loss: 0.7702805995941162\n",
            "\n",
            "\n",
            "epoch[5] loss: 0.7678771844277015\n",
            "\n",
            "\n",
            "epoch[5] val_loss: 0.8152701556682587\n",
            "[6,   1] loss: 0.7402180433273315\n",
            "[6,   2] loss: 0.7418918609619141\n",
            "[6,   3] loss: 0.7731114029884338\n",
            "[6,   4] loss: 0.7060949206352234\n",
            "[6,   5] loss: 0.8284099102020264\n",
            "[6,   6] loss: 0.7833095192909241\n",
            "[6,   7] loss: 0.8099137544631958\n",
            "[6,   8] loss: 0.7218989133834839\n",
            "[6,   9] loss: 0.7267746925354004\n",
            "[6,  10] loss: 0.7933094501495361\n",
            "[6,  11] loss: 0.7675542831420898\n",
            "[6,  12] loss: 0.8257777690887451\n",
            "[6,  13] loss: 0.7370949387550354\n",
            "\n",
            "\n",
            "epoch[6] loss: 0.7657968814556415\n",
            "\n",
            "\n",
            "epoch[6] val_loss: 0.8084097802639008\n",
            "[7,   1] loss: 0.7960699796676636\n",
            "[7,   2] loss: 0.8370940089225769\n",
            "[7,   3] loss: 0.805744469165802\n",
            "[7,   4] loss: 0.7808565497398376\n",
            "[7,   5] loss: 0.7961006164550781\n",
            "[7,   6] loss: 0.7446841597557068\n",
            "[7,   7] loss: 0.7440426349639893\n",
            "[7,   8] loss: 0.7255073189735413\n",
            "[7,   9] loss: 0.7717838883399963\n",
            "[7,  10] loss: 0.772132396697998\n",
            "[7,  11] loss: 0.7065931558609009\n",
            "[7,  12] loss: 0.7542265057563782\n",
            "[7,  13] loss: 0.7451278567314148\n",
            "\n",
            "\n",
            "epoch[7] loss: 0.7676895031562219\n",
            "\n",
            "\n",
            "epoch[7] val_loss: 0.8225474953651428\n",
            "[8,   1] loss: 0.772646427154541\n",
            "[8,   2] loss: 0.8251439332962036\n",
            "[8,   3] loss: 0.827462375164032\n",
            "[8,   4] loss: 0.7660529613494873\n",
            "[8,   5] loss: 0.7769931554794312\n",
            "[8,   6] loss: 0.7171875834465027\n",
            "[8,   7] loss: 0.7771958708763123\n",
            "[8,   8] loss: 0.8007844090461731\n",
            "[8,   9] loss: 0.7458038330078125\n",
            "[8,  10] loss: 0.878793478012085\n",
            "[8,  11] loss: 0.7511096596717834\n",
            "[8,  12] loss: 0.7559789419174194\n",
            "[8,  13] loss: 0.7603453993797302\n",
            "\n",
            "\n",
            "epoch[8] loss: 0.7811921559847318\n",
            "\n",
            "\n",
            "epoch[8] val_loss: 0.8125590880711874\n",
            "[9,   1] loss: 0.7890466451644897\n",
            "[9,   2] loss: 0.7668395042419434\n",
            "[9,   3] loss: 0.7672305107116699\n",
            "[9,   4] loss: 0.8534417152404785\n",
            "[9,   5] loss: 0.8165075182914734\n",
            "[9,   6] loss: 0.7318997383117676\n",
            "[9,   7] loss: 0.7200356721878052\n",
            "[9,   8] loss: 0.7574536800384521\n",
            "[9,   9] loss: 0.8121477961540222\n",
            "[9,  10] loss: 0.8424078822135925\n",
            "[9,  11] loss: 0.7248539924621582\n",
            "[9,  12] loss: 0.8011544942855835\n",
            "[9,  13] loss: 0.6860658526420593\n",
            "\n",
            "\n",
            "epoch[9] loss: 0.7745450001496536\n",
            "\n",
            "\n",
            "epoch[9] val_loss: 0.8186996777852377\n",
            "[10,   1] loss: 0.6938521862030029\n",
            "[10,   2] loss: 0.7714001536369324\n",
            "[10,   3] loss: 0.8007669448852539\n",
            "[10,   4] loss: 0.7480502128601074\n",
            "[10,   5] loss: 0.7666171789169312\n",
            "[10,   6] loss: 0.8004138469696045\n",
            "[10,   7] loss: 0.7879801988601685\n",
            "[10,   8] loss: 0.7001146078109741\n",
            "[10,   9] loss: 0.7205847501754761\n",
            "[10,  10] loss: 0.7452441453933716\n",
            "[10,  11] loss: 0.741134762763977\n",
            "[10,  12] loss: 0.8193390369415283\n",
            "[10,  13] loss: 0.8857045769691467\n",
            "\n",
            "\n",
            "epoch[10] loss: 0.7677848155681903\n",
            "\n",
            "\n",
            "epoch[10] val_loss: 0.7770067751407623\n",
            "Finished Training\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-pUSKrPcC2T7"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/gdrive/MyDrive/test.zip"
      ],
      "metadata": {
        "id": "WpE3ybdQN7zq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b612034-1888-4881-d61c-c8c8757d299d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_transform = transforms.Compose([\n",
        "        transforms.Resize(224),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])\n",
        "    ])"
      ],
      "metadata": {
        "id": "wN0ZAw_6Mf6C"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Rice_Dataset_Test(Dataset):\n",
        "\n",
        "    def __init__(self, images, img_dir,transform):\n",
        "\n",
        "        self.img_dir = img_dir\n",
        "        self.images = images\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "\n",
        "        return len(self.images)\n",
        "\n",
        "    def __getitem__(self,idx):\n",
        "\n",
        "        img_path = os.path.join(self.img_dir, self.images[idx])\n",
        "        img = cv2.imread(img_path)\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "        img = Image.fromarray(img)\n",
        "        img = self.transform(img)\n",
        "        image_name = self.images[idx]\n",
        "\n",
        "        return img,image_name\n"
      ],
      "metadata": {
        "id": "0hd9P5i9DBVI"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = pd.read_csv(\"Test.csv\")\n",
        "test_df"
      ],
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        "id": "qSyhLeQCyJcR",
        "colab": {
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          "height": 424
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        "outputId": "0fd4dc2e-1c2c-4574-8007-1b4d0ed9c684"
      },
      "execution_count": 34,
      "outputs": [
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              "2288      id_zyo7m4fj8h.jpg\n",
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              "      <td>id_00vl5wvxq3_rgn.jpg</td>\n",
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              "      <th>2</th>\n",
              "      <td>id_01hu05mtch.jpg</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-92dd59c5-541a-4883-9210-f028901171e2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.drop(axis = 0, index = [i for i in range(1,test_df.shape[0],2)], inplace = True)\n",
        "test_df.reset_index(inplace = True)\n",
        "test_df.drop('index',axis = 1, inplace = True)\n",
        "test_df"
      ],
      "metadata": {
        "id": "7W73pAoaM7V_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "00bc427c-4630-4739-bbb8-3eb49ff6be02"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Image_id\n",
              "0     id_00vl5wvxq3.jpg\n",
              "1     id_01hu05mtch.jpg\n",
              "2     id_030ln10ewn.jpg\n",
              "3     id_03z57m8xht.jpg\n",
              "4     id_04ngep1w4b.jpg\n",
              "...                 ...\n",
              "1140  id_zrdlgjrq3r.jpg\n",
              "1141  id_zsfayxwipp.jpg\n",
              "1142  id_ztvp2l9k3h.jpg\n",
              "1143  id_zwwcma7hlt.jpg\n",
              "1144  id_zyo7m4fj8h.jpg\n",
              "\n",
              "[1145 rows x 1 columns]"
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              "      <th>1</th>\n",
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              "      <td>id_03z57m8xht.jpg</td>\n",
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              "    <tr>\n",
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              "      <th>1140</th>\n",
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              "      <th>1141</th>\n",
              "      <td>id_zsfayxwipp.jpg</td>\n",
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              "      <th>1144</th>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9c84198-6fe9-4b94-8b6a-fc13e3be3bfc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9c84198-6fe9-4b94-8b6a-fc13e3be3bfc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = Rice_Dataset_Test(test_df['Image_id'].values,\"/content/test/\",transform = test_transform )"
      ],
      "metadata": {
        "id": "QWCWAB1bNfPg"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataloader = DataLoader(test_dataset, batch_size = 150, shuffle = False)"
      ],
      "metadata": {
        "id": "w2Z2cozpOOe6"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = Attn_ResNet34(3)\n",
        "x.load_state_dict(torch.load(PATH))\n",
        "x.eval()"
      ],
      "metadata": {
        "id": "XDMQ1oYJOdHh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28515dd4-7d24-429c-eae4-1680c31f62cc"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Attn_ResNet34(\n",
              "  (conv1): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "  )\n",
              "  (conv2): Sequential(\n",
              "    (0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "    (1): Sequential(\n",
              "      (0): BasicBlock(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicBlock(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (2): BasicBlock(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (conv3): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (2): BasicBlock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (3): BasicBlock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (conv4): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (2): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (3): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (4): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (5): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (conv5): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (2): BasicBlock(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (drp1): Dropout(p=0.3, inplace=False)\n",
              "  (drp2): Dropout(p=0.4, inplace=False)\n",
              "  (pool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (cls): Linear(in_features=1536, out_features=3, bias=True)\n",
              "  (projector1): ProjectorBlock(\n",
              "    (op): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "  )\n",
              "  (projector2): ProjectorBlock(\n",
              "    (op): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "  )\n",
              "  (attn1): Attn(\n",
              "    (op): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "  )\n",
              "  (attn2): Attn(\n",
              "    (op): Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = {'Image_id':[], 'blast':[], 'brown':[], 'healthy':[]}\n",
        "\n",
        "with torch.no_grad():\n",
        "\n",
        "    for data in test_dataloader:\n",
        "\n",
        "        images,names = data\n",
        "\n",
        "        outputs,_,_ = x(images)\n",
        "\n",
        "        _,predicted = torch.max(outputs,1)\n",
        "\n",
        "        for i in range(len(predicted)):\n",
        "\n",
        "            df['Image_id'].append(names[i])\n",
        "\n",
        "            if(predicted[i] == 0):\n",
        "\n",
        "                df['blast'].append(1)\n",
        "                df['brown'].append(0)\n",
        "                df['healthy'].append(0)\n",
        "\n",
        "            elif(predicted[i] == 1):\n",
        "\n",
        "                df['blast'].append(0)\n",
        "                df['brown'].append(1)\n",
        "                df['healthy'].append(0)\n",
        "\n",
        "            else:\n",
        "\n",
        "                df['blast'].append(0)\n",
        "                df['brown'].append(0)\n",
        "                df['healthy'].append(1)\n",
        "\n",
        "\n",
        "submission = pd.DataFrame(df)\n"
      ],
      "metadata": {
        "id": "bUVUOUCYPufl"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission"
      ],
      "metadata": {
        "id": "-NZO6a51fLYY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "b9814d8b-6e52-4b85-e5a8-df2480a4f349"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Image_id  blast  brown  healthy\n",
              "0     id_00vl5wvxq3.jpg      1      0        0\n",
              "1     id_01hu05mtch.jpg      0      1        0\n",
              "2     id_030ln10ewn.jpg      0      0        1\n",
              "3     id_03z57m8xht.jpg      1      0        0\n",
              "4     id_04ngep1w4b.jpg      0      0        1\n",
              "...                 ...    ...    ...      ...\n",
              "1140  id_zrdlgjrq3r.jpg      0      0        1\n",
              "1141  id_zsfayxwipp.jpg      0      1        0\n",
              "1142  id_ztvp2l9k3h.jpg      0      1        0\n",
              "1143  id_zwwcma7hlt.jpg      1      0        0\n",
              "1144  id_zyo7m4fj8h.jpg      1      0        0\n",
              "\n",
              "[1145 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-259f16b4-e2bf-4dfa-8b85-1fb939668886\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Image_id</th>\n",
              "      <th>blast</th>\n",
              "      <th>brown</th>\n",
              "      <th>healthy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_00vl5wvxq3.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_01hu05mtch.jpg</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_030ln10ewn.jpg</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_03z57m8xht.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_04ngep1w4b.jpg</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1140</th>\n",
              "      <td>id_zrdlgjrq3r.jpg</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1141</th>\n",
              "      <td>id_zsfayxwipp.jpg</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>id_ztvp2l9k3h.jpg</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>id_zwwcma7hlt.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1144</th>\n",
              "      <td>id_zyo7m4fj8h.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1145 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-259f16b4-e2bf-4dfa-8b85-1fb939668886')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-259f16b4-e2bf-4dfa-8b85-1fb939668886 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-259f16b4-e2bf-4dfa-8b85-1fb939668886');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "submission.to_csv('submission.csv', encoding = 'utf-8-sig')\n",
        "files.download('submission.csv')"
      ],
      "metadata": {
        "id": "4pq1nw9Rf6pj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "1a0792de-d09a-4911-9915-ea2eddc7cb1b"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_8ec49073-46c6-453d-85f9-00f738548448\", \"submission.csv\", 32128)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a3Euf73pJNXU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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}