ViTLarge.ipynb

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      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import clear_output\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "N425l5nnBbrb"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOc-aJOYBTiN",
        "outputId": "77428459-5bdd-422b-d033-dc56511ac9d7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Da8QEeWVBCMA"
      },
      "outputs": [],
      "source": [
        "!pip install tqdm \n",
        "!pip install wandb -qqq\n",
        "!pip install timm\n",
        "!pip install --upgrade --force-reinstall --no-deps kaggle\n",
        "!pip install einops\n",
        "clear_output()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U albumentations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYiDrQ61fQc0",
        "outputId": "74e77aec-a278-4a95-8fde-d44f5b10cc2b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: albumentations in /usr/local/lib/python3.7/dist-packages (1.1.0)\n",
            "Requirement already satisfied: opencv-python-headless>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations) (4.5.4.60)\n",
            "Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations) (0.18.3)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations) (3.13)\n",
            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations) (1.4.1)\n",
            "Requirement already satisfied: qudida>=0.0.4 in /usr/local/lib/python3.7/dist-packages (from albumentations) (0.0.4)\n",
            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from qudida>=0.0.4->albumentations) (1.0.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from qudida>=0.0.4->albumentations) (3.10.0.2)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations) (1.2.0)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations) (2.6.3)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations) (2.4.1)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations) (7.1.2)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations) (2021.11.2)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.16.1->albumentations) (3.2.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations) (1.3.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations) (3.0.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.16.1->albumentations) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (3.0.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/Bjarten/early-stopping-pytorch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpLRGLH8Bklp",
        "outputId": "9476726a-c93f-4855-f6e7-916fea09f6fa"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'early-stopping-pytorch' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.append('/content/early-stopping-pytorch')"
      ],
      "metadata": {
        "id": "bkqcnaIOBnR2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir ~/.kaggle\n",
        "! cp /content/drive/MyDrive/kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle datasets download schulta/petfinder-pawpularity-score-clean\n",
        "!unzip /content/petfinder-pawpularity-score-clean.zip -d /data/\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "y2FSB5ntBVpx"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import gc\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import wandb\n",
        "import cv2\n",
        "import timm\n",
        "from timm.models import load_checkpoint\n",
        "from tqdm.notebook import trange, tqdm\n",
        "\n",
        "from einops import rearrange, reduce, repeat\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset\n",
        "from torch.utils.data import DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.optim import AdamW, Adam\n",
        "import albumentations as A\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "from pytorchtools import EarlyStopping\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "from datetime import datetime"
      ],
      "metadata": {
        "id": "ZE6neot5CLQG"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Config = {\n",
        "    'CSV_PATH': \"/data/train.csv\",\n",
        "    'IMG_PATH': \"/data/train\",\n",
        "    'TEST_PATH': '/data/test',\n",
        "    'N_SPLITS': 5,\n",
        "    'TRAIN_BS': 16,\n",
        "    'VALID_BS': 16,\n",
        "    'N_EPOCHS': 10,\n",
        "    'NUM_WORKERS': 4,\n",
        "    'LR': 1e-5,\n",
        "    'ARCH': 'vit_large_patch16_224',\n",
        "    'OPTIM': \"AdamW\",\n",
        "    'SCH_STEP': 4,\n",
        "    'GAMMA': 0.1,\n",
        "    'SCHEDULER': 'CosineWarm',\n",
        "    \"T_0\": 20,\n",
        "    \"η_min\": 1e-6,\n",
        "    'PATIENCE': 3,\n",
        "    'LOSS': \"RMSE\",\n",
        "    'IMG_SIZE': 224,\n",
        "    'DEVICE': \"cuda\",\n",
        "    'infra': \"Colab\",\n",
        "    'competition': 'pawpularity',\n",
        "    '_wandb_kernel': 'tanaym',\n",
        "    \"wandb\": True,\n",
        "}"
      ],
      "metadata": {
        "id": "ezw37QcQCsYN"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class PawsDataset(Dataset):\n",
        "    def __init__(self, df, config=Config, augments=None, target_transform=None, is_test=False,is_val=False):\n",
        "        if type(df) == pd.core.frame.DataFrame:\n",
        "            self.df = df\n",
        "        else:\n",
        "            self.df = pd.read_csv(df)\n",
        "        self.config = config\n",
        "        self.transform = augments\n",
        "        self.target_transform = target_transform\n",
        "        self.is_test = is_test\n",
        "        self.is_val = is_val\n",
        "        \n",
        "        self.img_paths = self._get_img_paths(self.df, self.config)\n",
        "        self.meta_feats = self._get_meta_feats(self.df, self.is_test)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if torch.is_tensor(idx):\n",
        "            idx = idx.tolist()\n",
        "\n",
        "        img_name = self.img_paths[idx]\n",
        "        #image = read_image(img_name)\n",
        "        image = cv2.imread(img_name)\n",
        "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "        #image = rearrange(image, 'c x y -> x y c')\n",
        "        label = self.df.iloc[idx, -1]\n",
        "        label = np.array([label])\n",
        "        if self.transform:\n",
        "            image = self.transform(image=image)['image']\n",
        "        sample = {'image': image, 'label': label}\n",
        "        return sample\n",
        " \n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    def _get_img_paths(self, df, config):\n",
        "        \"\"\"\n",
        "        Returns the image paths in a list\n",
        "        \"\"\"\n",
        "        if self.is_val:\n",
        "            imgs = df['Id'].apply(lambda x: os.path.join(config['TEST_PATH'], x + \".jpg\")).tolist()\n",
        "        else:\n",
        "            imgs = df['Id'].apply(lambda x: os.path.join(config['IMG_PATH'], x + \".jpg\")).tolist()\n",
        "        return imgs\n",
        "    \n",
        "    def _get_meta_feats(self, df, is_test):\n",
        "        \"\"\"\n",
        "        Returns the meta features in a df\n",
        "        \"\"\"\n",
        "        if self.is_test or self.is_val:\n",
        "            meta = self.df.drop(['Id'], axis=1)\n",
        "            return meta\n",
        "        else:\n",
        "            meta = self.df.drop(['Id', 'Pawpularity'], axis=1)\n",
        "            return meta"
      ],
      "metadata": {
        "id": "nA0AKiVXDlHD"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GetDate():\n",
        "    def __init__(self):\n",
        "        self.currentMinute = datetime.now().minute\n",
        "        self.currentHour = datetime.now().hour\n",
        "        self.currentDay = datetime.now().day\n",
        "        self.currentMonth = datetime.now().month\n",
        "        self.currentYear = datetime.now().year"
      ],
      "metadata": {
        "id": "qE9YKlXCH8x4"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class RMSELoss(nn.Module):\n",
        "    def __init__(self, eps=1e-6):\n",
        "        super().__init__()\n",
        "        self.mse = nn.MSELoss()\n",
        "        self.eps = eps\n",
        "        \n",
        "    def forward(self,yhat,y):\n",
        "        loss = torch.sqrt(self.mse(yhat,y) + self.eps)\n",
        "        return loss"
      ],
      "metadata": {
        "id": "yZDEF7zBDr7D"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_loss_val(training_losses,valid_losses):\n",
        "    plt.figure(figsize=(10,5))\n",
        "    plt.title(\"Training and Validation Loss\")\n",
        "    plt.plot(training_losses,label=\"train\")\n",
        "    plt.plot(valid_losses,label=\"val\")\n",
        "    plt.xlabel(\"iterations\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "N5L0gP7gG9Hs"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "paws_dataset = PawsDataset(df=Config['CSV_PATH'],)\n",
        "paws_dataset.df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "uBjF2TFOejBR",
        "outputId": "34b54fc0-9517-44fb-ca41-e125a6dd490d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>Id</th>\n",
              "      <th>Subject Focus</th>\n",
              "      <th>Eyes</th>\n",
              "      <th>Face</th>\n",
              "      <th>Near</th>\n",
              "      <th>Action</th>\n",
              "      <th>Accessory</th>\n",
              "      <th>Group</th>\n",
              "      <th>Collage</th>\n",
              "      <th>Human</th>\n",
              "      <th>Occlusion</th>\n",
              "      <th>Info</th>\n",
              "      <th>Blur</th>\n",
              "      <th>Pawpularity</th>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9880</th>\n",
              "      <td>ffbfa0383c34dc513c95560d6e1fdb57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9881</th>\n",
              "      <td>ffcc8532d76436fc79e50eb2e5238e45</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9882</th>\n",
              "      <td>ffdf2e8673a1da6fb80342fa3b119a20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9883</th>\n",
              "      <td>fff19e2ce11718548fa1c5d039a5192a</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9884</th>\n",
              "      <td>fff8e47c766799c9e12f3cb3d66ad228</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9885 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Id  Subject Focus  ...  Blur  Pawpularity\n",
              "0     0007de18844b0dbbb5e1f607da0606e0              0  ...     0           63\n",
              "1     0009c66b9439883ba2750fb825e1d7db              0  ...     0           42\n",
              "2     0013fd999caf9a3efe1352ca1b0d937e              0  ...     0           28\n",
              "3     0018df346ac9c1d8413cfcc888ca8246              0  ...     0           15\n",
              "4     001dc955e10590d3ca4673f034feeef2              0  ...     0           72\n",
              "...                                ...            ...  ...   ...          ...\n",
              "9880  ffbfa0383c34dc513c95560d6e1fdb57              0  ...     1           15\n",
              "9881  ffcc8532d76436fc79e50eb2e5238e45              0  ...     0           70\n",
              "9882  ffdf2e8673a1da6fb80342fa3b119a20              0  ...     0           20\n",
              "9883  fff19e2ce11718548fa1c5d039a5192a              0  ...     0           20\n",
              "9884  fff8e47c766799c9e12f3cb3d66ad228              0  ...     0           30\n",
              "\n",
              "[9885 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_image(image):\n",
        "    \"\"\"Show image with landmarks\"\"\"\n",
        "    plt.imshow(image)\n",
        "\n",
        "    plt.pause(0.001)  # pause a bit so that plots are updated\n"
      ],
      "metadata": {
        "id": "ucNG7I7yepTH"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "\n",
        "for i in tqdm(range(5)):\n",
        "    sample = paws_dataset[i]\n",
        "    image = sample['image'] \n",
        "    print(f\"{i} {image.shape} Pawpularity:{sample['label']}\")\n",
        "\n",
        "    if image.shape[0] == 3:\n",
        "        image = rearrange(image,'c x y -> x y c')\n",
        "    ax = plt.subplot(1, 5, i + 1)\n",
        "    plt.tight_layout()\n",
        "    ax.set_title('Sample #{}'.format(i))\n",
        "    ax.axis('off')\n",
        "    \n",
        "    plot_image(image)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713,
          "referenced_widgets": [
            "dfecd4ac63cb43579aa062803f498fc1",
            "254fc3d8b8ef4a1ea77dc44ab14f060b",
            "54bd06b97d844b08a1041f51a754aa16",
            "1585be64a0a241fb8408ac3a18336426",
            "406c5397e2cb46db80449c0ebbfa6d73",
            "9743d7c4a6084709bef5be5d266ab63e",
            "24d5179e43b24217b6b6522b17dd3360",
            "1b3a1bea16b0496eb2b2df749d712c46",
            "500f167dba4a4e4eba8e094134da4e0f",
            "e2aa2d39cdc848fb803eca939372362b",
            "557844fb6fd84f678487187ef81a1988"
          ]
        },
        "id": "e8bGN2n1DuZO",
        "outputId": "3e11d168-c934-4184-cba4-88c838bfe811"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dfecd4ac63cb43579aa062803f498fc1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 (720, 405, 3) Pawpularity:[63]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 (774, 1032, 3) Pawpularity:[42]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 (960, 720, 3) Pawpularity:[28]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3 (720, 405, 3) Pawpularity:[15]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 (960, 540, 3) Pawpularity:[72]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_aug = A.Compose(\n",
        "    [\n",
        "        A.SmallestMaxSize(max_size=256),\n",
        "        A.Flip(p=0.5),\n",
        "        A.GaussNoise(p=0.5),\n",
        "        A.ShiftScaleRotate(shift_limit=0.05, scale_limit=0.05, rotate_limit=15, p=0.5),\n",
        "        A.RandomCrop(height=224, width=224),\n",
        "        A.RGBShift(r_shift_limit=10, g_shift_limit=10, b_shift_limit=10, p=0.1),\n",
        "        A.RandomBrightnessContrast(p=0.4),\n",
        "        A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n",
        "        ToTensorV2(),\n",
        "    ]\n",
        ")\n",
        "\n",
        "test_aug = A.Compose(\n",
        "    [\n",
        "        A.SmallestMaxSize(max_size=256),\n",
        "        A.CenterCrop(height=224, width=224),\n",
        "        A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n",
        "        A.pytorch.transforms.ToTensorV2(),\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "vdzXGKxRDz89"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = paws_dataset.df.iloc[:,:-1]\n",
        "y = paws_dataset.df['Pawpularity']\n",
        "rs = ShuffleSplit(n_splits=1, test_size=.2, random_state=42)\n",
        "rs.get_n_splits(X)\n",
        "\n",
        "print(rs)\n",
        "\n",
        "for train_index, test_index in rs.split(X,y):\n",
        "    print(\"TRAIN:\", train_index, \"TEST:\", test_index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T8d4MofCD4xK",
        "outputId": "5ff88470-a1bd-4558-ce91-38f04bd993b0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ShuffleSplit(n_splits=1, random_state=42, test_size=0.2, train_size=None)\n",
            "TRAIN: [6740 5123 8601 ... 5390  860 7270] TEST: [4170 5809 3023 ...  473 6537 5477]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_data = PawsDataset(df=paws_dataset.df.iloc[train_index],config=Config,augments=train_aug)\n",
        "\n",
        "test_data = PawsDataset(df=paws_dataset.df.iloc[test_index],config=Config,augments=test_aug,is_test=True)"
      ],
      "metadata": {
        "id": "EyDau_rGD8_Z"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainloader = DataLoader(training_data, batch_size=Config['TRAIN_BS'], shuffle=True)\n",
        "\n",
        "testloader = DataLoader(test_data,batch_size=Config['TRAIN_BS'],shuffle=True)"
      ],
      "metadata": {
        "id": "lDQJH_MmD-wD"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = Config['ARCH']\n",
        "model = timm.create_model(model_name, pretrained=True)\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VwVpnKUQEBKn",
        "outputId": "567f6327-6e14-415c-8bb3-97cbba7320db"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VisionTransformer(\n",
              "  (patch_embed): PatchEmbed(\n",
              "    (proj): Conv2d(3, 1024, kernel_size=(16, 16), stride=(16, 16))\n",
              "    (norm): Identity()\n",
              "  )\n",
              "  (pos_drop): Dropout(p=0.0, inplace=False)\n",
              "  (blocks): Sequential(\n",
              "    (0): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (1): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (2): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (3): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (4): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (5): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (6): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (7): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (8): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (9): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (10): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (11): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (12): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (13): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (14): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (15): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (16): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (17): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (18): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (19): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (20): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (21): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (22): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (23): Block(\n",
              "      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (attn): Attention(\n",
              "        (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
              "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "        (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "      (drop_path): Identity()\n",
              "      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "      (mlp): Mlp(\n",
              "        (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "        (act): GELU()\n",
              "        (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "        (drop): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
              "  (pre_logits): Identity()\n",
              "  (head): Linear(in_features=1024, out_features=1000, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.head = nn.Linear(model.head.in_features,1)"
      ],
      "metadata": {
        "id": "O0gT5VVfEEVD"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "id": "WPfTw8e5If0v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "print(device)\n",
        "model.to(device)\n",
        "\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "uf0mA97cE6j0"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(epoch):\n",
        "    running_loss = 0.0\n",
        "    loss_values = []\n",
        "    model.train()\n",
        "    with tqdm(trainloader, unit=\"batch\") as tepoch:\n",
        "        for i, data in enumerate(tepoch):\n",
        "            tepoch.set_description(f\"Epoch {epoch}\")\n",
        "            inputs, labels = data['image'], data['label']\n",
        "            labels = labels.to(torch.float32)\n",
        "            inputs = inputs.to(device) \n",
        "            labels = labels.to(device)\n",
        "            # zero the parameter gradients\n",
        "            scheduler2.step(epoch+i/len(tepoch))\n",
        "            optimizer.zero_grad()\n",
        "            # forward + backward + optimize\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion2(outputs, labels)\n",
        "            #rmseloss = criterion2(outputs,labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            # print statistics \n",
        "    \n",
        "            running_loss += loss.item()\n",
        "            loss_values.append(loss.item())\n",
        "            if i % 64 == 0 and i>0:    # every 64 mini-batches\n",
        "                train_loss = running_loss/i\n",
        "                print(i)\n",
        "                print(f'Training loss: {train_loss}')\n",
        "        train_loss = sum(loss_values) /len(tepoch)\n",
        "        \n",
        "        gc.collect()\n",
        "        torch.cuda.empty_cache()\n",
        "        return train_loss"
      ],
      "metadata": {
        "id": "zRhKr9SKE_ba"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test(epoch):\n",
        "    losslis=[]\n",
        "    test_loss = 0.0\n",
        "    # since we're not training, we don't need to calculate the gradients for our outputs\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for data in tqdm(testloader):\n",
        "            inputs, labels = data['image'], data['label']\n",
        "            # calculate outputs by running images through the network\n",
        "            labels = labels.to(torch.float32)\n",
        "            inputs = inputs.to(device) \n",
        "            labels = labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            # print(outputs)\n",
        "            # print(labels)\n",
        "            loss = criterion2(outputs,labels)\n",
        "\n",
        "            test_loss += loss.item()\n",
        "            losslis.append(loss.item())\n",
        "            #writer.add_scalar('Test/Loss', test_loss, epoch)  \n",
        "        test_loss = test_loss/len(testloader)\n",
        "        print(f'Validation loss: {test_loss}')\n",
        "    gc.collect()\n",
        "    torch.cuda.empty_cache()\n",
        "    return test_loss"
      ],
      "metadata": {
        "id": "ovawVI_2Fiwo"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "criterion1 = nn.BCEWithLogitsLoss()\n",
        "criterion2 = RMSELoss()\n",
        "optimizer = AdamW(model.parameters(), lr=Config['LR'] )"
      ],
      "metadata": {
        "id": "ff4-1QZpFw9h"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scheduler1 = torch.optim.lr_scheduler.StepLR(optimizer, step_size=Config['SCH_STEP'],\n",
        "                gamma=Config['GAMMA']) \n",
        "scheduler2 = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=Config['T_0'],\n",
        "                eta_min=Config['η_min'])"
      ],
      "metadata": {
        "id": "Ze1-YQcSDxVU"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scheduler_name = f\"{Config['SCHEDULER']}_{Config['SCH_STEP']}_{Config['GAMMA']}\"\n",
        "early_stopping = EarlyStopping(patience=Config['PATIENCE'], verbose=True)"
      ],
      "metadata": {
        "id": "e4H4K6LlF09Q"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if Config['wandb']:\n",
        "  wandb.init(project=\"pawpularity_kaggle_final\", entity=\"edgarlcs\",config=Config,name=f'{model_name}_{scheduler_name}_epoch{Config[\"N_EPOCHS\"]}_{Config[\"TRAIN_BS\"]}')\n",
        "  wandb.watch(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "7x1rcaS4GxfS",
        "outputId": "82fcfc41-070e-4975-9619-c8abd1492b44"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33medgarlcs\u001b[0m (use `wandb login --relogin` to force relogin)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/edgarlcs/pawpularity_kaggle_final/runs/3t1zr7nb\" target=\"_blank\">vit_large_patch16_224_CosineWarm_4_0.1_epoch10_16</a></strong> to <a href=\"https://wandb.ai/edgarlcs/pawpularity_kaggle_final\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
              "\n",
              "                "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = Config['N_EPOCHS']\n",
        "training_losses = []\n",
        "valid_losses = []\n",
        "\n",
        "for epoch in tqdm(range(1,epochs+1)):\n",
        "    train_loss = train(epoch)\n",
        "    test_loss = test(epoch)\n",
        "    \n",
        "    #scheduler2.step()\n",
        "    early_stopping(test_loss, model)\n",
        "        \n",
        "    if early_stopping.early_stop:\n",
        "      print(\"Early stopping\")\n",
        "      break\n",
        "    if Config['wandb']:\n",
        "      wandb.log({\n",
        "        \"Epoch\": epoch,\n",
        "        \"Train Loss\": train_loss,\n",
        "        \"Valid Loss\": test_loss})\n",
        "    else:\n",
        "      training_losses.append(train_loss)\n",
        "      valid_losses.append(test_loss)\n",
        "print('Finished Training')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "4CpA6Kw6F33N",
        "outputId": "a10e1fb8-c628-42d7-a5dd-245e46d2c65d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "320f517a188a46338fec38674b860bf9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2aade493f50f4b79b79fbf42e6c605a9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/495 [00:00<?, ?batch/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64\n",
            "Training loss: 23.775204464793205\n",
            "128\n",
            "Training loss: 21.519349448382854\n",
            "192\n",
            "Training loss: 20.690198649962742\n",
            "256\n",
            "Training loss: 20.20080081000924\n",
            "320\n",
            "Training loss: 19.91635960638523\n",
            "384\n",
            "Training loss: 19.82130380968253\n",
            "448\n",
            "Training loss: 19.69954062998295\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "31aea01612964d0e97694e3f1f20f223",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/124 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 19.01289475348688\n",
            "Validation loss decreased (inf --> 19.012895).  Saving model ...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5130cc66359f4edfb14a3844ed6ed2e3",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64\n",
            "Training loss: 19.519830614328384\n",
            "128\n",
            "Training loss: 18.27313081175089\n",
            "192\n",
            "Training loss: 18.216439756254356\n",
            "256\n",
            "Training loss: 18.32769967801869\n",
            "320\n",
            "Training loss: 18.43473815321922\n",
            "384\n",
            "Training loss: 18.29457504550616\n",
            "448\n",
            "Training loss: 18.211143461721285\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d3fa48ef887c4e319a5f02a0920c1ed0",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 19.465734166483724\n",
            "EarlyStopping counter: 1 out of 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eab78ee8c4ea4a948acfe0656186ef3c",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64\n",
            "Training loss: 17.64696830511093\n",
            "128\n",
            "Training loss: 17.309968940913677\n",
            "192\n",
            "Training loss: 17.557084689537685\n",
            "256\n",
            "Training loss: 17.452031979337335\n",
            "320\n",
            "Training loss: 17.523846827447414\n",
            "384\n",
            "Training loss: 17.424702662974596\n",
            "448\n",
            "Training loss: 17.397435925900936\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5ffef8bd3cd4bfeb0b626aadd2e18e5",
              "version_minor": 0,
              "version_major": 2
            },
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              "  0%|          | 0/124 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 19.113293978475756\n",
            "EarlyStopping counter: 2 out of 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "18489416636f47b9878ebc4fbdfa8063",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64\n",
            "Training loss: 17.002250522375107\n",
            "128\n",
            "Training loss: 16.80110201239586\n",
            "192\n",
            "Training loss: 16.73009569197893\n",
            "256\n",
            "Training loss: 16.777361745014787\n",
            "320\n",
            "Training loss: 16.596191285550596\n",
            "384\n",
            "Training loss: 16.578625425696373\n",
            "448\n",
            "Training loss: 16.509478709527425\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f86ee9b604f94638bab4f90b4867e458",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/124 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 18.14323628333307\n",
            "Validation loss decreased (19.012895 --> 18.143236).  Saving model ...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "993b6b8fe05140ebaa4ee259ac7466c5",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64\n",
            "Training loss: 15.024598084390163\n",
            "128\n",
            "Training loss: 15.832190919667482\n",
            "192\n",
            "Training loss: 15.854659117758274\n",
            "256\n",
            "Training loss: 15.776078766211867\n",
            "320\n",
            "Training loss: 15.726467078924179\n",
            "384\n",
            "Training loss: 15.707156422237555\n",
            "448\n",
            "Training loss: 15.65132968553475\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c991c9d1a3af40cfa7689c04840eda41",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/124 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 18.3118238026096\n",
            "EarlyStopping counter: 1 out of 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a7e0f7c7e42845c5b459916c37e0c054",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64\n",
            "Training loss: 14.375644341111183\n",
            "128\n",
            "Training loss: 14.452793627977371\n",
            "192\n",
            "Training loss: 14.407437364260355\n",
            "256\n",
            "Training loss: 14.346018139272928\n",
            "320\n",
            "Training loss: 14.404621605575084\n",
            "384\n",
            "Training loss: 14.42170549184084\n",
            "448\n",
            "Training loss: 14.3658385936703\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d27861780be5425db2e858ee6e75fdbb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/124 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 18.253081998517438\n",
            "EarlyStopping counter: 2 out of 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0fee487e59442eaba368bcd0c727c26",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64\n",
            "Training loss: 13.830086305737495\n",
            "128\n",
            "Training loss: 13.915023893117905\n",
            "192\n",
            "Training loss: 13.910071422656378\n",
            "256\n",
            "Training loss: 13.868914356455207\n",
            "320\n",
            "Training loss: 13.859402677416801\n",
            "384\n",
            "Training loss: 13.817761159191528\n",
            "448\n",
            "Training loss: 13.76868683631931\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ceea1602d7904d48a9ce1a062713ac22",
              "version_minor": 0,
              "version_major": 2
            },
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              "  0%|          | 0/124 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 18.230878903019814\n",
            "EarlyStopping counter: 3 out of 3\n",
            "Early stopping\n",
            "Finished Training\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if Config['wandb'] == False:\n",
        "  plot_loss_val(training_losses,valid_losses)"
      ],
      "metadata": {
        "id": "0K4SG3xuHr0L"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "now = GetDate()"
      ],
      "metadata": {
        "id": "jaSUs_hdH_W2"
      },
      "execution_count": 33,
      "outputs": []
    }
  ]
}