Fastai_Pawpularity.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashikshafi08/Learning-Fastai/blob/main/Fastai_Pawpularity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cPEsZrnLgPc0",
        "outputId": "ba0ecc44-94f3-4733-d9c3-9eb3cc9453d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive \n",
        "drive._mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Specifying the data paths \n",
        "train_image_path = '/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/'\n",
        "test_image_path = '/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/test/'\n",
        "\n",
        "train_df_path = '/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train.csv'\n",
        "test_df_path = '/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/test.csv'\n",
        "sample_sub_path = '/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/sample_submission.csv'"
      ],
      "metadata": {
        "id": "0S4rhifxhMQe"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_esnt9ohUW0",
        "outputId": "69183db5-660b-4397-b15f-0ef56158a79a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Jan  7 23:53:04 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 495.44       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P8    27W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -Uq fastai==2.5.3 # newer version\n",
        "\n",
        "\n",
        "import fastai \n",
        "import pandas as pd \n",
        "import numpy as np\n",
        "from fastai.vision.all import * \n",
        "from fastai import * \n",
        "print(fastai.__version__)\n",
        "\n",
        "train_df = pd.read_csv(train_df_path)\n",
        "test_df = pd.read_csv(test_df_path)\n",
        "\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "P75eObbehaeb",
        "outputId": "7a008f45-5e75-4829-ab96-7f759cd70407"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |█▊                              | 10 kB 25.9 MB/s eta 0:00:01\r\u001b[K     |███▌                            | 20 kB 24.0 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 30 kB 11.1 MB/s eta 0:00:01\r\u001b[K     |███████                         | 40 kB 8.8 MB/s eta 0:00:01\r\u001b[K     |████████▋                       | 51 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 61 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 71 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |█████████████▉                  | 81 kB 6.2 MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 92 kB 6.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████▎              | 102 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 112 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████▊           | 122 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▌         | 133 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▏       | 143 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 153 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▊    | 163 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 174 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 184 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 189 kB 5.4 MB/s \n",
            "\u001b[?25h\u001b[?25l\r\u001b[K     |█████▊                          | 10 kB 43.9 MB/s eta 0:00:01\r\u001b[K     |███████████▌                    | 20 kB 42.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████▎              | 30 kB 43.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 40 kB 47.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▉   | 51 kB 49.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 56 kB 4.6 MB/s \n",
            "\u001b[?25h2.5.3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0018df346ac9c1d8413cfcc888ca8246</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>15</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>001dc955e10590d3ca4673f034feeef2</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>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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "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",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets normalize the targets \n",
        "from sklearn.preprocessing import MinMaxScaler \n",
        "scaler = MinMaxScaler() \n",
        "\n",
        "# Fit the scaler to our targets \n",
        "scaled_targets = scaler.fit_transform(np.expand_dims(train_df.Pawpularity , axis = 1))\n",
        "scaled_targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euAgy49Jh8dq",
        "outputId": "013e584a-91ff-4565-92fe-92590fc2b503"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.62626263],\n",
              "       [0.41414141],\n",
              "       [0.27272727],\n",
              "       ...,\n",
              "       [0.19191919],\n",
              "       [0.19191919],\n",
              "       [0.29292929]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Applying PCA to condense the rest of the variables into one whole column \n",
        "\n",
        "X = train_df.drop(['Id' , 'Pawpularity'] , axis = 1)\n",
        "\n",
        "from sklearn.decomposition import PCA \n",
        "pca = PCA(n_components = 1)\n",
        "\n",
        "X_new = pca.fit_transform(X)\n",
        "X_new\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09E5x0BA_-SX",
        "outputId": "36cef667-bd17-43aa-eeef-06d97d5fdfc1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.05400829],\n",
              "       [ 0.08657212],\n",
              "       [-0.86495566],\n",
              "       ...,\n",
              "       [-0.86495566],\n",
              "       [-0.51179131],\n",
              "       [-0.13278019]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Appending the new cols \n",
        "train_df['Pawpularity_scaled'] = scaled_targets \n",
        "#train_df['reg_block'] = X_new\n",
        "\n",
        "# Append the full path \n",
        "def append_ext(fn , train = True):\n",
        "  if train:\n",
        "    return f'{train_image_path}{fn}.jpg'\n",
        "  else:\n",
        "    return f'{test_image_path}{fn}.jpg'\n",
        "\n",
        "# Applying the above function on our dataframe \n",
        "train_df['full_path'] = train_df['Id'].apply(lambda x: append_ext(x))\n",
        "test_df['full_path'] = test_df['Id'].apply(lambda x: append_ext(x , train = False))\n",
        "\n",
        "train_df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "7mXQXwjSA5eD",
        "outputId": "5a1b61ed-e874-4e30-f0ee-f48404770eea"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Subject Focus</th>\n",
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              "      <th>Collage</th>\n",
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              "      <th>full_path</th>\n",
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              "      <th>0</th>\n",
              "      <td>0007de18844b0dbbb5e1f607da0606e0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0009c66b9439883ba2750fb825e1d7db</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0013fd999caf9a3efe1352ca1b0d937e</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0018df346ac9c1d8413cfcc888ca8246</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",
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              "      <td>15</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>001dc955e10590d3ca4673f034feeef2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <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",
              "      <td>0.717172</td>\n",
              "      <td>/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/001dc955e10590d3ca4673f034feeef2.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>001dd4f6fafb890610b1635f967ea081</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>74</td>\n",
              "      <td>0.737374</td>\n",
              "      <td>/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/001dd4f6fafb890610b1635f967ea081.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0023b8a3abc93c712edd6120867deb53</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",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0031d6a9ef7340f898c3e05f92c7bb04</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>0.343434</td>\n",
              "      <td>/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0031d6a9ef7340f898c3e05f92c7bb04.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0042bc5bada6d1cf8951f8f9f0d399fa</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.525253</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0049cb81313c94fa007286e9039af910</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      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-624c6b04-bdf8-447f-bf67-f812d3b5724a 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-624c6b04-bdf8-447f-bf67-f812d3b5724a');\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",
              "  "
            ],
            "text/plain": [
              "                                 Id  ...                                                                                                         full_path\n",
              "0  0007de18844b0dbbb5e1f607da0606e0  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0007de18844b0dbbb5e1f607da0606e0.jpg\n",
              "1  0009c66b9439883ba2750fb825e1d7db  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0009c66b9439883ba2750fb825e1d7db.jpg\n",
              "2  0013fd999caf9a3efe1352ca1b0d937e  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0013fd999caf9a3efe1352ca1b0d937e.jpg\n",
              "3  0018df346ac9c1d8413cfcc888ca8246  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0018df346ac9c1d8413cfcc888ca8246.jpg\n",
              "4  001dc955e10590d3ca4673f034feeef2  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/001dc955e10590d3ca4673f034feeef2.jpg\n",
              "5  001dd4f6fafb890610b1635f967ea081  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/001dd4f6fafb890610b1635f967ea081.jpg\n",
              "6  0023b8a3abc93c712edd6120867deb53  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0023b8a3abc93c712edd6120867deb53.jpg\n",
              "7  0031d6a9ef7340f898c3e05f92c7bb04  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0031d6a9ef7340f898c3e05f92c7bb04.jpg\n",
              "8  0042bc5bada6d1cf8951f8f9f0d399fa  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0042bc5bada6d1cf8951f8f9f0d399fa.jpg\n",
              "9  0049cb81313c94fa007286e9039af910  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0049cb81313c94fa007286e9039af910.jpg\n",
              "\n",
              "[10 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "item_tfms=RandomResizedCrop(460)\n",
        "batch_tfms=[*aug_transforms(size=224, max_warp=0), Normalize.from_stats(*imagenet_stats)]\n",
        "train_data = Path(train_image_path)\n",
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "La52TXF1BJcY",
        "outputId": "23d20f1d-1c50-4cd1-f0f6-8e79a2e60f4b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOFlxNaaxLPk",
        "outputId": "72196ff1-0d08-44dc-e60f-8f3fe7faaa57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'Subject Focus', 'Eyes', 'Face', 'Near', 'Action', 'Accessory',\n",
              "       'Group', 'Collage', 'Human', 'Occlusion', 'Info', 'Blur', 'Pawpularity',\n",
              "       'Pawpularity_scaled', 'reg_block', 'full_path'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_y_1(df): return df['Pawpularity_scaled']\n",
        "def get_y_2(df): return df['reg_block']"
      ],
      "metadata": {
        "id": "0n72J8axG84H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Constructing a DataBlock \n",
        "\n",
        "dblock = DataBlock(\n",
        "    blocks = (ImageBlock() , RegressionBlock()),\n",
        "    get_x = ColReader('full_path') , \n",
        "    get_y = ColReader('Pawpularity_scaled'), \n",
        "    #getters = [ColReader('full_path') , Pipeline([ColReader('reg_block') , ColReader('Pawpularity_scaled')])],\n",
        "    item_tfms = Resize(224), \n",
        "    splitter = RandomSplitter(valid_pct = 0.15 , seed = 42), \n",
        "    batch_tfms = batch_tfms\n",
        ")\n",
        "\n",
        "dblock.summary(train_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BnSAJ8REIOL",
        "outputId": "e1511b22-eb34-474d-8e8e-48e1417335c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting-up type transforms pipelines\n",
            "Collecting items from                                     Id  ...                                                                                                         full_path\n",
            "0     0007de18844b0dbbb5e1f607da0606e0  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0007de18844b0dbbb5e1f607da0606e0.jpg\n",
            "1     0009c66b9439883ba2750fb825e1d7db  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0009c66b9439883ba2750fb825e1d7db.jpg\n",
            "2     0013fd999caf9a3efe1352ca1b0d937e  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0013fd999caf9a3efe1352ca1b0d937e.jpg\n",
            "3     0018df346ac9c1d8413cfcc888ca8246  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0018df346ac9c1d8413cfcc888ca8246.jpg\n",
            "4     001dc955e10590d3ca4673f034feeef2  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/001dc955e10590d3ca4673f034feeef2.jpg\n",
            "...                                ...  ...                                                                                                               ...\n",
            "9907  ffbfa0383c34dc513c95560d6e1fdb57  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/ffbfa0383c34dc513c95560d6e1fdb57.jpg\n",
            "9908  ffcc8532d76436fc79e50eb2e5238e45  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/ffcc8532d76436fc79e50eb2e5238e45.jpg\n",
            "9909  ffdf2e8673a1da6fb80342fa3b119a20  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/ffdf2e8673a1da6fb80342fa3b119a20.jpg\n",
            "9910  fff19e2ce11718548fa1c5d039a5192a  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/fff19e2ce11718548fa1c5d039a5192a.jpg\n",
            "9911  fff8e47c766799c9e12f3cb3d66ad228  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/fff8e47c766799c9e12f3cb3d66ad228.jpg\n",
            "\n",
            "[9912 rows x 17 columns]\n",
            "Found 9912 items\n",
            "2 datasets of sizes 8426,1486\n",
            "Setting up Pipeline: ColReader -- {'cols': 'full_path', 'pref': '', 'suff': '', 'label_delim': None} -> PILBase.create\n",
            "Setting up Pipeline: ColReader -- {'cols': 'Pawpularity_scaled', 'pref': '', 'suff': '', 'label_delim': None} -> RegressionSetup -- {'c': None}\n",
            "\n",
            "Building one sample\n",
            "  Pipeline: ColReader -- {'cols': 'full_path', 'pref': '', 'suff': '', 'label_delim': None} -> PILBase.create\n",
            "    starting from\n",
            "      Id                                                                                                    aac20460f0f08b9eb5e5240d01a43bc3\n",
            "Subject Focus                                                                                                                        0\n",
            "Eyes                                                                                                                                 1\n",
            "Face                                                                                                                                 1\n",
            "Near                                                                                                                                 1\n",
            "Action                                                                                                                               0\n",
            "Accessory                                                                                                                            0\n",
            "Group                                                                                                                                0\n",
            "Collage                                                                                                                              0\n",
            "Human                                                                                                                                0\n",
            "Occlusion                                                                                                                            0\n",
            "Info                                                                                                                                 0\n",
            "Blur                                                                                                                                 0\n",
            "Pawpularity                                                                                                                         53\n",
            "Pawpularity_scaled                                                                                                            0.525253\n",
            "reg_block                                                                                                                     -0.13278\n",
            "full_path             /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/aac20460f0f08b9eb5e5240d01a43bc3.jpg\n",
            "Name: 6616, dtype: object\n",
            "    applying ColReader -- {'cols': 'full_path', 'pref': '', 'suff': '', 'label_delim': None} gives\n",
            "      /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/aac20460f0f08b9eb5e5240d01a43bc3.jpg\n",
            "    applying PILBase.create gives\n",
            "      PILImage mode=RGB size=720x960\n",
            "  Pipeline: ColReader -- {'cols': 'Pawpularity_scaled', 'pref': '', 'suff': '', 'label_delim': None} -> RegressionSetup -- {'c': None}\n",
            "    starting from\n",
            "      Id                                                                                                    aac20460f0f08b9eb5e5240d01a43bc3\n",
            "Subject Focus                                                                                                                        0\n",
            "Eyes                                                                                                                                 1\n",
            "Face                                                                                                                                 1\n",
            "Near                                                                                                                                 1\n",
            "Action                                                                                                                               0\n",
            "Accessory                                                                                                                            0\n",
            "Group                                                                                                                                0\n",
            "Collage                                                                                                                              0\n",
            "Human                                                                                                                                0\n",
            "Occlusion                                                                                                                            0\n",
            "Info                                                                                                                                 0\n",
            "Blur                                                                                                                                 0\n",
            "Pawpularity                                                                                                                         53\n",
            "Pawpularity_scaled                                                                                                            0.525253\n",
            "reg_block                                                                                                                     -0.13278\n",
            "full_path             /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/aac20460f0f08b9eb5e5240d01a43bc3.jpg\n",
            "Name: 6616, dtype: object\n",
            "    applying ColReader -- {'cols': 'Pawpularity_scaled', 'pref': '', 'suff': '', 'label_delim': None} gives\n",
            "      0.5252525252525253\n",
            "    applying RegressionSetup -- {'c': None} gives\n",
            "      tensor(0.5253)\n",
            "\n",
            "Final sample: (PILImage mode=RGB size=720x960, tensor(0.5253))\n",
            "\n",
            "\n",
            "Collecting items from                                     Id  ...                                                                                                         full_path\n",
            "0     0007de18844b0dbbb5e1f607da0606e0  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0007de18844b0dbbb5e1f607da0606e0.jpg\n",
            "1     0009c66b9439883ba2750fb825e1d7db  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0009c66b9439883ba2750fb825e1d7db.jpg\n",
            "2     0013fd999caf9a3efe1352ca1b0d937e  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0013fd999caf9a3efe1352ca1b0d937e.jpg\n",
            "3     0018df346ac9c1d8413cfcc888ca8246  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0018df346ac9c1d8413cfcc888ca8246.jpg\n",
            "4     001dc955e10590d3ca4673f034feeef2  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/001dc955e10590d3ca4673f034feeef2.jpg\n",
            "...                                ...  ...                                                                                                               ...\n",
            "9907  ffbfa0383c34dc513c95560d6e1fdb57  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/ffbfa0383c34dc513c95560d6e1fdb57.jpg\n",
            "9908  ffcc8532d76436fc79e50eb2e5238e45  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/ffcc8532d76436fc79e50eb2e5238e45.jpg\n",
            "9909  ffdf2e8673a1da6fb80342fa3b119a20  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/ffdf2e8673a1da6fb80342fa3b119a20.jpg\n",
            "9910  fff19e2ce11718548fa1c5d039a5192a  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/fff19e2ce11718548fa1c5d039a5192a.jpg\n",
            "9911  fff8e47c766799c9e12f3cb3d66ad228  ...  /content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/fff8e47c766799c9e12f3cb3d66ad228.jpg\n",
            "\n",
            "[9912 rows x 17 columns]\n",
            "Found 9912 items\n",
            "2 datasets of sizes 8426,1486\n",
            "Setting up Pipeline: ColReader -- {'cols': 'full_path', 'pref': '', 'suff': '', 'label_delim': None} -> PILBase.create\n",
            "Setting up Pipeline: ColReader -- {'cols': 'Pawpularity_scaled', 'pref': '', 'suff': '', 'label_delim': None} -> RegressionSetup -- {'c': None}\n",
            "Setting up after_item: Pipeline: Resize -- {'size': (224, 224), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (2, 0), 'p': 1.0} -> ToTensor\n",
            "Setting up before_batch: Pipeline: \n",
            "Setting up after_batch: Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Flip -- {'size': 224, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 0.5} -> Brightness -- {'max_lighting': 0.2, 'p': 1.0, 'draw': None, 'batch': False} -> Normalize -- {'mean': tensor([[[[0.4850]],\n",
            "\n",
            "         [[0.4560]],\n",
            "\n",
            "         [[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],\n",
            "\n",
            "         [[0.2240]],\n",
            "\n",
            "         [[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}\n",
            "\n",
            "Building one batch\n",
            "Applying item_tfms to the first sample:\n",
            "  Pipeline: Resize -- {'size': (224, 224), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (2, 0), 'p': 1.0} -> ToTensor\n",
            "    starting from\n",
            "      (PILImage mode=RGB size=720x960, tensor(0.5253))\n",
            "    applying Resize -- {'size': (224, 224), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (2, 0), 'p': 1.0} gives\n",
            "      (PILImage mode=RGB size=224x224, tensor(0.5253))\n",
            "    applying ToTensor gives\n",
            "      (TensorImage of size 3x224x224, tensor(0.5253))\n",
            "\n",
            "Adding the next 3 samples\n",
            "\n",
            "No before_batch transform to apply\n",
            "\n",
            "Collating items in a batch\n",
            "\n",
            "Applying batch_tfms to the batch built\n",
            "  Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Flip -- {'size': 224, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 0.5} -> Brightness -- {'max_lighting': 0.2, 'p': 1.0, 'draw': None, 'batch': False} -> Normalize -- {'mean': tensor([[[[0.4850]],\n",
            "\n",
            "         [[0.4560]],\n",
            "\n",
            "         [[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],\n",
            "\n",
            "         [[0.2240]],\n",
            "\n",
            "         [[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}\n",
            "    starting from\n",
            "      (TensorImage of size 4x3x224x224, tensor([0.5253, 0.3737, 0.5253, 0.7071], device='cuda:0'))\n",
            "    applying IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} gives\n",
            "      (TensorImage of size 4x3x224x224, tensor([0.5253, 0.3737, 0.5253, 0.7071], device='cuda:0'))\n",
            "    applying Flip -- {'size': 224, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 0.5} gives\n",
            "      (TensorImage of size 4x3x224x224, tensor([0.5253, 0.3737, 0.5253, 0.7071], device='cuda:0'))\n",
            "    applying Brightness -- {'max_lighting': 0.2, 'p': 1.0, 'draw': None, 'batch': False} gives\n",
            "      (TensorImage of size 4x3x224x224, tensor([0.5253, 0.3737, 0.5253, 0.7071], device='cuda:0'))\n",
            "    applying Normalize -- {'mean': tensor([[[[0.4850]],\n",
            "\n",
            "         [[0.4560]],\n",
            "\n",
            "         [[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],\n",
            "\n",
            "         [[0.2240]],\n",
            "\n",
            "         [[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)} gives\n",
            "      (TensorImage of size 4x3x224x224, tensor([0.5253, 0.3737, 0.5253, 0.7071], device='cuda:0'))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dls = dblock.dataloaders(train_df , batch_size = 128)\n",
        "dls.show_batch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "o6U_AfPrh10q",
        "outputId": "23444052-eeb9-49fc-bf10-2883a53b40f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_image_files(items)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "FowrjSD6ybNi",
        "outputId": "a857cde5-7371-45e5-fa08-f31f3ca80ab1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-117-e81c02c2bd36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_image_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/transforms.py\u001b[0m in \u001b[0;36mget_image_files\u001b[0;34m(path, recurse, folders)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_image_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfolders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;34m\"Get image files in `path` recursively, only in `folders`, if specified.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mget_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextensions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage_extensions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecurse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfolders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfolders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/transforms.py\u001b[0m in \u001b[0;36mget_files\u001b[0;34m(path, extensions, recurse, folders, followlinks)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextensions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfolders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfollowlinks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;34m\"Get all the files in `path` with optional `extensions`, optionally with `recurse`, only in `folders`, if specified.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m     \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m     \u001b[0mfolders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mextensions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msetify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextensions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/pathlib.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1025\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1026\u001b[0m             \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWindowsPath\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'nt'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mPosixPath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1027\u001b[0;31m         \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_parts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1028\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flavour\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_supported\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1029\u001b[0m             raise NotImplementedError(\"cannot instantiate %r on your system\"\n",
            "\u001b[0;32m/usr/lib/python3.7/pathlib.py\u001b[0m in \u001b[0;36m_from_parts\u001b[0;34m(cls, args, init)\u001b[0m\n\u001b[1;32m    672\u001b[0m         \u001b[0;31m# right flavour.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m         \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__new__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 674\u001b[0;31m         \u001b[0mdrv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    675\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdrv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_root\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/pathlib.py\u001b[0m in \u001b[0;36m_parse_args\u001b[0;34m(cls, args)\u001b[0m\n\u001b[1;32m    656\u001b[0m                 \u001b[0mparts\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 658\u001b[0;31m                 \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    659\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    660\u001b[0m                     \u001b[0;31m# Force-cast str subclasses to str (issue #21127)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: expected str, bytes or os.PathLike object, not list"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a dls function \n",
        "\n",
        "def get_dls(bs , size):\n",
        " \n",
        "  dblock = DataBlock(\n",
        "    blocks = (ImageBlock() , RegressionBlock()), \n",
        "    get_x = ColReader('full_path') , \n",
        "    get_y = ColReader('Pawpularity_scaled'), \n",
        "    #get_x = ColReader('full_path') , \n",
        "    item_tfms = item_tfms, \n",
        "    splitter = RandomSplitter(valid_pct = 0.15 , seed = 42), \n",
        "    batch_tfms = batch_tfms)\n",
        "  \n",
        "  return dblock.dataloaders(train_df , bs = bs)\n",
        "\n",
        "# Using the function and getting a dls \n",
        "dls = get_dls(32 , 224)\n"
      ],
      "metadata": {
        "id": "UHXW9VcYis44"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "iSw5VWYe7p-E",
        "outputId": "75b05a3a-3798-49c1-d6d0-f76842d068db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-e2dace1ed414>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: module 'torch.cuda' has no attribute 'clear_cache'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xz1wsX7tzxLb",
        "outputId": "dc0bb5c9-c875-4716-abd7-c92b0eac4fb6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Jan  8 00:00:34 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 495.44       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   62C    P0    65W / 149W |    548MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn = cnn_learner(dls , arch = resnet50 , loss_func= BCELossFlat() , opt_func= Adam , \n",
        "                    metrics = [error_rate , rmse])\n",
        "\n",
        "learn.fit_one_cycle(4 , 3e-3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509,
          "referenced_widgets": [
            "5862e384f0f848169a095ff64cc07839",
            "40bc6f3d0f044399b64db40645eb1129",
            "f65ee396e2a24cae8323f95afebbf028",
            "52e11ae4f4c44de18c13e19b289e3cc1",
            "77d61d5cf72d43d591e871eeee2ceae3",
            "5a98f6b4b1b34f24977ab0b3a030fe85",
            "747947f0d4934a238ee1d0af79a02444",
            "ccc76d92d5404cc896b463c68b6feb7b",
            "605eddacb8ae4316bed46611e6f922f5",
            "a770b304cfce47708ec4a5e93655ab45",
            "bba8fe4ef4464df4aa234c306c2ab262"
          ]
        },
        "id": "yK0u5ih3is8_",
        "outputId": "c904588f-5f31-419e-97f6-bce091ae10f0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5862e384f0f848169a095ff64cc07839",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/97.8M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='4' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/4 00:00<00:00]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>_rmse</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='0' class='' max='263' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      0.00% [0/263 00:00<00:00]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-8c7031148a20>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m                     metrics = [error_rate , rmse])\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m3e-3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/callback/schedule.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)\u001b[0m\n\u001b[1;32m    114\u001b[0m     scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),\n\u001b[1;32m    115\u001b[0m               'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}\n\u001b[0;32m--> 116\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mParamScheduler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscheds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset_opt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, n_epoch, lr, wd, cbs, reset_opt)\u001b[0m\n\u001b[1;32m    219\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_hypers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_fit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelFitException\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_end_cleanup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_end_cleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mfinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'epoch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelEpochException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset_opt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mfinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_epoch_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'train'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelTrainException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds_idx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mfinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36mall_batches\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mall_batches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36mone_batch\u001b[0;34m(self, i, b)\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_one_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'batch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelBatchException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m  \u001b[0mfinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_one_batch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    178\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'before_backward'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_grad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'step'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelStepException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0mretain_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m                 inputs=inputs)\n\u001b[0m\u001b[1;32m    307\u001b[0m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/overrides.py\u001b[0m in \u001b[0;36mhandle_torch_function\u001b[0;34m(public_api, relevant_args, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1353\u001b[0m         \u001b[0;31m# Use `public_api` instead of `implementation` so __torch_function__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1354\u001b[0m         \u001b[0;31m# implementations can do equality/identity comparisons.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1355\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch_func_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpublic_api\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1357\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/torch_core.py\u001b[0m in \u001b[0;36m__torch_function__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m    338\u001b[0m         \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_torch_handled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__torch_function__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    341\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTensorBase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_meta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_copy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36m__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1050\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDisableTorchFunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mget_default_nowrap_functions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 307\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    154\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    155\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "0x_2UAxfitGs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "WN9MPAcKitQp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "qYbwpa2OitWY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "0jKCLYWqitdq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "iJgBDc3xityB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using Low Level API "
      ],
      "metadata": {
        "id": "cGfX6GkvFOSd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting the filepaths in a list \n",
        "train_file_str = train_df.full_path.tolist()\n",
        "items = [Path(string) for string in train_file_str]\n",
        "\n",
        "items[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DRpdJ48lRMTJ",
        "outputId": "94268dcc-e90e-49e1-919f-828215b40cb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Path('/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0007de18844b0dbbb5e1f607da0606e0.jpg'),\n",
              " Path('/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0009c66b9439883ba2750fb825e1d7db.jpg'),\n",
              " Path('/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0013fd999caf9a3efe1352ca1b0d937e.jpg')]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Trying to display a sample image \n",
        "\n",
        "im = PILImage.create(items[0])\n",
        "im.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "btgyZkiwRWdx",
        "outputId": "84a8d40d-49d1-49cb-82ae-eb8e5c7d9690"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Specifying the random split \n",
        "splits = RandomSplitter(valid_pct= 0.15 , seed = 42)\n",
        "\n",
        "# Passing the filepaths to the splitter \n",
        "splits = splits(items)\n",
        "\n",
        "splits[0][:5] , splits[1][:4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PigjZypMRd_M",
        "outputId": "dbf331ee-5463-42f6-f273-3c5d5357e42f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((#5) [6616,8252,4800,7139,1818], (#4) [9150,7444,208,1645])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t = ToTensor()\n",
        "i = IntToFloatTensor()\n",
        "i(t(items[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flQ25ZhCbaqu",
        "outputId": "32fe6a06-03b6-447f-abaf-c2b3c76c375f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('/content/drive/MyDrive/Kaggle_Competition/petfinder-pawpularity-score/train/0007de18844b0dbbb5e1f607da0606e0.jpg')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample = PILImage.create(items[0])\n",
        "print(f'Int to Float Tensor: \\n{i(t(sample))}')\n",
        "im_t = i(t(sample))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIlhY9kqbuDe",
        "outputId": "220636bb-adf6-4f8a-b3fe-d21e02eed4b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Int to Float Tensor: \n",
            "TensorImage([[[0.7451, 0.7569, 0.6941,  ..., 0.4745, 0.4706, 0.4667],\n",
            "         [0.4275, 0.4549, 0.4353,  ..., 0.7059, 0.6980, 0.6902],\n",
            "         [0.5176, 0.4941, 0.4588,  ..., 0.7059, 0.6941, 0.6745],\n",
            "         ...,\n",
            "         [0.6275, 0.6196, 0.5843,  ..., 0.8000, 0.7961, 0.7843],\n",
            "         [0.6314, 0.6471, 0.6000,  ..., 0.7686, 0.7647, 0.7490],\n",
            "         [0.6039, 0.6510, 0.6039,  ..., 0.7333, 0.7373, 0.7294]],\n",
            "\n",
            "        [[0.7216, 0.7333, 0.6706,  ..., 0.4863, 0.4824, 0.4784],\n",
            "         [0.4039, 0.4314, 0.4118,  ..., 0.7137, 0.7059, 0.6980],\n",
            "         [0.5020, 0.4784, 0.4431,  ..., 0.7137, 0.7020, 0.6824],\n",
            "         ...,\n",
            "         [0.5922, 0.5843, 0.5490,  ..., 0.7961, 0.7922, 0.7804],\n",
            "         [0.5961, 0.6118, 0.5647,  ..., 0.7647, 0.7608, 0.7451],\n",
            "         [0.5686, 0.6157, 0.5686,  ..., 0.7294, 0.7333, 0.7255]],\n",
            "\n",
            "        [[0.7765, 0.7882, 0.7255,  ..., 0.5529, 0.5490, 0.5451],\n",
            "         [0.4588, 0.4863, 0.4667,  ..., 0.7725, 0.7647, 0.7569],\n",
            "         [0.5569, 0.5333, 0.4980,  ..., 0.7608, 0.7490, 0.7294],\n",
            "         ...,\n",
            "         [0.5961, 0.5882, 0.5529,  ..., 0.7804, 0.7765, 0.7647],\n",
            "         [0.6000, 0.6157, 0.5686,  ..., 0.7490, 0.7451, 0.7294],\n",
            "         [0.5725, 0.6196, 0.5725,  ..., 0.7137, 0.7176, 0.7098]]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "norm = Normalize.from_stats([0.5]*3 , [0.5]*3)\n",
        "norm(im_t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "OIJ0te-DcOK9",
        "outputId": "957acea0-f2c2-4f31-9a34-58e186bfe9ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-54-fc9c8beba049>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNormalize\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_stats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m3\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/transform.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_get_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'encodes'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecode\u001b[0m  \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'decodes'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34mf'{self.name}:\\nencodes: {self.encodes}decodes: {self.decodes}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/transform.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, fn, x, split_idx, **kwargs)\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit_idx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msplit_idx\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_idx\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_idx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/transform.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, f, x, **kwargs)\u001b[0m\n\u001b[1;32m     87\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'returns'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mretain_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mretain_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/dispatch.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minst\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMethodType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mowner\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMethodType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/transforms.py\u001b[0m in \u001b[0;36mencodes\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    370\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1e-7\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 372\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mencodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mTensorImage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    373\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mTensorImage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_cpu\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'cpu'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnoop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/torch_core.py\u001b[0m in \u001b[0;36m__torch_function__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m    338\u001b[0m         \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_torch_handled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__torch_function__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    341\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTensorBase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_meta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_copy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36m__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1050\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDisableTorchFunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mget_default_nowrap_functions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_tfm = RegressionSetup()\n",
        "\n",
        "dsets = Datasets([0 , 1 ,2] , RegressionSetup)\n",
        "dsets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9CX75MvcOQ4",
        "outputId": "fba66beb-9d08-4f8b-e6cf-20a946169bd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#3) [(tensor(0.),),(tensor(1.),),(tensor(2.),)]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Datasets(scaled_targets , RegressionSetup)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ATJcTX8OdZfi",
        "outputId": "6ed52581-b5ba-4438-d7ce-50f5847f2a69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#9912) [(tensor([0.6263]),),(tensor([0.4141]),),(tensor([0.2727]),),(tensor([0.1414]),),(tensor([0.7172]),),(tensor([0.7374]),),(tensor([0.2121]),),(tensor([0.3434]),),(tensor([0.5253]),),(tensor([0.2020]),)...]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def open_img(fn): \n",
        "  return PILImage.create(fn)\n",
        "\n",
        "def img2tensor(im):\n",
        "  return TensorImage(im)\n",
        "\n",
        "image2tensor(open_img(items[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-U7SN1yzdZkY",
        "outputId": "07454b57-f250-47a0-e5da-bf151108f366"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[190, 193, 177,  ..., 121, 120, 119],\n",
              "         [109, 116, 111,  ..., 180, 178, 176],\n",
              "         [132, 126, 117,  ..., 180, 177, 172],\n",
              "         ...,\n",
              "         [160, 158, 149,  ..., 204, 203, 200],\n",
              "         [161, 165, 153,  ..., 196, 195, 191],\n",
              "         [154, 166, 154,  ..., 187, 188, 186]],\n",
              "\n",
              "        [[184, 187, 171,  ..., 124, 123, 122],\n",
              "         [103, 110, 105,  ..., 182, 180, 178],\n",
              "         [128, 122, 113,  ..., 182, 179, 174],\n",
              "         ...,\n",
              "         [151, 149, 140,  ..., 203, 202, 199],\n",
              "         [152, 156, 144,  ..., 195, 194, 190],\n",
              "         [145, 157, 145,  ..., 186, 187, 185]],\n",
              "\n",
              "        [[198, 201, 185,  ..., 141, 140, 139],\n",
              "         [117, 124, 119,  ..., 197, 195, 193],\n",
              "         [142, 136, 127,  ..., 194, 191, 186],\n",
              "         ...,\n",
              "         [152, 150, 141,  ..., 199, 198, 195],\n",
              "         [153, 157, 145,  ..., 191, 190, 186],\n",
              "         [146, 158, 146,  ..., 182, 183, 181]]], dtype=torch.uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfms = [[open_img , img2tensor] , \n",
        "        []]"
      ],
      "metadata": {
        "id": "fWjRHBprd7Ax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "1049mKTXd7G2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_ll_QibhdZpe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Making a dataset \n",
        "\n",
        "dset = Datasets(items = items , tfms = [[PILImage.create] , [scaled_targets , RegressionSetup]], \n",
        "                splits = splits)\n",
        "\n",
        "dset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "wJyWgLVoULAk",
        "outputId": "d5407c5a-2fd8-48c0-bd80-bafb784d9204"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-57-1c3210c22b8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m dset = Datasets(items = items , tfms = [[PILImage.create] , [scaled_targets , RegressionSetup]], \n\u001b[0;32m----> 4\u001b[0;31m                 splits = splits)\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mdset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/basics.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    681\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_Arg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m         \u001b[0mfargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Arg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpargs\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 683\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    685\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/transform.py\u001b[0m in \u001b[0;36mmk_transform\u001b[0;34m(f)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[0;34m\"Convert function `f` to `Transform` if it isn't already one\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m     \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minstantiate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTransform\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mPipeline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/transform.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m     37\u001b[0m             \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/transform.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, enc, dec, split_idx, order)\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mifnone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0morder\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_enc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_enc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dsets = Datasets( train_df , \n",
        "                 [\n",
        "                  [ColReader('full_path') , \n",
        "                   PILImage.create , \n",
        "                   Resize(224) , \n",
        "                   ToTensor() , \n",
        "                   IntToFloatTensor()], \n",
        "                  \n",
        "                  [ColReader('reg_block') , RegressionSetup()] , \n",
        "                  [ColReader('Pawpularity_scaled') , RegressionSetup()] ,\n",
        "                 ] , \n",
        "                 splits = RandomSplitter(valid_pct= 0.15))\n",
        "dsets[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "8YVPn3FzUr7_",
        "outputId": "189b7788-9de4-40af-8dd4-72adbfc3992c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-91-ddd30f504078>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m                   \u001b[0;34m[\u001b[0m\u001b[0mColReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Pawpularity_scaled'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mRegressionSetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m                  ] , \n\u001b[0;32m---> 12\u001b[0;31m                  splits = RandomSplitter(valid_pct= 0.15))\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mdsets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, items, tfms, tls, n_inp, dl_type, **kwargs)\u001b[0m\n\u001b[1;32m    326\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtfms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_inp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdl_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdl_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtls\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtls\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mTfmdLists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mifnone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtfms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    329\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_inp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mifnone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_inp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/basics.py\u001b[0m in \u001b[0;36mmap_ex\u001b[0;34m(iterable, f, gen, *args, **kwargs)\u001b[0m\n\u001b[1;32m    696\u001b[0m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 698\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/basics.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    681\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_Arg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m         \u001b[0mfargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Arg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpargs\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 683\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    685\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/foundation.py\u001b[0m in \u001b[0;36mmask2idxs\u001b[0;34m(mask)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;34m\"Convert bool mask or index list to index `L`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m     \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0mit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'function' object is not iterable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Applying some important transforms \n",
        "tfms = [ToTensor() , Resize(448 , method = ResizeMethod.Pad) ]\n",
        "gpu_tfms = [IntToFloatTensor() , Normalize()]\n",
        "\n"
      ],
      "metadata": {
        "id": "TAZQVqqsUtid"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a dataloaders \n",
        "dls = dset.dataloaders(bs = 64, after_item= tfms , after_batch =gpu_tfms)\n",
        "dls.show_batch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "lDBBOCoiW45-",
        "outputId": "1ed3bbe0-7057-4b65-d28f-917ad76dd285"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-39-80c3f2751b17>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Creating a dataloaders\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataloaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mafter_item\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtfms\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mafter_batch\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mgpu_tfms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/core.py\u001b[0m in \u001b[0;36mdataloaders\u001b[0;34m(self, bs, shuffle_train, shuffle, val_shuffle, n, path, dl_type, dl_kwargs, device, drop_last, val_bs, **kwargs)\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mval_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'val_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m         \u001b[0mdef_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'bs'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'shuffle'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'drop_last'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mdrop_last\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'n'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'device'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m         \u001b[0mdl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdl_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdef_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m         \u001b[0mdef_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'bs'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mval_bs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mval_bs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'shuffle'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mval_shuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'n'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'drop_last'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m         dls = [dl] + [dl.new(self.subset(i), **merge(kwargs,def_kwargs,val_kwargs,dl_kwargs[i]))\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dataset, bs, shuffle, num_workers, verbose, do_setup, **kwargs)\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mnm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_batch_tfms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m                 \u001b[0mpv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Setting up {nm}: {kwargs[nm]}\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_one_pass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastcore/basics.py\u001b[0m in \u001b[0;36mfirst\u001b[0;34m(x, f, negate, **kwargs)\u001b[0m\n\u001b[1;32m    553\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfilter_ex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnegate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnegate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 555\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/load.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    107\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbefore_iter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__idxs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_idxs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# called in context of main process (not workers/subprocesses)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_loaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfake_l\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfake_l\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    559\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 561\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    562\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     37\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/load.py\u001b[0m in \u001b[0;36mcreate_batches\u001b[0;34m(self, samps)\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mo\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_item\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunkify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mnew\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/load.py\u001b[0m in \u001b[0;36mdo_batch\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot index an iterable dataset numerically - must use `None`.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfa_convert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprebatched\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbefore_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    145\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mone_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/load.py\u001b[0m in \u001b[0;36mcreate_batch\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot index an iterable dataset numerically - must use `None`.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfa_convert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprebatched\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbefore_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/load.py\u001b[0m in \u001b[0;36mfa_collate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m             else default_collate(t))\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/load.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m             else default_collate(t))\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/fastai/data/load.py\u001b[0m in \u001b[0;36mfa_collate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m     49\u001b[0m     return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[1;32m     50\u001b[0m             \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             else default_collate(t))\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdefault_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtransposed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_collate_err_msg_format\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'fastai.data.block.TransformBlock'>"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dset.dataloaders??"
      ],
      "metadata": {
        "id": "-8LUW2pyW5vL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "QuB6qX5lZpsD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}