Logo_dec.ipynb

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            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "9ba191ed00214c1a87293a98ace11853": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "state": {
            "_view_name": "OutputView",
            "msg_id": "",
            "_dom_classes": [],
            "_model_name": "OutputModel",
            "outputs": [
              {
                "output_type": "display_data",
                "metadata": {
                  "tags": []
                },
                "image/png": 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          "model_name": "OutputModel",
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            "_view_name": "OutputView",
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            "_dom_classes": [],
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            "outputs": [
              {
                "output_type": "display_data",
                "metadata": {
                  "tags": []
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            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": "0 0 auto",
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "58cd0f0e2324474ba5a6409c6598a6de": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "state": {
            "_view_name": "OutputView",
            "msg_id": "",
            "_dom_classes": [],
            "_model_name": "OutputModel",
            "outputs": [
              {
                "output_type": "display_data",
                "metadata": {
                  "tags": []
                },
                "image/png": 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qIi8vD6UUWmuqzlWxceNGjhw5wpFDhzh29BhHDh8j6bgIS/GTn/+UocOGsnfv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\n2pe1a8EYG4PjYWK8gJ5J4TbBjl+tZt+D6xHxIDmdJN2/OJn2k3uANJx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                "image/png": 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Dt5OLTb/9QjBNLEXGl5WF6nSh636mTH2Hnj2eonRCIrqhoyXEI2Hhz8pC9/mQHSqK2wmA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\nYNWqVXTv3p2hQ4dG7bswoNA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SqbsY2yjCaJKqkb9JODCEqCjDOI6mY9XYdU0bikKED45PHj56PJ1uTjc3QlkhpqjaRTWz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\n8OY7R/uPf/nL3Rd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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/Cybertocks/Logo-dec/blob/master/Logo_dec.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlewI4wiMPbZ",
        "colab_type": "text"
      },
      "source": [
        "We'll be building a logo\n",
        " discriminator detector using our own custom created dataset. We are also going to host this on the server so you can show it off to your friends"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "us4kHYmgOK0b",
        "colab_type": "text"
      },
      "source": [
        "<!-- The 3 types of monkeys that will be considered are gorrilas, chimpanzees and bonobos because why not?!! -->"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76XGKNoYTmzF",
        "colab_type": "text"
      },
      "source": [
        "We'll be making use of fastai for solving this problem so we just install it from pypi using pip"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P28GXun8TzpM",
        "colab_type": "code",
        "outputId": "2af46271-fcb9-4dc9-c9c0-197b012e7e5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!pip -q install fastai2"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K     |█▉                              | 10kB 29.4MB/s eta 0:00:01\r\u001b[K     |███▋                            | 20kB 6.6MB/s eta 0:00:01\r\u001b[K     |█████▍                          | 30kB 9.3MB/s eta 0:00:01\r\u001b[K     |███████▎                        | 40kB 6.1MB/s eta 0:00:01\r\u001b[K     |█████████                       | 51kB 7.4MB/s eta 0:00:01\r\u001b[K     |██████████▉                     | 61kB 8.7MB/s eta 0:00:01\r\u001b[K     |████████████▋                   | 71kB 9.9MB/s eta 0:00:01\r\u001b[K     |██████████████▌                 | 81kB 11.1MB/s eta 0:00:01\r\u001b[K     |████████████████▎               | 92kB 12.2MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 102kB 9.8MB/s eta 0:00:01\r\u001b[K     |███████████████████▉            | 112kB 9.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████▊          | 122kB 9.8MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 133kB 9.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▎      | 143kB 9.8MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▏    | 153kB 9.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 163kB 9.8MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▊ | 174kB 9.8MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 184kB 9.8MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Tj20X7xO3cX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "37b91c8b-c2e7-4de2-a83a-3532ac891ae0"
      },
      "source": [
        "! pip -q install azure-cognitiveservices-search-imagesearch"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K     |███████                         | 10kB 30.9MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 20kB 7.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 30kB 10.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 40kB 6.6MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 51kB 4.4MB/s \n",
            "\u001b[?25h\u001b[?25l\r\u001b[K     |████                            | 10kB 42.6MB/s eta 0:00:01\r\u001b[K     |███████▉                        | 20kB 51.8MB/s eta 0:00:01\r\u001b[K     |███████████▉                    | 30kB 62.3MB/s eta 0:00:01\r\u001b[K     |███████████████▊                | 40kB 68.9MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 51kB 16.3MB/s eta 0:00:01\r\u001b[K     |███████████████████████▋        | 61kB 18.8MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▌    | 71kB 20.9MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▌| 81kB 23.0MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 92kB 9.6MB/s \n",
            "\u001b[?25h\u001b[?25l\r\u001b[K     |████████                        | 10kB 34.1MB/s eta 0:00:01\r\u001b[K     |████████████████▏               | 20kB 45.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████▎       | 30kB 55.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 40kB 6.9MB/s \n",
            "\u001b[?25h\u001b[?25l\r\u001b[K     |███████▏                        | 10kB 47.1MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 20kB 53.6MB/s eta 0:00:01\r\u001b[K     |█████████████████████▌          | 30kB 63.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▊   | 40kB 69.8MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 51kB 9.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 10.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.3MB 28.0MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y632A9bTsjGq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2a075e07-0360-4293-ae14-a0b26390e2e2"
      },
      "source": [
        "!pip -q install voila\n",
        "!pip -q jupyter serverextension enable voila --sys-prefix"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ERROR: unknown command \"jupyter\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-t9tyUD5YDk",
        "colab_type": "text"
      },
      "source": [
        "! pip install nbdev"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKcEXSKDT3wV",
        "colab_type": "text"
      },
      "source": [
        "import the library into the notebook. We'll be importing everything in the vision module"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KE5R6IDiT6gc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai2.vision.all import *\n",
        "from azure.cognitiveservices.search.imagesearch import ImageSearchClient as api\n",
        "from msrest.authentication import CognitiveServicesCredentials as auth"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvkXG1ccO_VI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# helper to download images\n",
        "def search_images_bing(key, term, min_sz=128):\n",
        "    client = api('https://api.cognitive.microsoft.com', auth(key))\n",
        "    return L(client.images.search(query=term, count=150, min_height=min_sz, min_width=min_sz).value)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iV5j9svOzip",
        "colab_type": "text"
      },
      "source": [
        "## Building the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnZj_XaMO2XY",
        "colab_type": "text"
      },
      "source": [
        "We'll be making use of Bing's search API for this because it is very straightforward and easy to use. There are also other ways to build custom dataset using web crawlers on other search engines but they won't be discussed here."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v__9fRANRf_g",
        "colab_type": "text"
      },
      "source": [
        "Visit this [link](https://azure.microsoft.com/en-us/try/cognitive-services/), navigate to search API, on Bing Search APIs v7 click on get API Key, a modal will pop up where you can choose to try the service as a guest user, accept the terms and conditions, sign in with an option of your choice, wait for some seconds and boom! all your Api keys for the Bing Search will appear on your dashboard. Copy the one that ends in '/images'. That is our API key. Put the API key in the variable API_key as shown below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qwhSZd3PLvO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "API_key = '703df11947c143e7ab7ef1e129e23548'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGpWcFk3TheX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cHqxaqg-Vfu9",
        "colab_type": "text"
      },
      "source": [
        "Let's test our function out"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgdwvx1rXXai",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url = 'https://th.bing.com/th/id/OIP.h2Gc3aUYlLD2E-U7TMYnqAHaFS?w=290&h=208&c=7&o=5&dpr=1.25&pid=1.7'\n",
        "puma_url = 'http://orignallogo.com/wp-content/uploads/2018/01/Puma-Logo-Design-Vector-Free-Download.jpg'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SiIwlrG4XdE6",
        "colab_type": "code",
        "outputId": "d9111da7-1aa8-4ae6-9c0f-cf7515a06170",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# this will download at most 150 images of size 128, 128 for us\n",
        "res = search_images_bing(API_key, 'nike logo jpg')\n",
        "imgs = res.attrgot('content_url')\n",
        "len(imgs)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "150"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6aWvMHAbG1M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "base_dir = Path('/content')\n",
        "(base_dir/'images').mkdir(parents=True, exist_ok=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZeHhtKGcCHy",
        "colab_type": "text"
      },
      "source": [
        "Thankfully fastai has a method `download_url` which we can use to make requests to a single url and download it's content into a file in `dest`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmmUmE_aaIce",
        "colab_type": "code",
        "outputId": "1127ed01-0c45-4eeb-da26-e97b797b4c0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "dest = 'images/nike.jpg'\n",
        "download_url(url, dest)\n",
        "dest = 'images/puma.jpg'\n",
        "download_url(puma_url, dest)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Zaq3DypaZPd",
        "colab_type": "code",
        "outputId": "388dc108-8e9d-4174-8bd4-9db7c442f071",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "img = PILImage.create(dest)\n",
        "img.to_thumb(228)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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IOVZHVVWFGQGKNJoBEYINhK8Hn89NGhRrjoDqXhkiOTQdqGji4hWVGCaDu1iMxAgOAqhQ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\nRK5CYII=\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=228x136 at 0x7F5CCFE8F8D0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPGHHUgFb4ep",
        "colab_type": "text"
      },
      "source": [
        "It works!!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5N6QPGYNclFp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SAIH00jc0Gz",
        "colab_type": "text"
      },
      "source": [
        "We now make use of another fastai method called `download_images` to download multiple images and store them in a folder called `dest`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WpzuTHjndCqv",
        "colab_type": "text"
      },
      "source": [
        "Defining the folder names for each of the classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rX0MwCIudIzS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "folder_names  = ['Nike', 'Puma', 'Adidas', 'New Balance', 'Vans', 'Reebok']\n",
        "path = Path('Logos')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcxI9SkreWyN",
        "colab_type": "text"
      },
      "source": [
        "Lets download the full dataset by querying bing continuously"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HHmMk0Zidegr",
        "colab_type": "code",
        "outputId": "8e2f331f-8b8d-4bff-899c-88fcc1887b14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        }
      },
      "source": [
        "path.mkdir(parents=True, exist_ok=True)\n",
        "for nm in folder_names:\n",
        "  if not (path/nm).exists():\n",
        "    dest = path/nm\n",
        "    dest.mkdir(exist_ok=True)\n",
        "    res = search_images_bing(API_key, f'{nm} logo')\n",
        "    download_images(dest, urls=res.attrgot('content_url'))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Download of http://mitsloan.mit.edu/sustainability/images/large/Nike.jpg has failed after 5 retries\n",
            " Fix the download manually:\n",
            "$ mkdir -p Logos/Nike\n",
            "$ cd Logos/Nike\n",
            "$ wget -c http://mitsloan.mit.edu/sustainability/images/large/Nike.jpg\n",
            "$ tar xf Nike.jpg\n",
            " And re-run your code once the download is successful\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AhgduqCkwKo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# shutil.rmtree(path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXabPADgee94",
        "colab_type": "text"
      },
      "source": [
        "Extract all the image files that have just been downloaded from the grandparent `bears` folder using the get_image_files. It looks into all the folders with recurse and returns and `L` list type which is a normal python list but supercharged"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gYrCzVBew3H",
        "colab_type": "code",
        "outputId": "7e712d3e-95e9-44d1-bbec-20b79c3ca6fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "all_logos = get_image_files(path)\n",
        "all_logos, len(all_logos)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((#888) [Path('Logos/New Balance/00000118.png'),Path('Logos/New Balance/00000025.jpg'),Path('Logos/New Balance/00000106.jpg'),Path('Logos/New Balance/00000031.png'),Path('Logos/New Balance/00000040.jpg'),Path('Logos/New Balance/00000080.jpg'),Path('Logos/New Balance/00000044.jpg'),Path('Logos/New Balance/00000128.png'),Path('Logos/New Balance/00000028.png'),Path('Logos/New Balance/00000146.jpg')...],\n",
              " 888)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwUC3uape94H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNFAxKi5glS4",
        "colab_type": "text"
      },
      "source": [
        "While downloading the images, some images may have been corrupted/broken so we have to check to see if there are any of such images and remove them from our dataset. Luckily fastai has the `verify_images` method which looks into the image `L` list and selects all the ones that are broken or can't be opened and stores them in a list object"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgZ4Dzq9hHdM",
        "colab_type": "code",
        "outputId": "821c6774-de32-49de-f70f-513e1a6c1bce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "broken = verify_images(all_logos)\n",
        "broken"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#24) [Path('Logos/New Balance/00000041.jpg'),Path('Logos/New Balance/00000060.png'),Path('Logos/New Balance/00000107.jpg'),Path('Logos/New Balance/00000093.png'),Path('Logos/New Balance/00000088.png'),Path('Logos/New Balance/00000019.svg'),Path('Logos/New Balance/00000020.png'),Path('Logos/New Balance/00000075.jpg'),Path('Logos/Vans/00000129.svg'),Path('Logos/Vans/00000108.svg')...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9dR9SnwhTR4",
        "colab_type": "text"
      },
      "source": [
        "As we can see, we have 10 broken images. Let's try to open one and see what happens"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ucFlkFAChl8y",
        "colab_type": "code",
        "outputId": "317cac25-3b8b-4a11-c3e1-322b9aa3cb1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "PILImage.create(broken[0])"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "error",
          "ename": "UnidentifiedImageError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnidentifiedImageError\u001b[0m                    Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-43-f4bf26a2d354>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mPILImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbroken\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[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai2/vision/core.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(cls, fn, **kwargs)\u001b[0m\n\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\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     97\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mload_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_args\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\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/python3.6/dist-packages/fastai2/vision/core.py\u001b[0m in \u001b[0;36mload_image\u001b[0;34m(fn, mode, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\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     73\u001b[0m     \u001b[0;34m\"Open and load a `PIL.Image` and convert to `mode`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m     \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\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     75\u001b[0m     \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\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     76\u001b[0m     \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim\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/python3.6/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2860\u001b[0m         \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2861\u001b[0m     raise UnidentifiedImageError(\n\u001b[0;32m-> 2862\u001b[0;31m         \u001b[0;34m\"cannot identify image file %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mfp\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   2863\u001b[0m     )\n\u001b[1;32m   2864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mUnidentifiedImageError\u001b[0m: cannot identify image file '/content/Logos/New Balance/00000041.jpg'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hyk5wTiiqlP",
        "colab_type": "text"
      },
      "source": [
        "See? I told you it was broken. Look at the Exception message. The Image still exists in the directory but it is broken"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epsvSNLJhr-i",
        "colab_type": "text"
      },
      "source": [
        "Remember I told you that `verify_images` returns an `L` list type. This `L` list type has a `map` method which you can use to recursively perfrom operations on the elements in the `L`. The operation we want to perform on this `L` type object is to go into it, pick out all it's elements and use them on any function in it's `map`. The function in the map of the `L` has to be something that can remove the image paths are broken from their parent paths. All of these can be done using an `unlink` method on the grandparent path"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3cvONCRixJF",
        "colab_type": "code",
        "outputId": "729ef52c-6774-4c8f-c3d6-54e3051c9d3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "broken.map(Path.unlink) # unlink is a pathlib.Path method"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#24) [None,None,None,None,None,None,None,None,None,None...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrnDSOrQkOIM",
        "colab_type": "text"
      },
      "source": [
        "let's check if it worked"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nN0yns2cqcZ_",
        "colab_type": "code",
        "outputId": "a7845303-3b59-47ab-83a0-7a268c5bcd8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "PILImage.create(broken[0])"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-45-f4bf26a2d354>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mPILImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbroken\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[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai2/vision/core.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(cls, fn, **kwargs)\u001b[0m\n\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\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     97\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mload_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_args\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\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/python3.6/dist-packages/fastai2/vision/core.py\u001b[0m in \u001b[0;36mload_image\u001b[0;34m(fn, mode, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\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     73\u001b[0m     \u001b[0;34m\"Open and load a `PIL.Image` and convert to `mode`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m     \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\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     75\u001b[0m     \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\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     76\u001b[0m     \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim\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/python3.6/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2807\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2808\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2809\u001b[0;31m         \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\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   2810\u001b[0m         \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/Logos/New Balance/00000041.jpg'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8U5xaJJcrQ6u",
        "colab_type": "text"
      },
      "source": [
        "As we can see from the Exception(error) message, it's been that image has been removed from our directory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiZDKj8-kfwn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBijf7FrlBLn",
        "colab_type": "text"
      },
      "source": [
        "## Making a DataBlock"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klqNbCTTn7Lo",
        "colab_type": "text"
      },
      "source": [
        "We will be using the midlevel datablock API"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L0jD87DuoE9F",
        "colab_type": "text"
      },
      "source": [
        "We will need some transforms for the items in the CPU and the batches in the GPU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXFAV4J9aAKS",
        "colab_type": "text"
      },
      "source": [
        "helper util to remove `.gif` files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBj6m_8waGYi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def remove_gif(path):\n",
        "  x = L(fn for fn in path if (fn.suffix == '.gif'))\n",
        "  return x.map(Path.unlink)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHYnmiZRcTME",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1fd999b0-b8e8-4e94-c887-6b22838011dd"
      },
      "source": [
        "# Run once\n",
        "# remove_gif(get_image_files(path))\n",
        "# for fn in get_image_files(path):\n",
        "#   if fn.suffix in ['.jpg', '.JPG', '.JPEG', '.jpeg']:\n",
        "#     PILImage.create(fn).save(fn)\n",
        "#   elif fn.suffix in ['.png', '.PNG']:\n",
        "#     im=PILImage.create(fn)\n",
        "#     if 'transparency' in im.info.keys():\n",
        "#       del im.info['transparency']\n",
        "#     im.save(fn)\n",
        "#   else:\n",
        "#     print(fn.suffix)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".tif\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-O3fnr3ZoO1O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# item_tfms = RandomResizedCrop(448, min_scale=0.1)\n",
        "item_tfms = Resize(224)\n",
        "# make use of the defualt augmentations\n",
        "# `mult=2` doubles the values in the `max_rotate`, `max_lighting` and `max_warp` \n",
        "# transfroms of the `aug_transforms`\n",
        "batch_tfms = aug_transforms()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "elbLGs8Pmm3Z",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KGHskxHKojTf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = DataBlock(blocks=(ImageBlock, CategoryBlock), \n",
        "                 get_items = get_image_files,\n",
        "                 splitter = RandomSplitter(valid_pct=0.2, seed=42), \n",
        "                 get_y=parent_label,\n",
        "                 item_tfms=item_tfms,\n",
        "                 batch_tfms=batch_tfms)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jiv8BurTsu7H",
        "colab_type": "text"
      },
      "source": [
        "Remeber that the DataBlock is just as architecture of how we want the data to be fed into the model. We now have to pass in a source to the data that we have into the datablock. It is important that the source passed in can be properly consumed by the get_x (get_item in our case). Since the function there is a `get_image_files`, we have to pass in a the gradparent directory for the images paths to be extracted from it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCtCeG-ttaS1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dls = data.dataloaders(path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADFIlE8auN8K",
        "colab_type": "code",
        "outputId": "c48f249a-6fff-4618-e89b-41bda0a95b66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        }
      },
      "source": [
        "dls.show_batch(max_n=9, nrows=3)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JhS9sMvRqDwKvpEywwq2IjUFYa9TMDtzEDIWzpALGG2AoB0ogKEv2lFI4pbFK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\nfJ5Prt2knNPwdUA9l+6+xvAnfxKjltZwSFKOSNwYq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76W4Nrr9pa/9hPwe41nP7MMgoVV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\n59HuNsoku+fCDojgFNLtDfKVpUEDoEFVyqjtroXtq4rxjiQv01pD0itSW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QAAIABJREFU\n88Vgs50N5UYhqvtv5CIPvoG7/EFd/7Dbgj049jr2oKGHOwH738id2HmuO0WXosUUCoU/WfG5BkiA\nRvF7L2Vmr9DGDUahr77l8kpvNMEJrXa17JI4INcpl+kIl9hoEI4Qv3Sq5N6Dc0ieQq8L3Q66uYGs\nrcPKGtnVRXR5FRvHSL2JT2Lc5Bj+2GGYmobxKRgfh0YLiRMSMcTGIJFFbPDHFP6IHWyrFd4cyBtR\nd/kG4HkTaV09EUImobtcL2mS3/c4tYffRTw7Q9SoY0Xw4oJFn4Nmffwrz+G+8BnizjbxIGY8rJQP\nfr9Q8eEBF8Vsv/t9xO9+AtesUadJZADvqKUZkma4PEPTlKzTJu22ke02cbuH7fdJsz6218VsrxHn\nXcJ6CR0z1YR5rZElT2K0XodWoJDuz85gjh2lefIY0ZE57OF5ovkjRDMzmLExkrFxJE4GfSJKFA6P\nHV66YaVBeYxHyQPrbi7QTclXNum8cg737Iv4rz9L/vKL6KVXSa6t4/sdrGYQCWlcA+dJekEBiEwt\nsA26DEEKM0ppW4NrtuB9f57453+ayQ98mNr4BHGRZ1CO1avis5zO+jbpC+fhM1+k/eTXMC8+D6vn\nqXU7wc2PJ/GKExcYFQFvfAhdaDgnq55IFYvQx+NVqBNo3oNnIVgzxzTfcx6/iQrAjbDf17xe4X+Q\n77jTsNd53Ojchkt9GGfyDAV/+bvkDagBda4vOHu7KgAULXd3pv+Uvwf6fenHLwS+76Vk7S10exM2\nr8HyMn5zE+33kTRFoxiZnsGPjyPjEzA1jR8bx9UbECdEUYK1MdaEGKeBAXPe8FLv2NUqwf8W4HZR\nAJ6KYk280lBDLgwT5CQmkghvQkmpERD1eFUiFayCakbks2JeB9V26OOTQfCvTAR0RunbOmosiYDR\nCDWBG8CYYk5q6OGgEoISiIYy0qhGHsW4OCJtWGyjBo0m+dg4dmqGZHoaDk1hDx0impsjPjJPNDdL\nNDeHjo2jUUQUm8Ka14JRL4zYSrSDR394CuVKBdQMlHVVT+5B8xztdEmX1uhduEr24ln0mW/SffYs\njQuvoFvL2P4W1oWwaL9RIzt8hOjMA0SnT5Hn0P3Ws7Se/gbJ1hYRgeXSu5Rci1wFEToTE2Q/8H2M\n/cxPM/HYE0RRDSNKJMGLCA6fO3pX1+l+6Wm2fusPqT35BdLFs5h+m5rmYCw+icnGx9GZGezcPPnM\nFDI+hrFxaOW8uU22ukR+dYFoYRnT2aLmdVDmHRd7/mgruWOqe87j2yTr69sd4397oHRJ78TeYZLR\ntBy9znMgDPkDIsK0CfSZhY7KToF/W+yBbzjCMlG8jjQ8Kaz7obDPIHf4fg+/3UU7Ka7TI12/BuvL\nxKYPtTokCZy8F8bGoDmGabYwrWYg+YljMAZrLDUJXbrikFowKK+/Lj3v7XnJK9wiaj/wV+i3t/Eb\nW+jWJmmnh81S6s5BnuN8cNGbYtqqd3jncQKooU8NIxCLKRrghHUeAoShTNjbUqgDYrAGchuq4aMo\ngaRGWqvjkwSX1EP73maMadYxYy1kcgozM4OZOUQ8M0tjepxoZpJoegYZH0daLYgtJooCTbAtkvEw\nqLUFEZfDSkl9Dogb1N6XrAVBlpXpizDMWQL1Hs09vpuRLV8jPX+F/MWX0G+do//iN+DSOezyKnF7\nHZuDigslvNNTbJ04RPLwO5En/hz1dz/O2H2nqY1P0ncZtXPn6P2zf072//42rCzi1Q58pg5oT43T\n/6G/xOzP/wLJfQ9ha8mgzDpTh+SC29yg/eXn6X3q36B/+hmShVeRtB24XZOY/qE53MMPYJ94jOZj\n78Hec4r40BwyPoat10NLbRTX7+O2N+m/eoH2v/sC7td/Hb18iS554A0ZqBvDZvP74TbxAFQ4CGTk\n/4NimOm/uzphFCG2tLPJUMkNMCQUejt5AMqnnSpp2RxEPSbL0V4KaYb0+mg79D3Pen18FjjyTb2G\nxhGaCL4eQyO0srVR6HBnojiQrQg7a7TLpilFstSO+/mWX6kKe+F28QBsXb2srpNiXY7zGXk/I9+8\nhm6soZvb0EnxnT5pd5Osl0LewzgJne/SXigX9DkigiPGSkQUWbIkJJdaQCOLMTE2qiFRTNSMQ5Od\npI6t17BJAkmMrzeQxhi21SKaaGHHGthWE7FxSN4rGSOh0HDNYN6ryID6txROOvA+BG9beN2MrJVh\nOZ4WHTlF0/BaDj5PcVs9/NIWev4y2bmL9F86R/+FF2i+eha9tojrtSHvYLzHG0OeWNzkDHrmNPrE\ne5h65xOYM6eIT53CTkxgbYSVCCzk6tA+bFy4QPuX/gnJv/y/qfV6gT4YIU3qbP3ojzDxD36R1snT\nYC01KxhylIw8M/QvrdP51d8i+81/hT33HLVsi9xAZ6xF75H7ib7zw9Q/9jFaj7yT2tgUcRyDDYnI\nJc122KOC1zIXRb2Qt9ss/2//B/aX/leSzSUER1Ls9b4IHmYCx7y7nT0AFb792J0YWKKstkjY2Vr4\nZj5vN25fvXFUH1D1uDTFdLtI7jC5Iv2UvJcGhj5r8a0WTEVE1kJhudgoxkQWbwRnBGtCg46ShWFU\nsMvw4RB7Plmhwv5ozM+DMghUlal7SBDy3nu8d+RZGix8Y4AckVDXJs4UpaQFCQ+mIK0KwtqjGB9C\nAyrBgSziUJNgTYIxBhVfJL9JcLUXTJGBD+P6xOHrpr2U8nynZ1EK1r8ybm2g8FAE40O9R52n33P4\nfob2eui1LfIri7hzF9CLC2QXF5DzF+HSRczmClFnA5un9I3iYwPjCW7mOPb4SaKHz2De9Sitx99N\ncuIEdmKcJKmHSzZQ2M1gfJFAL4ba8Xvo/7V/n/wLXyJ56RvBxS7C5ql7GPuZn2LsnhN467HeYDQI\n4LwHnS8+w8b//E+pff7fUd9aQvH0xsfI3/Uu4r/0/Yx97CMkp+8lHptEjBBJyThY3Gcp9xIZJCMZ\nDXHDZGyCie/9Prq/8W/hueVAE80whd4Dmd1fzFcKwB0EHfm/xHWu4+ve8VrCuHxfUSEwKL0cLsDr\nkzFfS/iX0+/2haqiXiHNsakLnNq1CG0U/citRSMLtuTAD25ISyHoC9N+aN2/dedS4e0Pgx2yMevO\n1aXWYKzBqEXiwgEsIcteUMSH3IBSwpldVLwDhVRHZHMRIhhliBIR0JgdufYilDwAIrLHqh9VuHc/\nYMdrHoPzPuTlZBl+q4eub5BfXcFdXKZ/7gru0iXclUvkC1dhbYWovYHNc0TBekfqU3LjSMcj3PQR\nsntPUnv0ndgH3kHrvnupnTyFzByC1hhRHGElhObCeYacAxHZMURRIVKFWsTEYw+x8YEPkZ17iSjt\nIEB/fp6pk8chqlGTUDOUq+LaKZt/+GWy/+l/p/m1zxJlm3gL6YkTuB/4flo/9hM0H3qEuFnDFF00\nh3uKXLfrDqL4xRbrAe892le8sUWAZNgjNkfJI0vn3hPXXe8SN6EA3OoOd3sLgjsdusejmy8WK6da\nqcX7Xc/djEC//YX/ACJIo0a/HoMYxBTEI+XiGxHye7Vl2vmgQoVvH0QlcFbo9Su+fCqs1oihYLfB\neW4LJ7oUgSeRkaqWgewLBEI7IlJm9A/KxaA7nxw81CKpdhRDhsJw2CD/IHeQOXynS77ZJV/fJr22\nTrq0CFdWyC9fhUsXkeUr5Osr6FY3dO5LO4jLQDW0O08a9KbryMwsHDtK9o7jtO6/l9r99xEdP010\neI641sBHNnjxrBkM2o8YKyUjqEdxecpmu8vi8jrGGE6fPEocxVgRkulJ2h//C6S//7vYxS6qkCyv\nYlY2iQ4fDVwDxpNt9Wj/7p/S/8f/mPpzX8dkbdJaxMa7H2Ps7/4CMx/9MPHkLJFYfJlgOXIxr99W\nymtbJHV7wTtP78oyvd/4baILr1BXF+q7Ch4TNYI7fZLWz/7U9ROqwLeZCnjvBLWbe3+FNx67cwF0\n5MePHDP68/a7F6ExSEwsIXRZEvntaGtbCfgKtwFy54I73xRhLF8I8sJTpyIFKZWMMFYqKkMGi2Bl\nl/x6w1U9FNpD14KOJNZJEYcvUbJmDN6lhaD3vhiboupCO/LUoVlO3uvDZo/8Whe/vkm+uEi+vIhe\nXkAXVnCLK+SbK8jWOvV2nyTdQrUflBITkycxeTPCH56FuaPI0VPY08eJTp2gduQQ9fkjyMwhZKJF\n3GwgSRIWtRVMkbAnEkoeKfoQWC3P2ZO7nO1un8tXV3nm+Vf4wpee4umnnuLeE3P80n/9n3J4bj5c\nAxsRv+dR2mdOU1u6ilGPWVxg+7mnqZ85jbUxPle6X3yWzj/9v7DPfYU8ayMCWw+fofHJTzL1ke8m\nSSKMkYK8ybC30bZT2QvsgOEeOwe9i8ts/cq/wv/2vyTeWgKECKUHOGvoHT3MxM/9R0z98N/Yd14d\nUAG41V3woO/bT7hUysPrx17XYWcTz50tQWTX7/0+485GGVcLLv0bxOsrVLgNsPKHnyeOE+xkAxp1\nqNexrYSomaBxDfXBFS/GEMVRCFlZGbjlFcUVSXQGj4gdqPyiGrwLgLpCcaAIkSmhY6QU8Xjn8WmG\n76doL8X3+mg/RTs9XLeL72bk69fQa9fQ5XVkdQNdWyVbX8CsbeA32mi3je93iF0OzuNQrHh84siS\nOtuz49jJKez8NMzNkxw5Tu3YYeyJeeIjR4jmTxBNTmObtQEFrjemINrZXdA70p+juA7qHVmudPp9\n1jY3uLK4wrdeOMfTTz3HU1/9ChdffYFry0u4rMfLszN8/a/+ZT72vfOBZtlA6/gJOh//BL2nvkVr\nc5X69gad/+d36b7nPXDsBJ1XLrP5y/8n0df/BJt38DgQC+98lJn3f4C4FhOJUJB7FrkdAXtZ/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VnvZG4MZX8XafAxUqVKhQ4bVww26AFSpUqFChQoW3J6ocgAoVKlSoUOEuRKUAVKhQoUKFCnch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W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DiI3x+g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\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iKLFOS5K4BaN/fh6ZL5g5vt71KB2h1hEJXcNIp80ZPRIiEcshabG8lMhMBlEqtSsA2mij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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mo4Zilo5uS9k",
        "colab_type": "text"
      },
      "source": [
        "Everything works fine"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PvxfCKl5pkFd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRoFgVnou6Hc",
        "colab_type": "text"
      },
      "source": [
        "## Building the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAHvEjkJvBwQ",
        "colab_type": "text"
      },
      "source": [
        "This is the exciting part"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJiRglVIvI4V",
        "colab_type": "text"
      },
      "source": [
        "We'll be using a resnet34 to train wit transfer learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DhJzE6rvFpn",
        "colab_type": "code",
        "outputId": "81673286-83bf-4643-929c-1575cd1ebf97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102,
          "referenced_widgets": [
            "793d26015dd54f24a0ae6522e142322f",
            "f332d1a40d934ee8a3b52a92a7bf0cb9",
            "c3f55904b691475687bfde3f9934f992",
            "281cc7544c824e49ad31529633687b14",
            "6f09b4739d914a678720f4ededc59fd6",
            "11f04e0c1b764ae896f2e75721e81053",
            "8dacd12fa1f144f0b4470511467955c9",
            "336e2395fba94dc1ad4193d4c397f763"
          ]
        }
      },
      "source": [
        "learn = cnn_learner(dls, resnet34, metrics=accuracy)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/checkpoints/resnet34-333f7ec4.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "793d26015dd54f24a0ae6522e142322f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=87306240), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVpnGd2fvV43",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "1859262d-f033-4930-db17-80d540f3a8b4"
      },
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(lr_min=0.017378008365631102, lr_steep=0.0012022644514217973)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g3CGs31rEPe9qHut9df8Hyxh77yeBbFsFQkSiZli3Nlw4qjvPzfiaOV9vrXkF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\n6/8Dy9dixUIbF0UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mnk40edevdCm",
        "colab_type": "code",
        "outputId": "63ea5a81-4f30-4f52-a527-6320313cbb05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "learn.fine_tune(4, 1e-03)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>2.653596</td>\n",
              "      <td>1.054905</td>\n",
              "      <td>0.614458</td>\n",
              "      <td>00:19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.185148</td>\n",
              "      <td>0.567876</td>\n",
              "      <td>0.813253</td>\n",
              "      <td>00:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.919052</td>\n",
              "      <td>0.393258</td>\n",
              "      <td>0.861446</td>\n",
              "      <td>00:19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.741261</td>\n",
              "      <td>0.281362</td>\n",
              "      <td>0.885542</td>\n",
              "      <td>00:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.622553</td>\n",
              "      <td>0.285281</td>\n",
              "      <td>0.879518</td>\n",
              "      <td>00:19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4RLPVSY3Rx2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nS1PgvD3SE8",
        "colab_type": "text"
      },
      "source": [
        "## Interpreting Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K3S-EpTwwAga",
        "colab_type": "text"
      },
      "source": [
        "Let's see how our model fared on the classificatio problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApGWxJn83bNT",
        "colab_type": "code",
        "outputId": "7203bf2d-bec4-4881-84ee-aecfe72293e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "interp = ClassificationInterpretation.from_learner(learn)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9b4pWV64GZV",
        "colab_type": "code",
        "outputId": "062931fb-1732-4bcb-c568-98a195df2168",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "source": [
        "interp.plot_confusion_matrix(figsize=(15, 10), dpi=50)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vRsQpEfHZIimfhauvmsl9985l0KDBrLPu8KVfYTkxasONmDZ9RukYrXLtHffzuZm3c9z3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\n2JIkVcDCliSpAha2JEkV+P9efXksehhLpAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 750x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6HXqnxcS5vz4",
        "colab_type": "text"
      },
      "source": [
        "the model seems to be having a really hard time differentiating between chipanzees and bonobos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhTfsJLF3248",
        "colab_type": "code",
        "outputId": "702ebca3-7d13-40fd-c437-6c76c50c9266",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        }
      },
      "source": [
        "interp.plot_top_losses(15, figsize=(15, 10))"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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o77jPwd29we/j5/lvUtXduLtsf1fiuHPW4Mbx/WPsei8A5uTt92O4ylMhRfN9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\ng/NrtdaJWutVST6R5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            "text/plain": [
              "<Figure size 1080x720 with 15 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PfnamnUJ4gQZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36o9nbVb4jP1",
        "colab_type": "text"
      },
      "source": [
        "## Image cleanup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvcCEVLr49D-",
        "colab_type": "text"
      },
      "source": [
        "We can use the results from the plotted `top_losses` we obtained above to clean up the dataset we have to enable the model learn better"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-1V7XRM4-UZ",
        "colab_type": "text"
      },
      "source": [
        "Using Ipython Widgets, we can create a customm widget that allows us to cean up the dataset easily"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-7D01KK6cpP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai2.vision.widgets import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gt_JiTra5HGL",
        "colab_type": "code",
        "outputId": "8951be92-7b2e-4a40-c09b-161641747f36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130,
          "referenced_widgets": [
            "05f4cf0a64154357bdce4cfb569f97c6",
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            "14a3d74aac004c8b81407825302f7f63",
            "0157d10ed07b447e95300f82017403bc",
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            "c82ace8d201d4453a598ff97812d3a03",
            "d53db2cccf51439ea171f5394ffaf548",
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            "1988419fc6794ceb9d74eadb258ff400"
          ]
        }
      },
      "source": [
        "cleaner = ImageClassifierCleaner(learn)\n",
        "cleaner"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "05f4cf0a64154357bdce4cfb569f97c6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "VBox(children=(Dropdown(options=('Adidas', 'New Balance', 'Nike', 'Puma', 'Reebok', 'Vans'), value='Adidas'), …"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QS771Cu-6pCS",
        "colab_type": "text"
      },
      "source": [
        "Still doesn't work well in colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtDudWC97e1o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-9BVIC9Z7vCd",
        "colab_type": "text"
      },
      "source": [
        "## Making a web APP"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D2u5dRPe7x7C",
        "colab_type": "text"
      },
      "source": [
        "Export your model parameters, architecture and dataloader as a serialized pickle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcpB34gN8AyC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.export()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iw63GHB78Hqz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = load_learner('export.pkl')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vee-7Jt28M6L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lS6Z5J_R8WAT",
        "colab_type": "text"
      },
      "source": [
        "Mkae a prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pC7-kaHG8XxW",
        "colab_type": "code",
        "outputId": "7697439b-8b44-4e4d-8047-4c32b8188092",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "learn.predict('images/nike.jpg')"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Nike',\n",
              " tensor(2),\n",
              " tensor([4.3680e-06, 1.7363e-07, 9.9998e-01, 1.0972e-05, 6.4961e-06, 2.3327e-07]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZiCCUIlH8dxa",
        "colab_type": "text"
      },
      "source": [
        "We can see the classes  the learner was trained with by calling `vocab` on the learner's dataloader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZ4BR1Xm85H6",
        "colab_type": "code",
        "outputId": "7ee2d2fe-ee65-4eea-bb1b-b7086850f93e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "learn.dls.vocab"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#6) ['Adidas','New Balance','Nike','Puma','Reebok','Vans']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRG3qFbG9DE9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7MvM9yjf9IR2",
        "colab_type": "text"
      },
      "source": [
        "We create something to use to upload the image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftMPTzY79IkM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "btn_upload = widgets.FileUpload()\n",
        "btn_upload"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "089BJgw8mK4k",
        "colab_type": "text"
      },
      "source": [
        "colab just loves ruining the flow heh?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBVqIJbn9JCq",
        "colab_type": "text"
      },
      "source": [
        "We can use a SimpleNamespace to grap the images instead"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "za_4E5BbmTxs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "btn_upload = SimpleNamespace(img='images/nike.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6KXh4Ovmavz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img = PILImage.create(btn_upload.img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OV0a7hS5mk8W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nPqpkQxlmmpm",
        "colab_type": "text"
      },
      "source": [
        "We can use a widget to output it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cv8RZcG0mrVT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "f4f95bdd5ce44a088752de2f998e0d75"
          ]
        },
        "outputId": "67c7c164-93f8-4822-a3b1-16cce1e4b6a7"
      },
      "source": [
        "out = widgets.Output()\n",
        "out.clear_output()\n",
        "with out: display(img.to_thumb(128, 128))\n",
        "out"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f4f95bdd5ce44a088752de2f998e0d75",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zs84FOOnge3",
        "colab_type": "text"
      },
      "source": [
        "Colab!!!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-it9ArinYq9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "6209ba2f-e40a-4045-bf48-b31921cd0f8a"
      },
      "source": [
        "pred,pred_idx,probs = learn.predict(img)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuKhKnSAntG3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvHJjbxNnmPd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c406c28903e54ff0a13f445cc403a7bf",
            "dd541ac22d794a4d8624b3c0c0928da2",
            "1c2caa6407574819acdc258842abf779"
          ]
        },
        "outputId": "27a27c9a-5c87-4f6a-a596-f5db77964d3f"
      },
      "source": [
        "lbl_pred = widgets.Label()\n",
        "lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
        "lbl_pred"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c406c28903e54ff0a13f445cc403a7bf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Label(value='Prediction: Nike; Probability: 1.0000')"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5MSUXwJn6GI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "bbc7cca286704893adbaec657f986ae5",
            "12686a178ead4625963000822cd14271",
            "3ff14be5295b44728adba3efbf85e410"
          ]
        },
        "outputId": "8b16848a-5abb-43bb-f118-43cca9c7b00b"
      },
      "source": [
        "btn_run = widgets.Button(description='Classify')\n",
        "btn_run"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bbc7cca286704893adbaec657f986ae5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Button(description='Classify', style=ButtonStyle())"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icA3OGAPoXCA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsI4LPAlobNx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def on_click_classify(change):\n",
        "  img = PIlImage.create(btn_upload.data[-1])\n",
        "  # img = PILImage.create(btn_upload.img)\n",
        "  out_pl.clear_output()\n",
        "  with out_pl: display(img.to_thumb(128,128))\n",
        "  lbl_pred = widgets.Label()\n",
        "  lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
        "\n",
        "btn_run.on_click(on_click_classify)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZ2RS0V0pF3y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "btn_upload = widgets.FileUpload()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-p_i1wV_pgAA",
        "colab_type": "text"
      },
      "source": [
        "Stack everything in a vertical box"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeZZ5aHspjVl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "2744138c043343269e080c2b68253b50",
            "05cf76603e014feb88d68c4fab23a6d9",
            "c55c91a6b75d4589a72a97de451d5fbd",
            "f68bb1fbc2754610874c720702b27867",
            "bbc7cca286704893adbaec657f986ae5",
            "db00ecef40e0430fbfd8f26ef0628dde",
            "c406c28903e54ff0a13f445cc403a7bf",
            "cc394f67557e46f882f2f525c75216ee",
            "2148e55e69dd4ba592f2c9ae60ce1e82",
            "d3f9ebdea5ea4a82b4151c49398c2629",
            "0f6fa35867f2428aa257075e48848fda",
            "12686a178ead4625963000822cd14271",
            "3ff14be5295b44728adba3efbf85e410",
            "dd541ac22d794a4d8624b3c0c0928da2",
            "1c2caa6407574819acdc258842abf779"
          ]
        },
        "outputId": "31a08dba-907a-4388-aacd-48085a720940"
      },
      "source": [
        "VBox([widgets.Label('Select your bear!'), \n",
        "      btn_upload, btn_run, out_pl, lbl_pred])"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2744138c043343269e080c2b68253b50",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "VBox(children=(Label(value='Select your bear!'), FileUpload(value={}, description='Upload'), Button(descriptio…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTyePQsDpkj7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}