{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WXqA_23DfeC"
      },
      "source": [
        "Modelo de clasificación de imágenes usando ViT\n",
        "=============================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3576kEYCwpp"
      },
      "source": [
        "## Introducción"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-aoxMmlCwpp"
      },
      "source": [
        "Los modelos basados en transformers para visión han demostrado ser ampliamente potentes en régimenes donde la cantidad de datos es suficiente, a pesar de no tener sezgo inductivo presente en su arquitectura.\n",
        "\n",
        "En este ejemplo veremos cómo podemos utilizar un modelo de este tipo para resolver un problema de clasificación de imágenes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TghjzJl0wKId"
      },
      "source": [
        "### Preparación del ambiente"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ez9d-jEjDfeV"
      },
      "source": [
        "Intalamos las librerias necesarias"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "3aeyeq_2FzX6"
      },
      "outputs": [],
      "source": [
        "!pip install -U transformers datasets fsspec --quiet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avCIgEVTCwpt"
      },
      "source": [
        "### Sobre el conjunto de datos de este ejemplo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJ1mR6CBCwpt"
      },
      "source": [
        "Para ejemplificar esta técnica utilizaremos un conjunto de datos muy popular llamado CIFAR-10. CIFAR-10 es un conjunto de datos que consiste en 60.000 imagenes a color de 32x32 agrupadas en 10 clases, con 6000 imagenes cada una. Hay alrededor de 50000 imagenes para entrenamiento y 10000 para testing.\n",
        "\n",
        "Las categorias son:\n",
        "\n",
        "* airplane\n",
        "* automobile\n",
        "* bird\n",
        "* cat\n",
        "* deer\n",
        "* dog\n",
        "* frog\n",
        "* horse\n",
        "* ship\n",
        "* truck"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ehq4pYRGCwpu"
      },
      "source": [
        "Podemos cargar este conjunto de datos facilmente utilizando `datasets`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281,
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            "2023113b305f4e41b0b128770050d9ef"
          ]
        },
        "gather": {
          "logged": 1683473835319
        },
        "id": "-ToQd6ybDfef",
        "outputId": "3e5a3a25-d963-45d2-ebe1-6ecf06d3cd27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/5.16k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb4607ac564743e396386cf4934a23af"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train-00000-of-00001.parquet:   0%|          | 0.00/120M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "43428dbf5b2640ef8abdd0f3439c3417"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test-00000-of-00001.parquet:   0%|          | 0.00/23.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aff98684faa44631902d96dbc5aa30f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "647b78cd7e1b41f1a195db9a2f0ca520"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b7c36ad4f1f4b52ba456d5f73f676b6"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "train_ds, test_ds = load_dataset('cifar10', split=['train', 'test'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CvZJJJrrlS6p"
      },
      "source": [
        "Divieremos el conjunto de datos de entrenamiento en 2 subconjuntos: uno de entrenamiento (90%) y otro de validación (10%):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "gather": {
          "logged": 1683475587861
        },
        "id": "pl6tmETmDfei"
      },
      "outputs": [],
      "source": [
        "splits = train_ds.train_test_split(test_size=0.1)\n",
        "train_ds = splits['train']\n",
        "val_ds = splits['test']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKUeGV_alc8y"
      },
      "source": [
        "Veamos algunos detalles del conjunto de datos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "gather": {
          "logged": 1683475590500
        },
        "id": "hUNrdJoWGIgv",
        "outputId": "8b04123c-3592-4bb5-93ad-62f1cd859905"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['img', 'label'],\n",
              "    num_rows: 45000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "train_ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "gather": {
          "logged": 1683475592356
        },
        "id": "avsOTtLoGL7C",
        "outputId": "aef3a7a2-fb3c-4f4e-e8de-1440cf782ef8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'img': Image(mode=None, decode=True, id=None),\n",
              " 'label': ClassLabel(names=['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'], id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "train_ds.features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "StbfaIWWDfel"
      },
      "source": [
        "Veamos algunos ejemplos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "gather": {
          "logged": 1683475593538
        },
        "id": "tnIfBNDhDfem",
        "outputId": "8cbedebe-6407-4d09-cf1a-79977becc9d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n",
            "image/jpeg": 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\n"
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "train_ds[0]['img'].resize((224, 224))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2U9-305aDfeq"
      },
      "source": [
        "## Construcción de un modelo de clasificación utilizando ViT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2ydVcyxCwpx"
      },
      "source": [
        "Antes de comenzar necesitaremos verificar que tenemos el runtime correcto en nuestro ambiente. Esta tarea se beneficiará mucho de una GPU."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "gather": {
          "logged": 1683475617914
        },
        "id": "ABQ6LdkQDfeZ",
        "outputId": "e9a0e7b0-66ed-420a-cf73-4618326ceabf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPUs disponibles:  1\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "print(\"GPUs disponibles: \", torch.cuda.device_count())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "rzJuaDIwvcSp"
      },
      "outputs": [],
      "source": [
        "model_name = \"google/vit-base-patch16-224-in21k\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PziBXrSoDfeq"
      },
      "source": [
        "### Preprocesamiento\n",
        "\n",
        "Como es común, deberemos de ocuparnos del preprocesamiento de nuestras imágenes:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GRbTiI1vcSp"
      },
      "source": [
        "#### Labels\n",
        "\n",
        "Debemos de disponer de un mapeo entre las etiquetas y sus valores:\n",
        "\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "meiGBhENvcSq"
      },
      "outputs": [],
      "source": [
        "label2id = { label:id for id, label in enumerate(test_ds.features[\"label\"].names) }\n",
        "id2label = { id:label for id, label in enumerate(test_ds.features[\"label\"].names) }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0v-psEHGvKO"
      },
      "source": [
        "#### Tamaño y cantidad de parches\n",
        "\n",
        "Los transformers esperan recibir cada imagen con una resolución específica y una cantidad de parches particular. Esta tarea se puede realizar utilizando el concepto de `processor` en `transformers`.\n",
        "\n",
        "Este tipo de components preprocesan los datos antes de ser enviados al modelo que los procesará. En este caso, utilizaremos el `processor` associado con un modelo en particular, `google/vit-base-patch16-224-in21k`. Notar como en el nombre está codificado el tamaño de los parches (16x16) y la resolución de las imágenes (224x224)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "gather": {
          "logged": 1683475622887
        },
        "id": "vHeC0R_9Gho7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "581fe23549df4e7ba74b1a335a481ab7",
            "65b476f30bc040279f04b67369965d26",
            "e76f5c0c368a450aa2ab3a8881ddf3b3",
            "477e5cdd191c46fa834b9d882704a7ff",
            "f639c28a4f5140798962770a7c245410",
            "93d7076cfef149f58b6f0492087f4d06",
            "14b4fd22251246fa8b9e022243b39e89",
            "e250317804084bd6b71f0581fae8bb94",
            "fe03debf2d0b46599b8651ec14470158",
            "431a537712c64ebda9de530e03fe8bdd",
            "80cd7885992a405397b15e943545ec97"
          ]
        },
        "outputId": "178717cb-9313-4670-bdbc-58faad4d6ca0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "581fe23549df4e7ba74b1a335a481ab7"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import ViTImageProcessor\n",
        "\n",
        "processor = ViTImageProcessor.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3qhDhbkH5PR"
      },
      "source": [
        "Este componente también realizará normalización y estandarización, dos operaciones que típicamente realizamos en visión por computadora."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zzKx7UvnlxdY"
      },
      "source": [
        "Generaremos una función que toma un ejemplo del conjunto de datos y aplica nuestro `processor` de forma que devuelve los pixeles preprocesados y su etiqueta correspondiente:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "gather": {
          "logged": 1683475625648
        },
        "id": "yyqDpzBsq69f"
      },
      "outputs": [],
      "source": [
        "def process_example(example):\n",
        "    inputs = processor(example['img'], return_tensors='pt')\n",
        "    inputs['labels'] = example['label']\n",
        "\n",
        "    return inputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02_EDm4Jl8wW"
      },
      "source": [
        "Veamos como funciona la función con un ejemplo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "gather": {
          "logged": 1683475628639
        },
        "id": "uykxbmEircP0",
        "outputId": "1e5b77fc-c37b-4e18-94db-23b222e7197b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'pixel_values': tensor([[[[-0.3961, -0.3961, -0.3961,  ..., -0.2549, -0.2549, -0.2549],\n",
              "          [-0.3961, -0.3961, -0.3961,  ..., -0.2549, -0.2549, -0.2549],\n",
              "          [-0.3961, -0.3961, -0.3961,  ..., -0.2549, -0.2549, -0.2549],\n",
              "          ...,\n",
              "          [ 0.2471,  0.2471,  0.2471,  ..., -0.8353, -0.8353, -0.8353],\n",
              "          [ 0.2471,  0.2471,  0.2471,  ..., -0.8353, -0.8353, -0.8353],\n",
              "          [ 0.2471,  0.2471,  0.2471,  ..., -0.8353, -0.8353, -0.8353]],\n",
              "\n",
              "         [[-0.8196, -0.8196, -0.8196,  ..., -0.6627, -0.6627, -0.6627],\n",
              "          [-0.8196, -0.8196, -0.8196,  ..., -0.6627, -0.6627, -0.6627],\n",
              "          [-0.8196, -0.8196, -0.8196,  ..., -0.6627, -0.6627, -0.6627],\n",
              "          ...,\n",
              "          [ 0.1059,  0.1059,  0.1059,  ..., -0.8824, -0.8824, -0.8824],\n",
              "          [ 0.1059,  0.1059,  0.1059,  ..., -0.8824, -0.8824, -0.8824],\n",
              "          [ 0.1059,  0.1059,  0.1059,  ..., -0.8824, -0.8824, -0.8824]],\n",
              "\n",
              "         [[-0.9137, -0.9137, -0.9137,  ..., -0.8510, -0.8510, -0.8510],\n",
              "          [-0.9137, -0.9137, -0.9137,  ..., -0.8510, -0.8510, -0.8510],\n",
              "          [-0.9137, -0.9137, -0.9137,  ..., -0.8510, -0.8510, -0.8510],\n",
              "          ...,\n",
              "          [ 0.1137,  0.1137,  0.1137,  ..., -0.8667, -0.8667, -0.8667],\n",
              "          [ 0.1137,  0.1137,  0.1137,  ..., -0.8667, -0.8667, -0.8667],\n",
              "          [ 0.1137,  0.1137,  0.1137,  ..., -0.8667, -0.8667, -0.8667]]]]), 'labels': 3}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "example = process_example(train_ds[0])\n",
        "example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-sffkR2mJPr"
      },
      "source": [
        "Es útil ver las dimensiones de nuestros datos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "gather": {
          "logged": 1683475631591
        },
        "id": "5umUYzWRmE0W",
        "outputId": "7e67af25-8d20-4207-f955-062b2793d82a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 224, 224])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "example[\"pixel_values\"].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1agCtwUmVbN"
      },
      "source": [
        "#### Aplicando las transformaciones al conjunto de datos\n",
        "\n",
        "Apliquemos las transformaciones a todas las instancias:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "gather": {
          "logged": 1683475633094
        },
        "id": "wbfSqmO4rp_d"
      },
      "outputs": [],
      "source": [
        "prepared_train_ds = train_ds.with_transform(process_example)\n",
        "prepared_val_ds = val_ds.with_transform(process_example)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkqYRbxAjwIh"
      },
      "source": [
        "### Modelado\n",
        "\n",
        "Utilizaremos el modelo `ViTForImageClassification`, el cual agrega una capa de clasificación (`torch.nn.Linear`) sobre el token `[CLS]` que se agrega al final de la secuencia de parches en el modelo preentrenado de `ViTModel`. El último *hidden state* de este token puede ser considerado como una repreentación de la imágen completa. Configuraremos el número de clases (units en la última capa):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "34add6181fc14891bd6844aeb6532e7d",
            "e0d338060129463390a33fdc7d4cc99e",
            "5fbd34d7851444ddbe7db150979bf63d",
            "b2c1146501c54f99802c88b02a9f6080",
            "1f04913917014c02b289c6fdc45d1f08",
            "fdcae8f9e6f4442b9e952945939fedfe",
            "cbafbd36dec74ba4ba2347e8de5b0dd0",
            "ef7a8157c6c34f4e91d70851d489b92a",
            "cdcd590263b1462289cd012f8179a3a6",
            "4a6759718bc24485b2cf2d93f2467543",
            "a115ad87ac7b4938a371045e008c414d",
            "79945322d6a641058129c20469e09b5c",
            "ba5c6a288a504d0bac3913ac28347f74",
            "c5d0582690e74cbc8be3268a6e741b8b",
            "4c914beeb826479a831d44b2bc991934",
            "d5584154bacc41a7929e7398cd92de5c",
            "963533b496ef42da9f5d30f0727c47ef",
            "0e1a6cf890474470927b8513b3660f15",
            "22402d068e77486cb31c10094f81645c",
            "280e62e3c52f42a5b380fcd493e562f7",
            "08508fa60c0540a19ce2c75254a01484",
            "68ac1190663b4fdc9e51ba1191e55735"
          ]
        },
        "gather": {
          "logged": 1683475640520
        },
        "id": "wpNrIM7pjtYr",
        "outputId": "a3950529-7ac3-4ac0-81ea-c0ad9b6e79e0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/502 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34add6181fc14891bd6844aeb6532e7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79945322d6a641058129c20469e09b5c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import ViTForImageClassification\n",
        "\n",
        "model = ViTForImageClassification.from_pretrained(model_name, num_labels=10, top_k=1, id2label=id2label, label2id=label2id).to('cuda')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IS12IfstmjUp"
      },
      "source": [
        "### Entrenamiento\n",
        "\n",
        "Configuraremos nuestros parámeteros de entrenamiento de la siguiente forma:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "gather": {
          "logged": 1683475659981
        },
        "id": "Vck3xg53kqwu"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "from transformers import default_data_collator\n",
        "\n",
        "args = TrainingArguments(\n",
        "    \"test-cifar-10\",\n",
        "    save_strategy=\"epoch\",\n",
        "    eval_strategy=\"epoch\",\n",
        "    num_train_epochs=3,\n",
        "    optim=\"adamw_torch\",\n",
        "    learning_rate=2e-5,\n",
        "    weight_decay=0.01,\n",
        "    per_device_train_batch_size=64,\n",
        "    per_device_eval_batch_size=64,\n",
        "    load_best_model_at_end=True,\n",
        "    report_to=\"none\",\n",
        "    remove_unused_columns=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aq7uolYHmuad"
      },
      "source": [
        "Configuramos el proceso de entrenamiento del modelo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "gather": {
          "logged": 1683475696543
        },
        "id": "ByvyWUtVk3iH"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    data_collator=default_data_collator,\n",
        "    train_dataset=prepared_train_ds,\n",
        "    eval_dataset=prepared_val_ds,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "gather": {
          "logged": 1683475943222
        },
        "id": "8y-1nfGnlNGs",
        "outputId": "d48f00cd-1a8a-4b33-c2ec-7e754a48354c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='966' max='2112' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 966/2112 36:39 < 43:35, 0.44 it/s, Epoch 1.37/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.370300</td>\n",
              "      <td>0.149960</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/configuration_utils.py:394: UserWarning: Some non-default generation parameters are set in the model config. These should go into either a) `model.generation_config` (as opposed to `model.config`); OR b) a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model).This warning will become an exception in the future.\n",
            "Non-default generation parameters: {'top_k': 1}\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2112' max='2112' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2112/2112 1:21:26, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.370300</td>\n",
              "      <td>0.149960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.129400</td>\n",
              "      <td>0.092566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.047000</td>\n",
              "      <td>0.085288</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/configuration_utils.py:394: UserWarning: Some non-default generation parameters are set in the model config. These should go into either a) `model.generation_config` (as opposed to `model.config`); OR b) a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model).This warning will become an exception in the future.\n",
            "Non-default generation parameters: {'top_k': 1}\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/transformers/configuration_utils.py:394: UserWarning: Some non-default generation parameters are set in the model config. These should go into either a) `model.generation_config` (as opposed to `model.config`); OR b) a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model).This warning will become an exception in the future.\n",
            "Non-default generation parameters: {'top_k': 1}\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "history = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-GvPFZ6mzST"
      },
      "source": [
        "### Evaluación\n",
        "\n",
        "Una vez que el modelo esta entrenado, veamos como se comporta en el conjunto de evaluación:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "OW0RJrJIhEPe"
      },
      "outputs": [],
      "source": [
        "prepared_test_ds = test_ds.with_transform(process_example)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "CypydNpEg3RI",
        "outputId": "65cc65f9-72fd-470f-efc8-ac1795dc18ac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ],
      "source": [
        "predictions = trainer.predict(test_dataset=prepared_test_ds).predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXlEdUA5m6AS"
      },
      "source": [
        "Obtenemos las clases asociadas:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "dJtsWqLfhA6z"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "predictions = np.argmax(predictions, axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BH4P7RDDm89K"
      },
      "source": [
        "Creamos un reporte de clasificación:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHvlzQ_OhlIz",
        "outputId": "24302b6f-ed1c-4eb3-a28e-aa428c9cdf15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.99      0.98      0.99      1000\n",
            "  automobile       0.98      0.98      0.98      1000\n",
            "        bird       0.99      0.99      0.99      1000\n",
            "         cat       0.96      0.97      0.96      1000\n",
            "        deer       0.99      0.99      0.99      1000\n",
            "         dog       0.98      0.95      0.97      1000\n",
            "        frog       0.99      1.00      1.00      1000\n",
            "       horse       0.99      0.99      0.99      1000\n",
            "        ship       0.99      0.99      0.99      1000\n",
            "       truck       0.98      0.98      0.98      1000\n",
            "\n",
            "    accuracy                           0.98     10000\n",
            "   macro avg       0.98      0.98      0.98     10000\n",
            "weighted avg       0.98      0.98      0.98     10000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(test_ds['label'], predictions, target_names=test_ds.features[\"label\"].names))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8DLq4e_jwUrE"
      },
      "source": [
        "### Guardar el modelo para su posterior utilización\n",
        "\n",
        "Podemos guardar los componentes de este modelo para luego construir un pipeline con el mismo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "KDYooXx6wizc",
        "outputId": "a4613167-2320-4216-fa21-cf07dad2d1a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/configuration_utils.py:394: UserWarning: Some non-default generation parameters are set in the model config. These should go into either a) `model.generation_config` (as opposed to `model.config`); OR b) a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model).This warning will become an exception in the future.\n",
            "Non-default generation parameters: {'top_k': 1}\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['vit_cifar10/preprocessor_config.json']"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "model_name = \"vit_cifar10\"\n",
        "\n",
        "trainer.save_model(model_name)\n",
        "processor.save_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "Ee__JGFdwYdz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f347ab9-3cde-4b1e-c4cc-a7f72ca58de7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n",
            "Device set to use cuda:0\n"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"image-classification\", model=\"vit_cifar10\", image_processor=\"vit_cifar10\", top_k=1, device=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "NEvmc6b-vcS0",
        "outputId": "23ff6062-8885-415c-9bcc-4f6bd6b90feb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'label': 'cat', 'score': 0.9640224575996399}],\n",
              " [{'label': 'ship', 'score': 0.9606396555900574}],\n",
              " [{'label': 'ship', 'score': 0.9603068828582764}]]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "classifier(train_ds[\"img\"][:3])"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "vit_classification.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernel_info": {
      "name": "huggingface-env"
    },
    "kernelspec": {
      "display_name": "HuggingFace W/Torch",
      "language": "python",
      "name": "huggingface-env"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "microsoft": {
      "ms_spell_check": {
        "ms_spell_check_language": "en"
      }
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    },
    "widgets": {
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        "eb4607ac564743e396386cf4934a23af": {
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