2_Pytorch_Resolver_ejercicio_1.ipynb

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      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Curso Transfer Learning\n",
        "\n",
        "<img src=\"https://yaelmanuel.com/wp-content/uploads/2021/12/platzi-banner-logo-matematicas.png\" width=\"500px\">\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "E4OEtECGjKtp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creando nuestro propio Clasificador de Imágenes 🌷🌳\n",
        "\n",
        "En este lab aprenderás:\n",
        "\n",
        "* [Pytorch](https://pytorch.org/)\n",
        "* [Torchvision](https://pytorch.org/vision/stable/index.html)\n",
        "* Descargar un dataset, prepararlo, entrenarlo, realizar finetuning y guardarlo.\n"
      ],
      "metadata": {
        "id": "p_ny9c9fjbLP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1) Descarga del dataset 🤓\n",
        "\n",
        "Utilizaremos un conjunto de imágenes de enfermedades de las hojas de plantas.\n",
        "<br>Para más detalle acá se puede ver el dataset de Kaggle: [Plant disease Dataset](https://www.kaggle.com/datasets/rashikrahmanpritom/plant-disease-recognition-dataset).\n"
      ],
      "metadata": {
        "id": "fxgR3AqoTxQP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --force-reinstall --no-deps kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5WVoiMyphoSc",
        "outputId": "4a0b6f24-14a8-4ee9-95e7-5686f149e74b"
      },
      "execution_count": null,
      "outputs": [
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          "text": [
            "Collecting kaggle\n",
            "  Downloading kaggle-1.6.17.tar.gz (82 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/82.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.7/82.7 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: kaggle\n",
            "  Building wheel for kaggle (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaggle: filename=kaggle-1.6.17-py3-none-any.whl size=105786 sha256=f05ca8a2acdc176d00bc052a55e0119fdea8168d9c23c43e51f0c18df282c525\n",
            "  Stored in directory: /root/.cache/pip/wheels/9f/af/22/bf406f913dc7506a485e60dce8143741abd0a92a19337d83a3\n",
            "Successfully built kaggle\n",
            "Installing collected packages: kaggle\n",
            "  Attempting uninstall: kaggle\n",
            "    Found existing installation: kaggle 1.6.17\n",
            "    Uninstalling kaggle-1.6.17:\n",
            "      Successfully uninstalled kaggle-1.6.17\n",
            "Successfully installed kaggle-1.6.17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "Lv-i750ghtiv",
        "outputId": "4833ba3d-51d1-4dfb-db64-40b1ec564331"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c835b3e9-500b-4554-936c-0273c2a547a3\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c835b3e9-500b-4554-936c-0273c2a547a3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
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              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
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              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
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              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
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              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"carlosbustillo\",\"key\":\"507f9f6ee972b4bcad76ddc3f00b2851\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 /root/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "kgDVQkKGibgT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets list -s plant-disease-classification"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9JJy0CIhxNn",
        "outputId": "092b8dee-3f33-4d54-cfcc-7a1140768da4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ref                                                         title                                         size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
            "----------------------------------------------------------  -------------------------------------------  -----  -------------------  -------------  ---------  ---------------  \n",
            "alinedobrovsky/plant-disease-classification-merged-dataset  Plant Disease Classification Merged Dataset   17GB  2023-01-03 15:11:19           6539         71  0.6875           \n",
            "smaranjitghose/corn-or-maize-leaf-disease-dataset           Corn or Maize Leaf Disease Dataset           161MB  2020-11-11 13:39:07          16417        204  0.875            \n",
            "vbookshelf/rice-leaf-diseases                               Rice Leaf Diseases Dataset                    37MB  2020-02-21 01:35:02          17676        221  0.625            \n",
            "nafishamoin/new-bangladeshi-crop-disease                    New Bangladeshi Crop Disease                   2GB  2022-09-27 08:35:14           2866         60  0.8125           \n",
            "asadullahgalib/guava-disease-dataset                        Guava Fruit Disease Dataset                    2GB  2024-11-03 19:26:30           2147         48  1.0              \n",
            "mexwell/crop-diseases-classification                        🌱 Crop Diseases Classification                 2GB  2024-03-08 09:23:10           2427         53  1.0              \n",
            "hafiznouman786/potato-plant-diseases-data                   🌱 Potato Plant Diseases Data 🍂                76MB  2024-05-28 09:29:49           2978         50  0.875            \n",
            "asheniranga/leaf-disease-dataset-combination                Leaf Disease Dataset (combination)           761MB  2022-01-09 14:23:28           2606         46  0.75             \n",
            "tushar5harma/plant-village-dataset-updated                  Plant Village Dataset (Updated)                1GB  2023-04-18 04:33:25           2781         33  0.75             \n",
            "sujallimje/plant-pathogens                                  Plant Pathogen Classification                 24GB  2024-04-12 18:28:21            583         30  0.875            \n",
            "sadmansakibmahi/plant-disease-expert                        Plant Disease Expert                           9GB  2024-08-31 11:09:18           4781         92  0.7058824        \n",
            "adilmubashirchaudhry/plant-village-dataset                  Plant Village Dataset                        989MB  2023-09-08 20:51:29            701         11  0.75             \n",
            "mahmoudshaheen1134/plant-leaf-image-dataset                 Plant leaf image Dataset                      60MB  2024-11-01 17:34:28            275         33  0.875            \n",
            "nirmalsankalana/plant-disease-test-dataset                  Plant Disease Test Dataset                    55MB  2024-09-16 17:10:44            456         16  0.875            \n",
            "mnavaidd/plant-disease-classification                       Plant Disease Classification                 300MB  2021-05-23 08:43:11            126         13  0.1875           \n",
            "amandam1/healthy-vs-diseased-leaf-image-dataset             Healthy vs. Diseased Leaf Image Dataset        6GB  2022-04-22 20:34:34           2201         39  0.75             \n",
            "srg9000/cassava-plant-disease-merged-20192020               Cassava plant disease Merged 2019-2020        11GB  2021-01-31 18:59:27            499         17  0.9411765        \n",
            "nirmalsankalana/plantdoc-dataset                            PlantDoc Classification dataset              896MB  2024-09-16 10:09:20            584         10  0.875            \n",
            "raiaone/olid-i                                              OLID I                                        13GB  2023-07-02 13:15:15            874         20  0.875            \n",
            "mamtag/tomato-village                                       Tomato-Village dataset                         2GB  2023-08-27 08:58:08           1193         16  0.7058824        \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d rashikrahmanpritom/plant-disease-recognition-dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z10nbuiQh4av",
        "outputId": "c22dea6b-55a6-4664-ecb3-389c504b7a62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/rashikrahmanpritom/plant-disease-recognition-dataset\n",
            "License(s): CC0-1.0\n",
            "Downloading plant-disease-recognition-dataset.zip to /content\n",
            "100% 1.25G/1.25G [00:58<00:00, 23.9MB/s]\n",
            "100% 1.25G/1.25G [00:58<00:00, 23.1MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip '/content/plant-disease-recognition-dataset.zip'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-6HAPlAiza9",
        "outputId": "a8695c68-2ff6-4139-e14c-2a2afeecf8f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/plant-disease-recognition-dataset.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Creamos un folder \"enfermedades_hojas\" para guardar nuestras imágenes y mantener una estructura similar a la del ejemplo anterior.***"
      ],
      "metadata": {
        "id": "OI9-7yGNh0dt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir /content/enfermedades_hojas"
      ],
      "metadata": {
        "id": "JH-DTZuF2e5n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Cambiamos el nombre de la carpeta \"validation\" por \"val\", para seguir la misma estructura del código de ejemplo***"
      ],
      "metadata": {
        "id": "O2kiOIrQniYq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mv /content/Train/Train /content/enfermedades_hojas/train/\n",
        "!mv /content/Test/Test /content/enfermedades_hojas/test/\n",
        "!mv /content/Validation/Validation /content/enfermedades_hojas/val/"
      ],
      "metadata": {
        "id": "Bc9ia4ZMnwmZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Removemos las carpetas vacías porque ya no los necesitamos.***"
      ],
      "metadata": {
        "id": "rKPTKQ7tmSWU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -r /content/Test\n",
        "!rm -r /content/Train\n",
        "!rm -r /content/Validation"
      ],
      "metadata": {
        "id": "EoNOyz1M0bNo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2) Preparación de la data 👌"
      ],
      "metadata": {
        "id": "HX8Q_QXsTts3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.1) Instalamos las dependencias 🙌"
      ],
      "metadata": {
        "id": "BYa0Tjnv74mE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import torch.backends.cudnn as cudnn\n",
        "\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "from torchvision.models import ResNet50_Weights\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import os\n",
        "import copy"
      ],
      "metadata": {
        "id": "Xu_qILeYSLv_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.2) Tips de implementación 👀"
      ],
      "metadata": {
        "id": "LghPjoil8Njr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cudnn.benchmark = True"
      ],
      "metadata": {
        "id": "d3OFpYzd5umr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "tSuVNQanVrUT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.3) Data Augmentation 📷 📸"
      ],
      "metadata": {
        "id": "aEVRaI1Q8ayz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "}"
      ],
      "metadata": {
        "id": "fzd7EpuaVj_l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.4) Dataset, Dataloader y más data 🔍"
      ],
      "metadata": {
        "id": "Ull0vjyr9Ihl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DSj6JT8QWbtG",
        "outputId": "ca340e3a-805f-4f22-f017-25a6ea4f48bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "enfermedades_hojas  kaggle.json  plant-disease-recognition-dataset.zip\tsample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = 'enfermedades_hojas'\n",
        "\n",
        "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])for x in ['train', 'val']}\n",
        "\n",
        "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=2) for x in ['train', 'val']}\n",
        "\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
        "\n",
        "class_names = image_datasets['train'].classes"
      ],
      "metadata": {
        "id": "2EmQl-jvV93M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def imshow(inp, title=None):\n",
        "    \"\"\"Imshow para tensor.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)\n",
        "    plt.pause(0.001)  # Pausarlo un poco para que se actualicen los plots\n",
        "\n",
        "\n",
        "# Obtener un batch de datos de entrenamiento\n",
        "inputs, classes = next(iter(dataloaders['train']))\n",
        "\n",
        "# Crear una grilla con las imágenes\n",
        "out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "imshow(out, title=[class_names[x] for x in classes])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "BTG-jSTXW2FS",
        "outputId": "62ad1ca3-423b-4f47-f8bd-8774e06d0452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Evaluar las class_names***"
      ],
      "metadata": {
        "id": "3RrtVT0bUYA2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVOIoRBdUVCk",
        "outputId": "a0722c8d-b083-48d2-8fb8-438e2ced4a1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Healthy', 'Powdery', 'Rust']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3) Entrenamiento 💪"
      ],
      "metadata": {
        "id": "z5CReMaiYSDt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "\n",
        "        # Cada epoch tiene una fase de entrenamiento y validación.\n",
        "        for phase in ['train', 'val']:\n",
        "            if phase == 'train':\n",
        "                model.train()  # Fase entrenamiento\n",
        "            else:\n",
        "                model.eval()   # Fase validación\n",
        "\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterar sobre la data.\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "\n",
        "                # zero the parameter gradients\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # forward\n",
        "                # track history solo si es la fase de entrenamiento\n",
        "                with torch.set_grad_enabled(phase == 'train'):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "\n",
        "                    # backward + optimize solo si es la fase de entrenamiento\n",
        "                    if phase == 'train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # Estadísticas\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                running_corrects += torch.sum(preds == labels.data)\n",
        "            if phase == 'train':\n",
        "                scheduler.step()\n",
        "\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "\n",
        "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "                phase, epoch_loss, epoch_acc))\n",
        "\n",
        "            # deep copy el modelo\n",
        "            if phase == 'val' and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "        print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    # Cargar el best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model"
      ],
      "metadata": {
        "id": "lIq8l9BeYUG9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def visualize_model(model, num_images=6):\n",
        "    was_training = model.training\n",
        "    model.eval()\n",
        "    images_so_far = 0\n",
        "    fig = plt.figure()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "\n",
        "            for j in range(inputs.size()[0]):\n",
        "                images_so_far += 1\n",
        "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
        "                ax.axis('off')\n",
        "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
        "                imshow(inputs.cpu().data[j])\n",
        "\n",
        "                if images_so_far == num_images:\n",
        "                    model.train(mode=was_training)\n",
        "                    return\n",
        "        model.train(mode=was_training)"
      ],
      "metadata": {
        "id": "y1JuSJraYiQK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4) Finetuning de la convnet 😨"
      ],
      "metadata": {
        "id": "NdRZS4ZactJU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_ft = models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)\n",
        "num_ftrs = model_ft.fc.in_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLqzI2RNRnPB",
        "outputId": "f243c283-c0d3-44c1-a251-9aafaed77014"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
            "100%|██████████| 97.8M/97.8M [00:01<00:00, 89.8MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Aquí el tamaño de cada muestra de salida se establece en len(class_names) = 3.\n",
        "model_ft.fc = nn.Linear(num_ftrs, len(class_names))\n",
        "\n",
        "model_ft = model_ft.to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Observe que todos los parámetros están siendo optimizados\n",
        "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "# Decae LR por un factor de 0.1 cada 7 epochs\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
      ],
      "metadata": {
        "id": "E1dwcF05Ynf9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Vamos a usar 5 epochs***"
      ],
      "metadata": {
        "id": "OSjLPIucUu9w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3cOz9yPgYs40",
        "outputId": "7cb89640-1ac2-4dc0-e9e5-f5b690707609"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0/4\n",
            "----------\n",
            "train Loss: 0.5382 Acc: 0.7920\n",
            "val Loss: 0.0628 Acc: 0.9833\n",
            "\n",
            "Epoch 1/4\n",
            "----------\n",
            "train Loss: 0.4038 Acc: 0.8593\n",
            "val Loss: 0.0264 Acc: 1.0000\n",
            "\n",
            "Epoch 2/4\n",
            "----------\n",
            "train Loss: 0.3488 Acc: 0.8835\n",
            "val Loss: 0.0126 Acc: 1.0000\n",
            "\n",
            "Epoch 3/4\n",
            "----------\n",
            "train Loss: 0.3074 Acc: 0.8994\n",
            "val Loss: 0.0086 Acc: 1.0000\n",
            "\n",
            "Epoch 4/4\n",
            "----------\n",
            "train Loss: 0.3421 Acc: 0.9024\n",
            "val Loss: 0.0169 Acc: 1.0000\n",
            "\n",
            "Training complete in 12m 30s\n",
            "Best val Acc: 1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_model(model_ft)"
      ],
      "metadata": {
        "id": "WnTpR4fkYy6N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 923
        },
        "outputId": "f881de27-40ff-4172-d44c-4d81f0f47846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5) Guardar el modelo 💾"
      ],
      "metadata": {
        "id": "F2MHfLfbxrtc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model_ft, \"model.pth\")"
      ],
      "metadata": {
        "id": "rdMvhdoIx2RN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6) Hacer Predicciones en Producción 🤙"
      ],
      "metadata": {
        "id": "icAp1SniSDuN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torchvision import transforms\n",
        "from PIL import Image"
      ],
      "metadata": {
        "id": "EO32eWWZSEPV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cargar el modelo una vez (al inicio de la aplicación)"
      ],
      "metadata": {
        "id": "SRtZXB8LSRw-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Determinar el dispositivo\n",
        "mi_device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Cargar el modelo en el dispositivo\n",
        "mi_modelo = torch.load(\"model.pth\", map_location=mi_device, weights_only=False)\n",
        "mi_modelo.eval()\n",
        "mi_modelo.to(mi_device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGkV7b_ISSqb",
        "outputId": "a0e85091-1025-4e26-f797-5cbed1af69dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (4): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (5): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Linear(in_features=2048, out_features=3, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Función para predicción / inferencia"
      ],
      "metadata": {
        "id": "7vhsu1naSWb_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_image(image_path, model, class_names, device):\n",
        "    \"\"\"\n",
        "    Predice la clase de una imagen usando un modelo entrenado.\n",
        "\n",
        "    Args:\n",
        "        image_path (str): Ruta de la imagen.\n",
        "        model (torch.nn.Module): Modelo ya cargado y configurado para inferencia.\n",
        "        class_names (list): Lista de nombres de clases.\n",
        "        device (torch.device): Dispositivo donde está el modelo.\n",
        "\n",
        "    Returns:\n",
        "        str: Clase predicha.\n",
        "    \"\"\"\n",
        "    # Preprocesamiento de la imagen\n",
        "    preprocess = transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "\n",
        "    # Cargar y preprocesar la imagen\n",
        "    image = Image.open(image_path).convert(\"RGB\")\n",
        "    image_tensor = preprocess(image).unsqueeze(0).to(device)  # Añadir dimensión batch y mover al dispositivo\n",
        "\n",
        "    # Realizar predicción\n",
        "    with torch.no_grad():\n",
        "        outputs = model(image_tensor)\n",
        "        _, preds = torch.max(outputs, 1)\n",
        "\n",
        "    # Retornar la clase predicha\n",
        "    return class_names[preds[0].item()]"
      ],
      "metadata": {
        "id": "Et4wFh3ISZK0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ejemplos de uso"
      ],
      "metadata": {
        "id": "3gkxguDSSc-P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['Healthy', 'Powdery', 'Rust']"
      ],
      "metadata": {
        "id": "I0Bfi7PHSeRX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path_image = \"/content/enfermedades_hojas/val/Rust/8030a3a79fca6abb.jpg\"\n",
        "\n",
        "predicted_class = predict_image(path_image, mi_modelo, class_names, mi_device)\n",
        "print(f\"La clase predicha es: {predicted_class}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uE3J0K9ESzcm",
        "outputId": "80762b72-faaa-41c7-9284-b29c098dcd88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La clase predicha es: Rust\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path_image = \"/content/enfermedades_hojas/val/Healthy/9c3f1c10ba54ed56.jpg\"\n",
        "\n",
        "predicted_class = predict_image(path_image, mi_modelo, class_names, mi_device)\n",
        "print(f\"La clase predicha es: {predicted_class}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Ugk46POS19t",
        "outputId": "85f075b0-74c9-4580-b0d0-3c2bc6695a7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La clase predicha es: Healthy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path_image = \"/content/enfermedades_hojas/val/Powdery/8f6737815b2cd234.jpg\"\n",
        "\n",
        "predicted_class = predict_image(path_image, mi_modelo, class_names, mi_device)\n",
        "print(f\"La clase predicha es: {predicted_class}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3GYERUhS2Va",
        "outputId": "50598c14-347d-474a-860f-aa36204d30e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La clase predicha es: Powdery\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7) Conclusiones\n",
        "\n",
        "- Aprender sobre los distintos objetos y métodos que nos ofrece Pytorch / Torchvision.\n",
        "\n",
        "- Realizar el proceso completo de clasificación de imágenes con Pytorch.\n",
        "\n",
        "- Aprender tips sobre implementación con el uso de la GPU.\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "<br>"
      ],
      "metadata": {
        "id": "Wl7v6oyFkREF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "\n",
        "<img src=\"https://static.platzi.com/media/avatars/platziteam_8cfe6fc7-1246-4c9a-9f5d-d10d467443ee.png\" width=\"100px\">\n",
        "\n",
        "[Platzi](https://platzi.com/) 🚀\n",
        "\n"
      ],
      "metadata": {
        "id": "0sQ95LlzkUZN"
      }
    }
  ]
}