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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/eleanarey/ProgramingPractices/blob/main/tfm_v3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install transformers torch torchvision matplotlib scikit-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VKCto_hZoJcF",
        "outputId": "ab2fe97b-0e34-4a00-8d94-93172c4c05f9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.47.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.1+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.20.1+cu121)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.8.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.6.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.27.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.11.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.21.0)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.67.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.10.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (11.0.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.55.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.7)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (3.0.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.12.14)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "x40Ve_03clLr"
      },
      "outputs": [],
      "source": [
        "# Script para Clasificación con ViT extendido con Detección de Fotos Borrosas, Duplicados y Optimización para Dispositivos Móviles\n",
        "\n",
        "from google.colab import drive\n",
        "import zipfile\n",
        "import os\n",
        "import cv2\n",
        "from skimage.metrics import structural_similarity as ssim\n",
        "import torch\n",
        "from torchvision import datasets, transforms\n",
        "from torch.utils.data import DataLoader\n",
        "from transformers import ViTForImageClassification, ViTImageProcessor\n",
        "\n",
        "# Asegurar que todas las dependencias estén definidas\n",
        "try:\n",
        "    import cv2\n",
        "except ImportError:\n",
        "    raise ImportError(\"La biblioteca cv2 no está instalada. Usa `pip install opencv-python` para instalarla.\")\n",
        "\n",
        "try:\n",
        "    from skimage.metrics import structural_similarity as ssim\n",
        "except ImportError:\n",
        "    raise ImportError(\"La biblioteca scikit-image no está instalada. Usa `pip install scikit-image` para instalarla.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import zipfile\n",
        "import os\n",
        "\n",
        "# Montar Google Drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "\n",
        "# Ruta al archivo ZIP en Google Drive\n",
        "dataset_zip_path = \"/content/drive/MyDrive/Colab_Notebooks/dataset/images/tiny-imagenet-200.zip\"\n",
        "extract_path = \"/content/dataset/tiny-imagenet-200\"\n",
        "\n",
        "# Verificar si el archivo ZIP existe\n",
        "if not os.path.exists(dataset_zip_path):\n",
        "    raise FileNotFoundError(f\"El archivo {dataset_zip_path} no existe. Verifica la ruta.\")\n",
        "\n",
        "# Crear la carpeta de extracción y descomprimir\n",
        "os.makedirs(extract_path, exist_ok=True)\n",
        "with zipfile.ZipFile(dataset_zip_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(extract_path)\n",
        "\n",
        "print(f\"Dataset extraído en: {extract_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mKsuxeZPx3s",
        "outputId": "ce17d557-3c13-4290-ce06-ee5648aa5831"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "Dataset extraído en: /content/dataset/tiny-imagenet-200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import datasets, transforms\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Transformaciones para las imágenes\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),   # Redimensionar\n",
        "    transforms.ToTensor(),           # Convertir a tensor\n",
        "    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])  # Normalización\n",
        "])\n",
        "\n",
        "# Rutas al dataset descomprimido\n",
        "train_dir = os.path.join(extract_path, \"tiny-imagenet-200/train\")\n",
        "val_dir = os.path.join(extract_path, \"tiny-imagenet-200/val\")\n",
        "\n",
        "# Crear datasets y DataLoaders\n",
        "train_dataset = datasets.ImageFolder(train_dir, transform=transform)\n",
        "val_dataset = datasets.ImageFolder(val_dir, transform=transform)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n",
        "\n",
        "print(f\"Clases: {train_dataset.classes}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UtO5tXA9RtqR",
        "outputId": "b5e5f404-f406-45eb-f03d-395ebffdef2d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Clases: ['n01443537', 'n01629819', 'n01641577', 'n01644900', 'n01698640', 'n01742172', 'n01768244', 'n01770393', 'n01774384', 'n01774750', 'n01784675', 'n01855672', 'n01882714', 'n01910747', 'n01917289', 'n01944390', 'n01945685', 'n01950731', 'n01983481', 'n01984695', 'n02002724', 'n02056570', 'n02058221', 'n02074367', 'n02085620', 'n02094433', 'n02099601', 'n02099712', 'n02106662', 'n02113799', 'n02123045', 'n02123394', 'n02124075', 'n02125311', 'n02129165', 'n02132136', 'n02165456', 'n02190166', 'n02206856', 'n02226429', 'n02231487', 'n02233338', 'n02236044', 'n02268443', 'n02279972', 'n02281406', 'n02321529', 'n02364673', 'n02395406', 'n02403003', 'n02410509', 'n02415577', 'n02423022', 'n02437312', 'n02480495', 'n02481823', 'n02486410', 'n02504458', 'n02509815', 'n02666196', 'n02669723', 'n02699494', 'n02730930', 'n02769748', 'n02788148', 'n02791270', 'n02793495', 'n02795169', 'n02802426', 'n02808440', 'n02814533', 'n02814860', 'n02815834', 'n02823428', 'n02837789', 'n02841315', 'n02843684', 'n02883205', 'n02892201', 'n02906734', 'n02909870', 'n02917067', 'n02927161', 'n02948072', 'n02950826', 'n02963159', 'n02977058', 'n02988304', 'n02999410', 'n03014705', 'n03026506', 'n03042490', 'n03085013', 'n03089624', 'n03100240', 'n03126707', 'n03160309', 'n03179701', 'n03201208', 'n03250847', 'n03255030', 'n03355925', 'n03388043', 'n03393912', 'n03400231', 'n03404251', 'n03424325', 'n03444034', 'n03447447', 'n03544143', 'n03584254', 'n03599486', 'n03617480', 'n03637318', 'n03649909', 'n03662601', 'n03670208', 'n03706229', 'n03733131', 'n03763968', 'n03770439', 'n03796401', 'n03804744', 'n03814639', 'n03837869', 'n03838899', 'n03854065', 'n03891332', 'n03902125', 'n03930313', 'n03937543', 'n03970156', 'n03976657', 'n03977966', 'n03980874', 'n03983396', 'n03992509', 'n04008634', 'n04023962', 'n04067472', 'n04070727', 'n04074963', 'n04099969', 'n04118538', 'n04133789', 'n04146614', 'n04149813', 'n04179913', 'n04251144', 'n04254777', 'n04259630', 'n04265275', 'n04275548', 'n04285008', 'n04311004', 'n04328186', 'n04356056', 'n04366367', 'n04371430', 'n04376876', 'n04398044', 'n04399382', 'n04417672', 'n04456115', 'n04465501', 'n04486054', 'n04487081', 'n04501370', 'n04507155', 'n04532106', 'n04532670', 'n04540053', 'n04560804', 'n04562935', 'n04596742', 'n04597913', 'n06596364', 'n07579787', 'n07583066', 'n07614500', 'n07615774', 'n07695742', 'n07711569', 'n07715103', 'n07720875', 'n07734744', 'n07747607', 'n07749582', 'n07753592', 'n07768694', 'n07871810', 'n07873807', 'n07875152', 'n07920052', 'n09193705', 'n09246464', 'n09256479', 'n09332890', 'n09428293', 'n12267677']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import ViTForImageClassification, ViTImageProcessor\n",
        "import torch\n",
        "\n",
        "# Configuración del modelo\n",
        "num_classes = len(train_dataset.classes)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Cargar modelo ViT preentrenado\n",
        "model = ViTForImageClassification.from_pretrained(\n",
        "    \"google/vit-base-patch16-224\",\n",
        "    num_labels=num_classes,  # Configurar para 200 clases\n",
        "    ignore_mismatched_sizes=True  # Ignorar discrepancias de tamaño\n",
        ")\n",
        "\n",
        "model = model.to(device)\n",
        "\n",
        "# Optimización\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)\n",
        "criterion = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "MTDy6Pnurxnm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263,
          "referenced_widgets": [
            "72f049f024b0498db10b0b19a401e764",
            "2d935a87339646f4a6036660c578ff70",
            "4dbe2b372e8c4281a83080c1dec64cad",
            "f068032afbc8494581605419986db5d4",
            "472551acba8a4ed189dc619f669d1938",
            "b4c33d36ab8649d9a7d96925d3ccd79f",
            "70d9e9b1fcc0483e8ead470541ad5eaa",
            "09e203dad8304b759cb3c16df6a11aae",
            "d99f46e223e94a72b8406663f7563a66",
            "7472b7ce5db840c4a424cb46a8dd87a5",
            "48c7c9944e444314b751bbe0d13fb823",
            "52831622f8af4b6abbb747e5f0c453e5",
            "7030b0532e264f3ba4130dfb3f440268",
            "3a5aba8d6f9749b8a2b752ad98eff356",
            "29fc29d329a1413b8de709df5880cd5b",
            "eaf5301e272b4939bb35c7a5e055d7c1",
            "63c3b1baf293471183b2728e1cd26db2",
            "190652c834414b219e1d774e065026cc",
            "8311ca43893245b9b9434eed1ad8bda0",
            "fa8fe7d6654f40ad9089e0ef001ae0b2",
            "fd8142b5460449cda77fa80dbb20b957",
            "7170d54510624686806ebd02faca69f2"
          ]
        },
        "collapsed": true,
        "outputId": "36799f5a-849b-496c-9662-a5461dec4f30"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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": [
              "config.json:   0%|          | 0.00/69.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72f049f024b0498db10b0b19a401e764"
            }
          },
          "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": "52831622f8af4b6abbb747e5f0c453e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224 and are newly initialized because the shapes did not match:\n",
            "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([200]) in the model instantiated\n",
            "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([200, 768]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Entrenamiento\n",
        "for epoch in range(1):  # Ajusta el número de épocas según sea necesario\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "    for inputs, labels in train_loader:\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs.logits, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        running_loss += loss.item()\n",
        "    print(f\"Época {epoch + 1}, Pérdida: {running_loss / len(train_loader)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5IR_yeXi_SV",
        "outputId": "688cee82-58db-414d-a4b4-c8e73fe8dd3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Época 1, Pérdida: 0.9228811931490898\n",
            "Época 2, Pérdida: 0.24542514461368323\n",
            "Época 3, Pérdida: 0.1313110634934157\n",
            "Época 4, Pérdida: 0.0858390294598043\n",
            "Época 5, Pérdida: 0.06696780223987997\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), \"/content/drive/MyDrive/Colab_Notebooks/vit_tiny_imagenet_200.pth\")\n"
      ],
      "metadata": {
        "id": "CFLBzTOHjakI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Detección de Fotos Borrosas\n",
        "def is_blurry(image_path, threshold=100):\n",
        "    if not os.path.exists(image_path):\n",
        "        raise FileNotFoundError(f\"La imagen {image_path} no existe.\")\n",
        "    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
        "    if image is None:\n",
        "        raise ValueError(f\"No se pudo leer la imagen {image_path}.\")\n",
        "    laplacian_var = cv2.Laplacian(image, cv2.CV_64F).var()\n",
        "    return laplacian_var < threshold\n",
        "\n",
        "image_path = \"/content/dataset/tiny-imagenet-200/train/\"\n",
        "try:\n",
        "    print(f\"¿La imagen está borrosa? {'Sí' if is_blurry(image_path) else 'No'}\")\n",
        "except Exception as e:\n",
        "    print(f\"Error en la detección de borrosidad: {e}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3DHj8oGym18R",
        "outputId": "bcd22b3e-30a9-42d6-c6f5-03420769d583"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error en la detección de borrosidad: La imagen /content/dataset/tiny-imagenet-200/train/ no existe.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Detección de Duplicados\n",
        "def are_images_similar(image1_path, image2_path, threshold=0.9):\n",
        "    if not os.path.exists(image1_path) or not os.path.exists(image2_path):\n",
        "        raise FileNotFoundError(\"Una o ambas imágenes no existen.\")\n",
        "    img1 = cv2.imread(image1_path, cv2.IMREAD_GRAYSCALE)\n",
        "    img2 = cv2.imread(image2_path, cv2.IMREAD_GRAYSCALE)\n",
        "    if img1 is None or img2 is None:\n",
        "        raise ValueError(\"No se pudo leer una o ambas imágenes.\")\n",
        "    similarity, _ = ssim(img1, img2, full=True)\n",
        "    return similarity > threshold\n",
        "\n",
        "image1 = \"/content/dataset/tiny-imagenet-200/train/n01443537/images/n01443537_0.JPEG\"\n",
        "image2 = \"/content/dataset/tiny-imagenet-200/train/n01443537/images/n01443537_1.JPEG\"\n",
        "\n",
        "try:\n",
        "    print(f\"¿Las imágenes son duplicadas? {'Sí' if are_images_similar(image1, image2) else 'No'}\")\n",
        "except Exception as e:\n",
        "    print(f\"Error en la detección de duplicados: {e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ad2-s8vwnB8x",
        "outputId": "fe9b41ab-93f2-485d-be77-09708187b4e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error en la detección de duplicados: Una o ambas imágenes no existen.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def optimize_model_for_mobile(model, output_path):\n",
        "    # Configurar el modelo para evaluación\n",
        "    model.eval()\n",
        "\n",
        "    # Configurar la cuantización con esquema compatible\n",
        "    model.qconfig = torch.quantization.get_default_qconfig('qnnpack')  # Cambiar a qnnpack\n",
        "    print(\"Preparando el modelo para la cuantización...\")\n",
        "\n",
        "    # Preparar y convertir el modelo\n",
        "    model_prepared = torch.quantization.prepare(model)\n",
        "    model_int8 = torch.quantization.convert(model_prepared)\n",
        "\n",
        "    # Guardar el modelo optimizado\n",
        "    scripted_model = torch.jit.script(model_int8)\n",
        "    scripted_model.save(output_path)\n",
        "    print(f\"Modelo optimizado guardado en: {output_path}\")\n",
        "\n",
        "# Ruta para guardar el modelo optimizado\n",
        "optimized_model_path = \"/content/drive/MyDrive/Colab_Notebooks/vit_tiny_imagenet_200_optimized.pt\"\n",
        "optimize_model_for_mobile(model, optimized_model_path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "Jv7WiaeMnBF5",
        "outputId": "4616d107-c964-49cb-9412-fea147c64350"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Preparando el modelo para la cuantización...\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "Can't redefine method: forward on class: __torch__.transformers.models.vit.modeling_vit.ViTEmbeddings (of Python compilation unit at: 0x5a7135991ee0)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-f6dfa8e8e8ab>\u001b[0m in \u001b[0;36m<cell line: 20>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# Ruta para guardar el modelo optimizado\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0moptimized_model_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/drive/MyDrive/Colab_Notebooks/vit_tiny_imagenet_200_optimized.pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0moptimize_model_for_mobile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimized_model_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-17-f6dfa8e8e8ab>\u001b[0m in \u001b[0;36moptimize_model_for_mobile\u001b[0;34m(model, output_path)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;31m# Guardar el modelo optimizado\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mscripted_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscript\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_int8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mscripted_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Modelo optimizado guardado en: {output_path}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_script.py\u001b[0m in \u001b[0;36mscript\u001b[0;34m(obj, optimize, _frames_up, _rcb, example_inputs)\u001b[0m\n\u001b[1;32m   1427\u001b[0m         \u001b[0mprev\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_TOPLEVEL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1428\u001b[0m         \u001b[0m_TOPLEVEL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1429\u001b[0;31m         ret = _script_impl(\n\u001b[0m\u001b[1;32m   1430\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1431\u001b[0m             \u001b[0moptimize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_script.py\u001b[0m in \u001b[0;36m_script_impl\u001b[0;34m(obj, optimize, _frames_up, _rcb, example_inputs)\u001b[0m\n\u001b[1;32m   1145\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1146\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_prepare_scriptable_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1147\u001b[0;31m         return torch.jit._recursive.create_script_module(\n\u001b[0m\u001b[1;32m   1148\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recursive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer_methods_to_compile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_recursive.py\u001b[0m in \u001b[0;36mcreate_script_module\u001b[0;34m(nn_module, stubs_fn, share_types, is_tracing)\u001b[0m\n\u001b[1;32m    555\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_tracing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m         \u001b[0mAttributeTypeIsSupportedChecker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcreate_script_module_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconcrete_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstubs_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_recursive.py\u001b[0m in \u001b[0;36mcreate_script_module_impl\u001b[0;34m(nn_module, concrete_type, stubs_fn)\u001b[0m\n\u001b[1;32m    628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m     \u001b[0;31m# Actually create the ScriptModule, initializing it with the function we just defined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 630\u001b[0;31m     \u001b[0mscript_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRecursiveScriptModule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcpp_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    631\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m     \u001b[0;31m# Compile methods if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_script.py\u001b[0m in \u001b[0;36m_construct\u001b[0;34m(cpp_module, init_fn)\u001b[0m\n\u001b[1;32m    648\u001b[0m             \"\"\"\n\u001b[1;32m    649\u001b[0m             \u001b[0mscript_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRecursiveScriptModule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcpp_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 650\u001b[0;31m             \u001b[0minit_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscript_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m             \u001b[0;31m# Finalize the ScriptModule: replace the nn.Module state with our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_recursive.py\u001b[0m in \u001b[0;36minit_fn\u001b[0;34m(script_module)\u001b[0m\n\u001b[1;32m    604\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m                 \u001b[0;31m# always reuse the provided stubs_fn to infer the methods to compile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 606\u001b[0;31m                 scripted = create_script_module_impl(\n\u001b[0m\u001b[1;32m    607\u001b[0m                     \u001b[0morig_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msub_concrete_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstubs_fn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_recursive.py\u001b[0m in \u001b[0;36mcreate_script_module_impl\u001b[0;34m(nn_module, concrete_type, stubs_fn)\u001b[0m\n\u001b[1;32m    628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m     \u001b[0;31m# Actually create the ScriptModule, initializing it with the function we just defined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 630\u001b[0;31m     \u001b[0mscript_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRecursiveScriptModule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcpp_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    631\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m     \u001b[0;31m# Compile methods if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_script.py\u001b[0m in \u001b[0;36m_construct\u001b[0;34m(cpp_module, init_fn)\u001b[0m\n\u001b[1;32m    648\u001b[0m             \"\"\"\n\u001b[1;32m    649\u001b[0m             \u001b[0mscript_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRecursiveScriptModule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcpp_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 650\u001b[0;31m             \u001b[0minit_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscript_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    652\u001b[0m             \u001b[0;31m# Finalize the ScriptModule: replace the nn.Module state with our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_recursive.py\u001b[0m in \u001b[0;36minit_fn\u001b[0;34m(script_module)\u001b[0m\n\u001b[1;32m    604\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m                 \u001b[0;31m# always reuse the provided stubs_fn to infer the methods to compile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 606\u001b[0;31m                 scripted = create_script_module_impl(\n\u001b[0m\u001b[1;32m    607\u001b[0m                     \u001b[0morig_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msub_concrete_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstubs_fn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_recursive.py\u001b[0m in \u001b[0;36mcreate_script_module_impl\u001b[0;34m(nn_module, concrete_type, stubs_fn)\u001b[0m\n\u001b[1;32m    632\u001b[0m     \u001b[0;31m# Compile methods if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mconcrete_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconcrete_type_store\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethods_compiled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m         create_methods_and_properties_from_stubs(\n\u001b[0m\u001b[1;32m    635\u001b[0m             \u001b[0mconcrete_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod_stubs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproperty_stubs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/jit/_recursive.py\u001b[0m in \u001b[0;36mcreate_methods_and_properties_from_stubs\u001b[0;34m(concrete_type, method_stubs, property_stubs)\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[0mproperty_rcbs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolution_callback\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mproperty_stubs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m     concrete_type._create_methods_and_properties(\n\u001b[0m\u001b[1;32m    467\u001b[0m         \u001b[0mproperty_defs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproperty_rcbs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod_defs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod_rcbs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod_defaults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m     )\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Can't redefine method: forward on class: __torch__.transformers.models.vit.modeling_vit.ViTEmbeddings (of Python compilation unit at: 0x5a7135991ee0)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install torch torchvision\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70Gm4OJdr0ix",
        "outputId": "8a30fa3d-4974-463b-f5d3-4ea05f06956c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.1+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.20.1+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.10.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.26.4)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (11.0.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (3.0.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.models import resnet18\n",
        "\n",
        "# Cargar un modelo ResNet preentrenado\n",
        "resnet_model = resnet18(pretrained=True)\n",
        "resnet_model.eval()\n",
        "\n",
        "# Cuantización\n",
        "resnet_model.qconfig = torch.quantization.get_default_qconfig('fbgemm')\n",
        "resnet_prepared = torch.quantization.prepare(resnet_model)\n",
        "resnet_int8 = torch.quantization.convert(resnet_prepared)\n",
        "\n",
        "# Guardar modelo cuantizado\n",
        "quantized_model_path = \"/content/drive/MyDrive/Colab_Notebooks/resnet18_quantized.pt\"\n",
        "torch.jit.save(torch.jit.script(resnet_int8), quantized_model_path)\n",
        "print(f\"Modelo cuantizado guardado en: {quantized_model_path}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KfJIwbnUsAva",
        "outputId": "8e5412d6-dbb6-4921-dc87-5075e7ad956d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
            "100%|██████████| 44.7M/44.7M [00:00<00:00, 85.6MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Modelo cuantizado guardado en: /content/drive/MyDrive/Colab_Notebooks/resnet18_quantized.pt\n"
          ]
        }
      ]
    }
  ]
}