drakenew.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "V100",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BRIAN12682/Automation-Projects/blob/main/drakenew.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 torch torchvision timm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mNEMujsALo5F",
        "outputId": "81283cd3-c7db-42d4-d688-e85c351dcc10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.2.1+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.17.1+cu121)\n",
            "Collecting timm\n",
            "  Downloading timm-0.9.16-py3-none-any.whl (2.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.14.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.25.2)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from timm) (6.0.1)\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (from timm) (0.20.3)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from timm) (0.4.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (4.66.2)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (24.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (2024.2.2)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, timm\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 timm-0.9.16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')\n",
        "# Setup directory paths to train and test images\n",
        "train_dir = '/content/drive/MyDrive/data_vit/split_dataset/train'\n",
        "test_dir = '/content/drive/MyDrive/data_vit/split_dataset/test'"
      ],
      "metadata": {
        "id": "tvYIuO8pLuac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "c617ca0b-53d5-46dc-f924-b3afd60097b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "MessageError",
          "evalue": "Error: credential propagation was unsuccessful",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mMessageError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-9e906f1219c7>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Mount Google Drive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\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      6\u001b[0m \u001b[0;31m# Setup directory paths to train and test images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mtrain_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/content/drive/MyDrive/data_vit/split_dataset/train'\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/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    101\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\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/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    131\u001b[0m   )\n\u001b[1;32m    132\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m     _message.blocking_request(\n\u001b[0m\u001b[1;32m    134\u001b[0m         \u001b[0;34m'request_auth'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'authType'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'dfs_ephemeral'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    174\u001b[0m       \u001b[0mrequest_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpect_reply\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m   )\n\u001b[0;32m--> 176\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m    101\u001b[0m     ):\n\u001b[1;32m    102\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mMessageError\u001b[0m: Error: credential propagation was unsuccessful"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision import transforms, datasets\n",
        "from torch.utils.data import DataLoader\n",
        "import timm\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Configuration and Hyperparameters\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "batch_size = 32\n",
        "num_epochs = 10\n",
        "learning_rate = 0.0001\n",
        "\n",
        "# Data Transforms\n",
        "train_transforms = transforms.Compose([\n",
        "    transforms.RandomResizedCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "val_transforms = transforms.Compose([\n",
        "    transforms.Resize(256),\n",
        "    transforms.CenterCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Data Loaders\n",
        "train_dataset = datasets.ImageFolder(root=train_dir, transform=train_transforms)\n",
        "val_dataset = datasets.ImageFolder(root=test_dir, transform=val_transforms)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)\n",
        "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)\n",
        "\n",
        "# Model Setup\n",
        "model = timm.create_model('vit_base_patch16_224', pretrained=True, num_classes=len(train_dataset.classes))\n",
        "model.to(device)\n",
        "\n",
        "# Loss and Optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "# Training Loop\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    total_loss = 0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "\n",
        "    for images, labels in train_loader:\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        total_loss += loss.item()\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "    print(f\"Epoch {epoch+1}, Loss: {total_loss/len(train_loader)}, Accuracy: {100 * correct / total}%\")\n",
        "\n",
        "    # Validation\n",
        "    model.eval()\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    with torch.no_grad():\n",
        "        for images, labels in val_loader:\n",
        "            images, labels = images.to(device), labels.to(device)\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "    print(f\"Validation Accuracy: {100 * correct / total}%\")\n",
        "\n",
        "# Save the trained model\n",
        "torch.save(model.state_dict(), 'vit_model.pth')"
      ],
      "metadata": {
        "id": "yNajMVbbljZ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision import transforms, datasets\n",
        "from torch.utils.data import DataLoader\n",
        "import timm\n",
        "\n",
        "# Configuration and Hyperparameters\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "batch_size = 32\n",
        "additional_epochs = 4\n",
        "learning_rate = 0.0001\n",
        "\n",
        "# Data Transforms\n",
        "train_transforms = transforms.Compose([\n",
        "    transforms.RandomResizedCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "val_transforms = transforms.Compose([\n",
        "    transforms.Resize(256),\n",
        "    transforms.CenterCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Data Loaders\n",
        "train_dataset = datasets.ImageFolder(root=train_dir, transform=train_transforms)\n",
        "val_dataset = datasets.ImageFolder(root=test_dir, transform=val_transforms)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)\n",
        "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)\n",
        "\n",
        "# Load and setup model\n",
        "model = timm.create_model('vit_base_patch16_224', pretrained=True, num_classes=len(train_dataset.classes))\n",
        "model.load_state_dict(torch.load('vit_model.pth'))\n",
        "model.to(device)\n",
        "\n",
        "# Loss and Optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "# Initialize best validation accuracy\n",
        "best_val_accuracy = 0\n",
        "\n",
        "# Continue Training Loop\n",
        "for epoch in range(additional_epochs):\n",
        "    model.train()\n",
        "    total_loss = 0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "\n",
        "    for images, labels in train_loader:\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        total_loss += loss.item()\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "    train_accuracy = 100 * correct / total\n",
        "    print(f\"Epoch {epoch+1}, Loss: {total_loss/len(train_loader)}, Train Accuracy: {train_accuracy}%\")\n",
        "\n",
        "    # Validation\n",
        "    model.eval()\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    with torch.no_grad():\n",
        "        for images, labels in val_loader:\n",
        "            images, labels = images.to(device), labels.to(device)\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "    val_accuracy = 100 * correct / total\n",
        "    print(f\"Validation Accuracy: {val_accuracy}%\")\n",
        "\n",
        "    # Checkpoint model if improved\n",
        "    if val_accuracy > best_val_accuracy:\n",
        "        best_val_accuracy = val_accuracy\n",
        "        torch.save(model.state_dict(), 'best_vit_model.pth')\n",
        "        print(\"Saved improved model\")\n",
        "\n",
        "print('Finished additional training!')"
      ],
      "metadata": {
        "id": "rFvqkV8imbKO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install torch torchvision timm\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6O1i17DGboBa",
        "outputId": "29163629-50c5-4a00-ef3f-0167419a3fdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.2.1+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.17.1+cu121)\n",
            "Collecting timm\n",
            "  Downloading timm-0.9.16-py3-none-any.whl (2.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.11.0)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.25.2)\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from timm) (6.0.1)\n",
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            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (4.66.2)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (2024.2.2)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, timm\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 timm-0.9.16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision import transforms, datasets\n",
        "from torch.utils.data import DataLoader\n",
        "import timm\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
        "\n",
        "# Setup device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Define transforms\n",
        "train_transforms = transforms.Compose([\n",
        "    transforms.RandomResizedCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "val_transforms = transforms.Compose([\n",
        "    transforms.Resize(256),\n",
        "    transforms.CenterCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "# Load datasets\n",
        "train_dataset = datasets.ImageFolder(root='/content/tomato/tomato/train', transform=train_transforms)\n",
        "val_dataset = datasets.ImageFolder(root='/content/tomato/tomato/valid', transform=val_transforms)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)\n",
        "\n",
        "# Load the best model\n",
        "model = timm.create_model('vit_base_patch16_224', pretrained=False, num_classes=len(train_dataset.classes))\n",
        "model.load_state_dict(torch.load('best_vit_model.pth'))\n",
        "model.to(device)\n",
        "model.eval()\n",
        "\n",
        "# Define the loss function\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Evaluate on training data\n",
        "train_loss, train_correct, train_total = 0, 0, 0\n",
        "train_preds, train_targets = [], []\n",
        "with torch.no_grad():\n",
        "    for images, labels in train_loader:\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        train_loss += loss.item()\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        train_correct += (predicted == labels).sum().item()\n",
        "        train_total += labels.size(0)\n",
        "        train_preds.extend(predicted.cpu().numpy())\n",
        "        train_targets.extend(labels.cpu().numpy())\n",
        "\n",
        "train_accuracy = accuracy_score(train_targets, train_preds)\n",
        "train_precision = precision_score(train_targets, train_preds, average='weighted')\n",
        "train_recall = recall_score(train_targets, train_preds, average='weighted')\n",
        "train_f1 = f1_score(train_targets, train_preds, average='weighted')\n",
        "\n",
        "# Evaluate on validation data\n",
        "val_correct, val_total = 0, 0\n",
        "val_preds, val_targets = [], []\n",
        "with torch.no_grad():\n",
        "    for images, labels in val_loader:\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        val_correct += (predicted == labels).sum().item()\n",
        "        val_total += labels.size(0)\n",
        "        val_preds.extend(predicted.cpu().numpy())\n",
        "        val_targets.extend(labels.cpu().numpy())\n",
        "\n",
        "val_accuracy = accuracy_score(val_targets, val_preds)\n",
        "val_precision = precision_score(val_targets, val_preds, average='weighted')\n",
        "val_recall = recall_score(val_targets, val_preds, average='weighted')\n",
        "val_f1 = f1_score(val_targets, val_preds, average='weighted')\n",
        "\n",
        "# Print metrics\n",
        "print(f'Train Loss: {train_loss / len(train_loader):.4f}')\n",
        "print(f'Train Accuracy: {train_accuracy:.4f}')\n",
        "print(f'Validation Accuracy: {val_accuracy:.4f}')\n",
        "print(f'Train Precision: {train_precision:.4f}')\n",
        "print(f'Train Recall: {train_recall:.4f}')\n",
        "print(f'Train F1 Score: {train_f1:.4f}')\n",
        "print(f'Validation Precision: {val_precision:.4f}')\n",
        "print(f'Validation Recall: {val_recall:.4f}')\n",
        "print(f'Validation F1 Score: {val_f1:.4f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f37a5abe-ae22-4716-d765-85eddbed3ba2",
        "id": "_y_gseLL3KCg"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0662\n",
            "Train Accuracy: 0.9791\n",
            "Validation Accuracy: 0.9850\n",
            "Train Precision: 0.9798\n",
            "Train Recall: 0.9791\n",
            "Train F1 Score: 0.9791\n",
            "Validation Precision: 0.9862\n",
            "Validation Recall: 0.9850\n",
            "Validation F1 Score: 0.9852\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install einops"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-06Pd0J3urpw",
        "outputId": "56717551-e977-4b66-f6f5-8eb67ca01862"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting einops\n",
            "  Downloading einops-0.7.0-py3-none-any.whl (44 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: einops\n",
            "Successfully installed einops-0.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/hila-chefer/Transformer-Explainability.git\n",
        "import os\n",
        "os.chdir(f'./Transformer-Explainability')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APChiAJMrjmI",
        "outputId": "70c75088-3533-43c7-9ea8-31bb07d0bb5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Transformer-Explainability'...\n",
            "remote: Enumerating objects: 386, done.\u001b[K\n",
            "remote: Counting objects: 100% (9/9), done.\u001b[K\n",
            "remote: Compressing objects: 100% (7/7), done.\u001b[K\n",
            "remote: Total 386 (delta 3), reused 6 (delta 2), pack-reused 377\u001b[K\n",
            "Receiving objects: 100% (386/386), 3.85 MiB | 29.20 MiB/s, done.\n",
            "Resolving deltas: 100% (194/194), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import numpy as np\n",
        "import cv2\n",
        "import timm\n",
        "from PIL import Image\n",
        "from collections import OrderedDict\n",
        "from modules.layers_ours import *\n",
        "from baselines.ViT.ViT_LRP import vit_base_patch16_224 as vit_LRP\n",
        "from baselines.ViT.ViT_explanation_generator import LRP\n",
        "\n",
        "CLS2IDX = {0: 'agricultural',\n",
        "           1: 'airplane',\n",
        "           2: 'baseballdiamond',\n",
        "           3: 'beach',\n",
        "           4: 'buildings',\n",
        "           5: 'chaparral',\n",
        "           6: 'denseresidential',\n",
        "           7: 'forest',\n",
        "           8: 'freeway',\n",
        "           9: 'golfcourse',\n",
        "           10: 'harbor',\n",
        "           11: 'intersection',\n",
        "           12: 'mediumresidential',\n",
        "           13: 'mobilehomepark',\n",
        "           14: 'overpass',\n",
        "           15: 'parkinglot',\n",
        "           16: 'river',\n",
        "           17: 'runway',\n",
        "           18: 'sparseresidential',\n",
        "           19: 'storagetanks',\n",
        "           20: 'tenniscourt'}"
      ],
      "metadata": {
        "id": "V-sHz1marsFv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Perform thresholding on the relevance (using Otsu's method)\n",
        "#@title Number of layers for image Transformer\n",
        "use_thresholding =  False#@param {type:\"boolean\"}"
      ],
      "metadata": {
        "id": "1yGF6Qf6xkFM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean = [0.485, 0.456, 0.406] # our images have three channels\n",
        "std = [0.229, 0.224, 0.225]\n",
        "normalize = transforms.Normalize(mean=mean, std=std)\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((224,224)),\n",
        "    transforms.ToTensor(),\n",
        "    normalize,\n",
        "])\n",
        "resizing =  transforms.Resize((224,224))\n",
        "\n"
      ],
      "metadata": {
        "id": "xsPCUtQ0sKOr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 10\n",
        "model =  vit_LRP(pretrained=True)\n",
        "num_ftrs = model.head.in_features\n",
        "model.head = Linear(num_ftrs, num_classes)\n",
        "# load state dictionary\n",
        "state_dict = torch.load('/content/best_vit_model.pth')\n",
        "print(\"[Loading Weights to the Model]\")\n",
        "model.load_state_dict(state_dict)\n",
        "model.cuda() # put model on GPU\n",
        "model.eval()\n",
        "attribution_generator = LRP(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-8nto3isgoH",
        "outputId": "8a7b2813-24cf-45ff-fd80-cc6b7e430b9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Loading Weights to the Model]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from baselines.ViT.ViT_LRP import vit_base_patch16_224 as vit_LRP\n",
        "from baselines.ViT.ViT_explanation_generator import LRP\n",
        "\n",
        "normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize(256),\n",
        "    transforms.CenterCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    normalize,\n",
        "])\n",
        "\n",
        "# create heatmap from mask on image\n",
        "def show_cam_on_image(img, mask):\n",
        "    heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)\n",
        "    heatmap = np.float32(heatmap) / 255\n",
        "    cam = heatmap + np.float32(img)\n",
        "    cam = cam / np.max(cam)\n",
        "    return cam\n",
        "\n",
        "# Initialize the model\n",
        "model = vit_LRP(pretrained=False, num_classes=10)  # Ensure num_classes matches your dataset\n",
        "model = model.cuda()\n",
        "\n",
        "# Load the saved state dictionary\n",
        "state_dict = torch.load('/content/best_vit_model.pth')\n",
        "model.load_state_dict(state_dict)\n",
        "\n",
        "model.eval()  # Set the model to evaluation mode\n",
        "attribution_generator = LRP(model)\n",
        "\n",
        "\n",
        "def generate_visualization(original_image, class_index=None):\n",
        "    transformer_attribution = attribution_generator.generate_LRP(original_image.unsqueeze(0).cuda(), method=\"transformer_attribution\", index=class_index).detach()\n",
        "    transformer_attribution = transformer_attribution.reshape(1, 1, 14, 14)\n",
        "    transformer_attribution = torch.nn.functional.interpolate(transformer_attribution, scale_factor=16, mode='bilinear')\n",
        "    transformer_attribution = transformer_attribution.reshape(224, 224).data.cpu().numpy()\n",
        "    transformer_attribution = (transformer_attribution - transformer_attribution.min()) / (transformer_attribution.max() - transformer_attribution.min())\n",
        "\n",
        "    if use_thresholding:\n",
        "      transformer_attribution = transformer_attribution * 255\n",
        "      transformer_attribution = transformer_attribution.astype(np.uint8)\n",
        "      ret, transformer_attribution = cv2.threshold(transformer_attribution, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
        "      transformer_attribution[transformer_attribution == 255] = 1\n",
        "\n",
        "    image_transformer_attribution = original_image.permute(1, 2, 0).data.cpu().numpy()\n",
        "    image_transformer_attribution = (image_transformer_attribution - image_transformer_attribution.min()) / (image_transformer_attribution.max() - image_transformer_attribution.min())\n",
        "    vis = show_cam_on_image(image_transformer_attribution, transformer_attribution)\n",
        "    vis =  np.uint8(255 * vis)\n",
        "    vis = cv2.cvtColor(np.array(vis), cv2.COLOR_RGB2BGR)\n",
        "    return vis\n",
        "\n",
        "def print_top_classes(predictions, **kwargs):\n",
        "    # Print Top-5 predictions\n",
        "    prob = torch.softmax(predictions, dim=1)\n",
        "    class_indices = predictions.data.topk(5, dim=1)[1][0].tolist()\n",
        "    max_str_len = 0\n",
        "    class_names = []\n",
        "    for cls_idx in class_indices:\n",
        "        class_names.append(CLS2IDX[cls_idx])\n",
        "        if len(CLS2IDX[cls_idx]) > max_str_len:\n",
        "            max_str_len = len(CLS2IDX[cls_idx])\n",
        "\n",
        "    print('Top 5 classes:')\n",
        "    for cls_idx in class_indices:\n",
        "        output_string = '\\t{} : {}'.format(cls_idx, CLS2IDX[cls_idx])\n",
        "        output_string += ' ' * (max_str_len - len(CLS2IDX[cls_idx])) + '\\t\\t'\n",
        "        output_string += 'value = {:.3f}\\t prob = {:.1f}%'.format(predictions[0, cls_idx], 100 * prob[0, cls_idx])\n",
        "        print(output_string)"
      ],
      "metadata": {
        "id": "iLH_F0FRyDbN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image = Image.open('/content/tomato/tomato/valid/Tomato___Spider_mites Two-spotted_spider_mite/0a246e71-0551-4603-a017-d1dfc4ea06be___Com.G_SpM_FL 1178.JPG')\n",
        "tomato_image = transform(image)\n",
        "\n",
        "fig, axs = plt.subplots(1, 2)\n",
        "axs[0].imshow(image);\n",
        "axs[0].axis('off');\n",
        "\n",
        "output = model(tomato_image.unsqueeze(0).cuda())\n",
        "print_top_classes(output)\n",
        "\n",
        "# cat - the predicted class\n",
        "Tomato___Bacterial_spot = generate_visualization(tomato_image)\n",
        "\n",
        "tomato = generate_visualization(image, class_index=5)\n",
        "\n",
        "axs[1].imshow(Tomato___Bacterial_spot );\n",
        "#axs[1].axis('off');\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "Fcov_Vu2O8MP",
        "outputId": "07948f02-8f8b-4d0b-9a89-e3688df52647"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 5 classes:\n",
            "\t9 : Tomato___healthy           \t\tvalue = -3.504\t prob = 71.2%\n",
            "\t6 : Tomato___Target_Spot       \t\tvalue = -5.257\t prob = 12.3%\n",
            "\t5 : Tomato___Septoria_leaf_spot\t\tvalue = -5.668\t prob = 8.2%\n",
            "\t3 : Tomato___Leaf_Mold         \t\tvalue = -6.360\t prob = 4.1%\n",
            "\t4 : Tomato___Septoria_leaf_spot\t\tvalue = -7.220\t prob = 1.7%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}