Preprocess_and_ResNet.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Load the data and Pre-process"
      ],
      "metadata": {
        "id": "buRDHP3Um4Pa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## import libraries"
      ],
      "metadata": {
        "id": "-IcgUf1_RbOF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "import torch\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.utils.data import ConcatDataset\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.nn import *\n",
        "import torch,torchvision\n",
        "from torchvision.models import *\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision.transforms import Compose, RandomHorizontalFlip, RandomRotation, RandomResizedCrop, ColorJitter, ToPILImage, ToTensor\n",
        "import matplotlib.image as image\n",
        "from google.colab import drive\n"
      ],
      "metadata": {
        "id": "NMxWAdwDvX-b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# connect to google drive\n",
        "drive.mount('/content/drive')\n",
        "import os\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = \"/content/drive/MyDrive/CS 679 project\"\n",
        "!kaggle datasets download -d miljan/stanford-dogs-dataset-traintest --unzip\n",
        "folder_path = '/content/cropped/train'\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "JUvW_wdbRVyJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load data"
      ],
      "metadata": {
        "id": "eJblUr3_Rd7u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_data(folder_path, device):\n",
        "    labels = {}\n",
        "    idx = 0\n",
        "    images = []\n",
        "    labels_r = {}\n",
        "    for folder in os.listdir(folder_path):\n",
        "        idx += 1\n",
        "        labels[folder] = idx\n",
        "        labels_r[idx] = folder\n",
        "    for folder in os.listdir(folder_path):\n",
        "        for file in os.listdir(os.path.join(folder_path, folder)):\n",
        "            img_path = os.path.join(folder_path, folder, file)\n",
        "            img = Image.open(img_path).convert(\"RGB\")\n",
        "            img = np.array(img)\n",
        "            images.append([img, np.eye(labels[folder] + 1, len(labels))[labels[folder]]])\n",
        "    np.random.shuffle(images)\n",
        "    X = []\n",
        "    y = []\n",
        "    for d in images:\n",
        "        X.append(d[0])\n",
        "        y.append(d[1])\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)\n",
        "    X_train = torch.from_numpy(np.array(X_train)).to(device).permute(0, 3, 1, 2)\n",
        "    y_train = torch.from_numpy(np.array(y_train)).to(device).float()\n",
        "    X_test = torch.from_numpy(np.array(X_test)).to(device).permute(0, 3, 1, 2)\n",
        "    y_test = torch.from_numpy(np.array(y_test)).to(device).float()\n",
        "    return X, y, X_train, X_test, y_train, y_test, labels, labels_r, idx, images\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "YJesfR_yu9Am"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "folder_path = \"/content/cropped/train\"\n",
        "X, y, X_train, X_test, y_train, y_test, labels, labels_r, idx, data = load_data(folder_path, device)"
      ],
      "metadata": {
        "id": "lSeWWwDYwtc1"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pre-process data(data augmentation) and read data into dataloader"
      ],
      "metadata": {
        "id": "4VhVDblfRg7P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomImageDataset(Dataset):\n",
        "    def __init__(self, images, labels, transform=None):\n",
        "        self.images = images\n",
        "        self.labels = labels\n",
        "        self.transform = transform\n",
        "        self.to_tensor = ToTensor()\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.images)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        image = self.images[idx % len(self.images)]\n",
        "        label = self.labels[idx % len(self.labels)]\n",
        "        image = Image.fromarray(image.transpose(1, 2, 0).astype(np.uint8), mode='RGB')\n",
        "\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "\n",
        "        # Convert the image to a tensor\n",
        "        image = self.to_tensor(image)\n",
        "\n",
        "        return image, torch.tensor(label, dtype=torch.float32)\n",
        "\n",
        "data_augmentation_transforms = Compose([\n",
        "    # RandomResizedCrop(224, scale=(0.8, 1.0)),\n",
        "    # RandomHorizontalFlip(),\n",
        "    RandomRotation(30)\n",
        "    # ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)\n",
        "])"
      ],
      "metadata": {
        "id": "8pXjW1yjwDbu"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 8\n",
        "\n",
        "# Create an instance of the CustomImageDataset for the original images\n",
        "original_dataset = CustomImageDataset(X_train.cpu().numpy(), y_train.cpu().numpy())\n",
        "\n",
        "# Create an instance of the CustomImageDataset with the data augmentation transforms\n",
        "augmented_dataset = CustomImageDataset(X_train.cpu().numpy(), y_train.cpu().numpy(), transform=data_augmentation_transforms)\n",
        "\n",
        "\n",
        "# Concatenate the original and augmented datasets\n",
        "combined_dataset = ConcatDataset([original_dataset, augmented_dataset])\n",
        "\n",
        "# Create a DataLoader with the desired batch size for the augmented dataset\n",
        "combined_data_loader = DataLoader(combined_dataset, batch_size=batch_size, shuffle=True, num_workers=0)"
      ],
      "metadata": {
        "id": "G2cZUE1kwPbK"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train Resnet18"
      ],
      "metadata": {
        "id": "Q-Ph3McgS45p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision.models import resnet18\n",
        "\n",
        "num_classes = 120\n",
        "model = resnet18(pretrained=True)\n",
        "model.fc = nn.Linear(512, num_classes)\n",
        "model = model.to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
        "# optimizer = torch.optim.Adam(model.parameters(),lr=0.001)\n",
        "\n",
        "num_epochs = 10\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "  running_loss = 0.0\n",
        "  i = 0\n",
        "\n",
        "  for inputs, targets in combined_data_loader:\n",
        "    inputs = inputs.to(device)\n",
        "    targets = targets.to(device)\n",
        "        \n",
        "    optimizer.zero_grad()\n",
        "    \n",
        "    outputs = model(inputs)\n",
        "    targets = torch.argmax(targets, dim=1)\n",
        "    loss = criterion(outputs, targets)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    \n",
        "    running_loss += loss.item()\n",
        "\n",
        "    print(f\"Epoch {epoch+1}, Loss: {running_loss / (i+1)}\")\n",
        "    i += 1\n",
        "\n",
        "print(\"Finished Training\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MPJPWpgX1o5H",
        "outputId": "93ee68ed-373e-4be5-8dc9-e0a594270f3f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Epoch 8, Loss: 0.15793686159391576\n",
            "Epoch 8, Loss: 0.1578814615311606\n",
            "Epoch 8, Loss: 0.1578695738355134\n",
            "Epoch 8, Loss: 0.15786520830374592\n",
            "Epoch 8, Loss: 0.15784188357696058\n",
            "Epoch 8, Loss: 0.15780516260560545\n",
            "Epoch 8, Loss: 0.15776389844435523\n",
            "Epoch 8, Loss: 0.15774643004038394\n",
            "Epoch 8, Loss: 0.15768742535465577\n",
            "Epoch 8, Loss: 0.15764771998754215\n",
            "Epoch 8, Loss: 0.15759676325625013\n",
            "Epoch 8, Loss: 0.15754195192380158\n",
            "Epoch 8, Loss: 0.15749090716542294\n",
            "Epoch 8, Loss: 0.15747419941926513\n",
            "Epoch 8, Loss: 0.15741932890815022\n",
            "Epoch 8, Loss: 0.15737926688870305\n",
            "Epoch 8, Loss: 0.15742786891499788\n",
            "Epoch 8, Loss: 0.15740688634115518\n",
            "Epoch 8, Loss: 0.15735040096364714\n",
            "Epoch 8, Loss: 0.15730076017950984\n",
            "Epoch 8, Loss: 0.15725499742708826\n",
            "Epoch 8, Loss: 0.15723244393151378\n",
            "Epoch 8, Loss: 0.15720906526121653\n",
            "Epoch 8, Loss: 0.15714775247938848\n",
            "Epoch 8, Loss: 0.1571486776825191\n",
            "Epoch 8, Loss: 0.15709366595943813\n",
            "Epoch 8, Loss: 0.1570394538347969\n",
            "Epoch 8, Loss: 0.15702061309571516\n",
            "Epoch 8, Loss: 0.15696356307648357\n",
            "Epoch 8, Loss: 0.156961787638143\n",
            "Epoch 8, Loss: 0.15697893783813976\n",
            "Epoch 8, Loss: 0.15692977792680585\n",
            "Epoch 8, Loss: 0.1568836181807259\n",
            "Epoch 8, Loss: 0.157107446680233\n",
            "Epoch 8, Loss: 0.15712092405231753\n",
            "Epoch 8, Loss: 0.15705693359155778\n",
            "Epoch 8, Loss: 0.1570019048124292\n",
            "Epoch 8, Loss: 0.15704439084164967\n",
            "Epoch 8, Loss: 0.1570432744807996\n",
            "Epoch 8, Loss: 0.1570305440766884\n",
            "Epoch 8, Loss: 0.15707730722092725\n",
            "Epoch 8, Loss: 0.15701602583911356\n",
            "Epoch 8, Loss: 0.15697338475111985\n",
            "Epoch 8, Loss: 0.15696944104412153\n",
            "Epoch 8, Loss: 0.15693963482811754\n",
            "Epoch 8, Loss: 0.1568770180358939\n",
            "Epoch 8, Loss: 0.15681447048905203\n",
            "Epoch 8, Loss: 0.1567767418953294\n",
            "Epoch 8, Loss: 0.15684089165222315\n",
            "Epoch 8, Loss: 0.15678201783415358\n",
            "Epoch 8, Loss: 0.1567440999032463\n",
            "Epoch 8, Loss: 0.15685976235608537\n",
            "Epoch 8, Loss: 0.156830190344174\n",
            "Epoch 8, Loss: 0.15677480421869494\n",
            "Epoch 8, Loss: 0.1567816398026428\n",
            "Epoch 8, Loss: 0.1567215880199171\n",
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            "Epoch 8, Loss: 0.15663896448358036\n",
            "Epoch 8, Loss: 0.15657739679723293\n",
            "Epoch 8, Loss: 0.15653561939757935\n",
            "Epoch 8, Loss: 0.156517302501787\n",
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            "Epoch 8, Loss: 0.15646429005023463\n",
            "Epoch 8, Loss: 0.15644147709737033\n",
            "Epoch 8, Loss: 0.15642931819805095\n",
            "Epoch 8, Loss: 0.15640382180310708\n",
            "Epoch 8, Loss: 0.15636036969654002\n",
            "Epoch 8, Loss: 0.15640728140889876\n",
            "Epoch 8, Loss: 0.1563683410430633\n",
            "Epoch 8, Loss: 0.1563181840772839\n",
            "Epoch 8, Loss: 0.15626466904307548\n",
            "Epoch 8, Loss: 0.15621110473258998\n",
            "Epoch 8, Loss: 0.1562283897927993\n",
            "Epoch 8, Loss: 0.1562422745929039\n",
            "Epoch 8, Loss: 0.1562215241758569\n",
            "Epoch 8, Loss: 0.15619425767381806\n",
            "Epoch 8, Loss: 0.15618961119755428\n",
            "Epoch 8, Loss: 0.1561737887696118\n",
            "Epoch 8, Loss: 0.15629302547343896\n",
            "Epoch 8, Loss: 0.15635698776501153\n",
            "Epoch 8, Loss: 0.15635391041200788\n",
            "Epoch 8, Loss: 0.15629498776408882\n",
            "Epoch 8, Loss: 0.15631360811114114\n",
            "Epoch 8, Loss: 0.15627159011425582\n",
            "Epoch 8, Loss: 0.1562217955196958\n",
            "Epoch 8, Loss: 0.15627279540017522\n",
            "Epoch 8, Loss: 0.15625719484609057\n",
            "Epoch 8, Loss: 0.1563554074310222\n",
            "Epoch 8, Loss: 0.1563294679255744\n",
            "Epoch 8, Loss: 0.15632071511220205\n",
            "Epoch 8, Loss: 0.15627710298036793\n",
            "Epoch 8, Loss: 0.15625017953536424\n",
            "Epoch 8, Loss: 0.15622847428938988\n",
            "Epoch 8, Loss: 0.15625362316972352\n",
            "Epoch 8, Loss: 0.15629647446461395\n",
            "Epoch 8, Loss: 0.1562503207595719\n",
            "Epoch 8, Loss: 0.15626999645940748\n",
            "Epoch 8, Loss: 0.15625173894162087\n",
            "Epoch 8, Loss: 0.1562113702829048\n",
            "Epoch 8, Loss: 0.15624903065947854\n",
            "Epoch 8, Loss: 0.15632487263342854\n",
            "Epoch 8, Loss: 0.15639942692885395\n",
            "Epoch 8, Loss: 0.15638089769129962\n",
            "Epoch 8, Loss: 0.15633115282279003\n",
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            "Epoch 8, Loss: 0.15623487654364823\n",
            "Epoch 8, Loss: 0.1561887941487382\n",
            "Epoch 8, Loss: 0.1562128724800463\n",
            "Epoch 8, Loss: 0.15615416164515997\n",
            "Epoch 8, Loss: 0.1561059559897639\n",
            "Epoch 8, Loss: 0.15614389457570374\n",
            "Epoch 8, Loss: 0.15609844367630787\n",
            "Epoch 8, Loss: 0.1561078289411077\n",
            "Epoch 8, Loss: 0.15605109522966845\n",
            "Epoch 8, Loss: 0.1560192505913848\n",
            "Epoch 8, Loss: 0.1560324663613747\n",
            "Epoch 8, Loss: 0.1560738579088556\n",
            "Epoch 8, Loss: 0.1560433788726845\n",
            "Epoch 8, Loss: 0.156000225383424\n",
            "Epoch 8, Loss: 0.1562497175024288\n",
            "Epoch 8, Loss: 0.15637539453071622\n",
            "Epoch 8, Loss: 0.15633196702263866\n",
            "Epoch 8, Loss: 0.1562723125737681\n",
            "Epoch 8, Loss: 0.1562230590140721\n",
            "Epoch 8, Loss: 0.15617274430023725\n",
            "Epoch 8, Loss: 0.15612234300781758\n",
            "Epoch 8, Loss: 0.15616991368796998\n",
            "Epoch 8, Loss: 0.15613575535326749\n",
            "Epoch 8, Loss: 0.15611172007322502\n",
            "Epoch 8, Loss: 0.15615961902397868\n",
            "Epoch 8, Loss: 0.15609909110194398\n",
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            "Epoch 8, Loss: 0.15598840615839016\n",
            "Epoch 8, Loss: 0.15592818750280885\n",
            "Epoch 8, Loss: 0.15589596560185093\n",
            "Epoch 8, Loss: 0.15589458439179338\n",
            "Epoch 8, Loss: 0.15586934502787386\n",
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            "Epoch 8, Loss: 0.15566571769213344\n",
            "Epoch 8, Loss: 0.15561002601636573\n",
            "Epoch 9, Loss: 0.022222306579351425\n",
            "Epoch 9, Loss: 0.10672967694699764\n",
            "Epoch 9, Loss: 0.10898736988504727\n",
            "Epoch 9, Loss: 0.0884377034381032\n",
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            "Epoch 9, Loss: 0.10726039148867131\n",
            "Epoch 9, Loss: 0.10653157362883742\n",
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            "Epoch 10, Loss: 0.10572458402308242\n",
            "Epoch 10, Loss: 0.10570955101228449\n",
            "Epoch 10, Loss: 0.10570540483966999\n",
            "Epoch 10, Loss: 0.10567039781729926\n",
            "Epoch 10, Loss: 0.10564684237971274\n",
            "Epoch 10, Loss: 0.10572674882954029\n",
            "Epoch 10, Loss: 0.10571963636407501\n",
            "Epoch 10, Loss: 0.1056947233650754\n",
            "Epoch 10, Loss: 0.10567423107713815\n",
            "Epoch 10, Loss: 0.1056332333240908\n",
            "Epoch 10, Loss: 0.1057221074984409\n",
            "Finished Training\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = CustomImageDataset(X_test.cpu().numpy(), y_test.cpu().numpy())\n",
        "test_data_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for inputs, targets in test_data_loader:\n",
        "      inputs = inputs.to(device)\n",
        "      targets = targets.to(device)\n",
        "        \n",
        "      outputs = model(inputs)\n",
        "      predicted = torch.argmax(targets, dim=1)\n",
        "      true_labels = torch.argmax(outputs, dim=1)\n",
        "        \n",
        "      total += targets.size(0)\n",
        "      correct += (predicted == true_labels).sum().item()\n",
        "\n",
        "print(f\"Accuracy on test set: {100 * correct / total}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-I6VdIG2ygXG",
        "outputId": "3eee919b-1524-49a8-e450-b7e30310821d"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 68.45833333333333%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train VGG nn"
      ],
      "metadata": {
        "id": "EgITjfW8HXnv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 120\n",
        "model = vgg16(pretrained=True)\n",
        "\n",
        "# Modify the last layer of the classifier to match the number of classes in your dataset\n",
        "model.classifier[6] = nn.Linear(4096, num_classes)\n",
        "\n",
        "# Move the model to the device\n",
        "model = model.to(device)\n",
        "\n",
        "# Set the loss function and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)"
      ],
      "metadata": {
        "id": "aNMaG0XDKBKk"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "num_epochs = 10\n",
        "loss_history = []\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    running_loss = 0.0\n",
        "    i = 0\n",
        "\n",
        "    for inputs, targets in combined_data_loader:\n",
        "        inputs = inputs.to(device)\n",
        "        targets = targets.to(device)\n",
        "        \n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        outputs = model(inputs)\n",
        "        targets = torch.argmax(targets, dim=1)\n",
        "        loss = criterion(outputs, targets)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        running_loss += loss.item()\n",
        "        i += 1\n",
        "        epoch_loss = running_loss / i\n",
        "        print(f\"Epoch {epoch+1}, Loss: {epoch_loss}\")\n",
        "\n",
        "    epoch_loss = running_loss / i\n",
        "    loss_history.append(epoch_loss)\n",
        "\n",
        "print(\"Finished Training\")\n",
        "\n",
        "# Plot the loss curve\n",
        "plt.plot(loss_history)\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Loss curve\")\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MCrSwIbVHaOk",
        "outputId": "caf2277c-dd2b-4b91-fe83-86ddd1bdb070"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Epoch 8, Loss: 0.2258422312503348\n",
            "Epoch 8, Loss: 0.2257811255649717\n",
            "Epoch 8, Loss: 0.22569606874572481\n",
            "Epoch 8, Loss: 0.22570020716796158\n",
            "Epoch 8, Loss: 0.2256724322984602\n",
            "Epoch 8, Loss: 0.22560767047746924\n",
            "Epoch 8, Loss: 0.2256981788450964\n",
            "Epoch 8, Loss: 0.22567007190661267\n",
            "Epoch 8, Loss: 0.22556862509629674\n",
            "Epoch 8, Loss: 0.22563999331360154\n",
            "Epoch 8, Loss: 0.2257328085264649\n",
            "Epoch 8, Loss: 0.22578994488211215\n",
            "Epoch 8, Loss: 0.22611412033911257\n",
            "Epoch 8, Loss: 0.22604465614593694\n",
            "Epoch 8, Loss: 0.22632514468331782\n",
            "Epoch 8, Loss: 0.22626008186919272\n",
            "Epoch 8, Loss: 0.22619928606830106\n",
            "Epoch 8, Loss: 0.22614701675263482\n",
            "Epoch 8, Loss: 0.22605656170765928\n",
            "Epoch 8, Loss: 0.2260175382699299\n",
            "Epoch 8, Loss: 0.2261152325478182\n",
            "Epoch 8, Loss: 0.22601719908141205\n",
            "Epoch 8, Loss: 0.22598947999713934\n",
            "Epoch 8, Loss: 0.22590386686207636\n",
            "Epoch 8, Loss: 0.2260048081856683\n",
            "Epoch 8, Loss: 0.225906419907437\n",
            "Epoch 8, Loss: 0.22591524261069756\n",
            "Epoch 8, Loss: 0.22589056484182052\n",
            "Epoch 8, Loss: 0.22580459133142813\n",
            "Epoch 8, Loss: 0.22579749079380018\n",
            "Epoch 8, Loss: 0.22583481916995637\n",
            "Epoch 8, Loss: 0.22577641374723684\n",
            "Epoch 8, Loss: 0.22587164459616418\n",
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            "Epoch 8, Loss: 0.2260232483398747\n",
            "Epoch 8, Loss: 0.22594037023569966\n",
            "Epoch 8, Loss: 0.22586007704514677\n",
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            "Epoch 8, Loss: 0.22593068051096035\n",
            "Epoch 8, Loss: 0.22588753964092367\n",
            "Epoch 8, Loss: 0.2260012744126715\n",
            "Epoch 8, Loss: 0.22590414039086823\n",
            "Epoch 8, Loss: 0.2258765385889376\n",
            "Epoch 8, Loss: 0.2257882432277823\n",
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            "Epoch 8, Loss: 0.22586191707859204\n",
            "Epoch 8, Loss: 0.22597127691457194\n",
            "Epoch 8, Loss: 0.22587350079965698\n",
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            "Epoch 8, Loss: 0.22608894863656548\n",
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            "Epoch 8, Loss: 0.2260475815870495\n",
            "Epoch 8, Loss: 0.22602424394382284\n",
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            "Epoch 8, Loss: 0.2255874466005608\n",
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            "Epoch 9, Loss: 0.049385134130716324\n",
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            "Epoch 10, Loss: 0.16527222250010407\n",
            "Finished Training\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = CustomImageDataset(X_test.cpu().numpy(), y_test.cpu().numpy())\n",
        "test_data_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for inputs, targets in test_data_loader:\n",
        "        inputs = inputs.to(device)\n",
        "        targets = targets.to(device)\n",
        "        \n",
        "        outputs = model(inputs)\n",
        "        predicted = torch.argmax(outputs, dim=1)\n",
        "        true_labels = torch.argmax(targets, dim=1)\n",
        "        \n",
        "        total += targets.size(0)\n",
        "        correct += (predicted == true_labels).sum().item()\n",
        "\n",
        "print(f\"Accuracy on test set: {100 * correct / total}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mH1UgC0HdMd",
        "outputId": "3b56fea4-be10-4c7e-b461-860de4bb1d56"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 66.625%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Siamese nn"
      ],
      "metadata": {
        "id": "8jwATIaJW_N4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pair images dataset"
      ],
      "metadata": {
        "id": "Q6Fhb2VRXCqA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ImagePairsDataset(Dataset):\n",
        "    def __init__(self, images, labels, transform=None):\n",
        "        self.images = images\n",
        "        self.labels = labels\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.images)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img1 = self.images[idx]\n",
        "        label1 = self.labels[idx]\n",
        "\n",
        "        idx2 = np.random.choice(len(self.images))\n",
        "        img2 = self.images[idx2]\n",
        "        label2 = self.labels[idx2]\n",
        "\n",
        "        return (img1, img2), (label1, label2)"
      ],
      "metadata": {
        "id": "wVVF2URcXYqw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sift and lbp"
      ],
      "metadata": {
        "id": "0r2KCQcVXcQ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_sift_features(image):\n",
        "    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n",
        "    sift = cv2.SIFT_create()\n",
        "    kp, des = sift.detectAndCompute(gray_image, None)\n",
        "    return des\n",
        "\n",
        "def extract_lbp_features(image, num_points, radius, method=\"uniform\"):\n",
        "    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n",
        "    lbp = feature.local_binary_pattern(gray_image, num_points, radius, method)\n",
        "    hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, num_points + 3), range=(0, num_points + 2))\n",
        "    hist = hist.astype(\"float\")\n",
        "    hist /= (hist.sum() + 1e-6)\n",
        "    return hist"
      ],
      "metadata": {
        "id": "arB7-KHYXjHm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pair features datasets"
      ],
      "metadata": {
        "id": "4C1Uxw8aXnsp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ImagePairsFeatureDataset(Dataset):\n",
        "    def __init__(self, images, targets, num_lbp_points, lbp_radius):\n",
        "        self.images = images\n",
        "        self.targets = targets\n",
        "        self.num_lbp_points = num_lbp_points\n",
        "        self.lbp_radius = lbp_radius\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.images)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img1 = self.images[idx].astype('uint8')\n",
        "        img2 = self.images[(idx + 1) % len(self.images)].astype('uint8')\n",
        "\n",
        "        # Ensure that the images have 3 channels\n",
        "        if img1.shape[2] != 3:\n",
        "            img1 = cv2.cvtColor(img1, cv2.COLOR_GRAY2RGB)\n",
        "        if img2.shape[2] != 3:\n",
        "            img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2RGB)\n",
        "\n",
        "        img1_sift = extract_sift_features(img1)\n",
        "        img2_sift = extract_sift_features(img2)\n",
        "        img1_lbp = extract_lbp_features(img1, self.num_lbp_points, self.lbp_radius)\n",
        "        img2_lbp = extract_lbp_features(img2, self.num_lbp_points, self.lbp_radius)\n",
        "\n",
        "        img1_features = np.concatenate((img1_sift, img1_lbp))\n",
        "        img2_features = np.concatenate((img2_sift, img2_lbp))\n",
        "\n",
        "        label1 = self.targets[idx]\n",
        "        label2 = self.targets[(idx + 1) % len(self.targets)]\n",
        "\n",
        "        return (torch.tensor(img1_features).float(), torch.tensor(img2_features).float()), (label1, label2)"
      ],
      "metadata": {
        "id": "Y7K8EDzdXyBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Siamese nn architecture"
      ],
      "metadata": {
        "id": "W3RGJha1X5ar"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class SiameseNetwork(nn.Module):\n",
        "    def __init__(self, feature_dim, num_classes):\n",
        "        super(SiameseNetwork, self).__init__()\n",
        "        self.fc = nn.Sequential(\n",
        "            nn.Linear(feature_dim, 512),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Linear(512, 256),\n",
        "            nn.ReLU(inplace=True),\n",
        "            nn.Linear(256, num_classes),\n",
        "            nn.Softmax(dim=1)\n",
        "        )\n",
        "\n",
        "    def forward(self, x1, x2):\n",
        "        output1 = self.fc(x1)\n",
        "        output2 = self.fc(x2)\n",
        "        return output1, output2"
      ],
      "metadata": {
        "id": "uZSCXeegX8ru"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training and evaluation"
      ],
      "metadata": {
        "id": "AePFeBV9YB0R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_siamese_network(model, dataloader, criterion, optimizer, device, num_epochs=10):\n",
        "    model.train()\n",
        "    for epoch in range(num_epochs):\n",
        "        running_loss = 0.0\n",
        "        i = 0\n",
        "        for (inputs1, inputs2), targets in dataloader:\n",
        "            inputs1 = inputs1.to(device)\n",
        "            inputs2 = inputs2.to(device)\n",
        "            targets = targets.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            outputs1, outputs2 = model(inputs1, inputs2)\n",
        "            loss = criterion(outputs1, targets) + criterion(outputs2, targets)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            running_loss += loss.item()\n",
        "            i += 1\n",
        "            print(f\"Epoch {epoch + 1}, Loss: {running_loss / i}\")\n",
        "\n",
        "    print(\"Finished Training\")"
      ],
      "metadata": {
        "id": "O2e79IhmYBLc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_siamese_network(model, dataloader, device):\n",
        "    model.eval()\n",
        "    correct = 0\n",
        "    total = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for (inputs1, inputs2), targets in dataloader:\n",
        "            inputs1 = inputs1.to(device)\n",
        "            inputs2 = inputs2.to(device)\n",
        "            targets = targets.to(device)\n",
        "\n",
        "            outputs1, outputs2 = model(inputs1, inputs2)\n",
        "            predicted1 = torch.argmax(outputs1, dim=1)\n",
        "            predicted2 = torch.argmax(outputs2, dim=1)\n",
        "\n",
        "            correct += (predicted1 == targets).sum().item() + (predicted2 == targets).sum().item()\n",
        "            total += 2 * targets.size(0)\n",
        "\n",
        "    print(f\"Accuracy: {100 * correct / total}%\")"
      ],
      "metadata": {
        "id": "Oz-uK0-eYKku"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_lbp_points = 24\n",
        "lbp_radius = 3\n",
        "\n",
        "train_pairs_dataset = ImagePairsFeatureDataset(X_train.cpu().numpy(), y_train.cpu().numpy(), num_lbp_points, lbp_radius)\n",
        "test_pairs_dataset = ImagePairsFeatureDataset(X_test.cpu().numpy(), y_test.cpu().numpy(), num_lbp_points, lbp_radius)\n",
        "\n",
        "batch_size = 32\n",
        "train_pairs_loader = DataLoader(train_pairs_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
        "test_pairs_loader = DataLoader(test_pairs_dataset, batch_size=batch_size, shuffle=False, num_workers=2)\n",
        "\n",
        "feature_dim = 128 + num_lbp_points * 2  # 128 for SIFT, num_lbp_points * 2 for LBP\n",
        "\n",
        "siamese_net = SiameseNetwork(feature_dim, num_classes = 120).to(device)\n",
        "\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = SiameseNetwork(feature_dim, num_classes = 120).to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(),lr=0.001)"
      ],
      "metadata": {
        "id": "yuKS7x-1YM8r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 10\n",
        "running_loss_history = []\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    running_loss = 0.0\n",
        "\n",
        "    for i, ((img1_features, img2_features), (label1, label2)) in enumerate(train_pairs_loader):\n",
        "        img1_features = img1_features.to(device)\n",
        "        img2_features = img2_features.to(device)\n",
        "        label1 = label1.to(device)\n",
        "        label2 = label2.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        output1, output2 = siamese_net(img1_features, img2_features)\n",
        "        loss = criterion(output1, output2, label1, label2)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "\n",
        "    epoch_loss = running_loss / (i + 1)\n",
        "    running_loss_history.append(epoch_loss)\n",
        "    print(f\"Epoch {epoch+1}, Loss: {epoch_loss}\")\n",
        "\n",
        "print(\"Finished Training\")"
      ],
      "metadata": {
        "id": "EonnoAIPYQe9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}