MLE_deepfake.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FFakasfheZZ",
        "outputId": "0c76663f-8f6d-4e7c-995b-fce303c1b015"
      },
      "outputs": [],
      "source": [
        "!pip install torchvision\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "enpNKE8ycoBC"
      },
      "outputs": [],
      "source": [
        "from torchvision import datasets, transforms\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),  # Resize images to 224x224\n",
        "    transforms.ToTensor(),          # Convert images to tensor\n",
        "])\n",
        "\n",
        "# Create datasets\n",
        "train_dataset = datasets.ImageFolder(root='/content/data/train', transform=transform)\n",
        "test_dataset = datasets.ImageFolder(root='/content/data/test', transform=transform)\n",
        "\n",
        "# Create data loaders\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYP0UDYChU4X",
        "outputId": "1f8c542c-8159-404d-de32-90d664431e55"
      },
      "outputs": [],
      "source": [
        "\n",
        "def check_data_loader_dim(loader):\n",
        "    for images, labels in loader:\n",
        "        print('Image batch dimensions:', images.shape)\n",
        "        print('Image label dimensions:', labels.shape)\n",
        "        break\n",
        "\n",
        "check_data_loader_dim(train_loader)\n",
        "check_data_loader_dim(test_loader)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SszFWno3jxUK"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision.datasets import ImageFolder\n",
        "from tqdm.auto import tqdm\n",
        "import gdown\n",
        "from sklearn.metrics import confusion_matrix, precision_recall_fscore_support, accuracy_score, roc_auc_score\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IQHTRFQBimmL"
      },
      "outputs": [],
      "source": [
        "class InceptionBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(InceptionBlock, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(in_channels, out_channels[0], kernel_size=1)\n",
        "        self.bn1 = nn.BatchNorm2d(out_channels[0])\n",
        "        self.conv3 = nn.Conv2d(in_channels, out_channels[1], kernel_size=3, padding=1)\n",
        "        self.bn3 = nn.BatchNorm2d(out_channels[1])\n",
        "        self.conv5 = nn.Conv2d(in_channels, out_channels[2], kernel_size=5, padding=2)\n",
        "        self.bn5 = nn.BatchNorm2d(out_channels[2])\n",
        "        self.conv_dil = nn.Conv2d(in_channels, out_channels[3], kernel_size=3, padding=2, dilation=2)\n",
        "        self.bn_dil = nn.BatchNorm2d(out_channels[3])\n",
        "\n",
        "    def forward(self, x):\n",
        "        conv1 = F.relu(self.bn1(self.conv1(x)))\n",
        "        conv3 = F.relu(self.bn3(self.conv3(x)))\n",
        "        conv5 = F.relu(self.bn5(self.conv5(x)))\n",
        "        conv_dil = F.relu(self.bn_dil(self.conv_dil(x)))\n",
        "        return torch.cat([conv1, conv3, conv5, conv_dil], dim=1)\n",
        "\n",
        "\n",
        "class DeeperMesoInception4(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(DeeperMesoInception4, self).__init__()\n",
        "\n",
        "        # Inception layer 1\n",
        "        self.inception_1 = InceptionBlock(3, [1, 4, 4, 2])\n",
        "\n",
        "        # Inception layer 2\n",
        "        self.inception_2 = InceptionBlock(11, [2, 4, 4, 2])\n",
        "\n",
        "        self.conv1 = nn.Conv2d(12, 16, kernel_size=5, padding=2)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.bn = nn.BatchNorm2d(16)\n",
        "        self.max_pool1 = nn.MaxPool2d(kernel_size=2)\n",
        "\n",
        "        self.conv3 = nn.Conv2d(16, 32, kernel_size=3, padding=1)\n",
        "        self.conv4 = nn.Conv2d(32, 32, kernel_size=3, padding=1)\n",
        "        self.max_pool3 = nn.MaxPool2d(kernel_size=2)\n",
        "\n",
        "        self.conv5 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
        "        self.conv6 = nn.Conv2d(64, 64, kernel_size=3, padding=1)\n",
        "        self.max_pool4 = nn.MaxPool2d(kernel_size=2)\n",
        "\n",
        "        self.flatten = nn.Flatten()\n",
        "\n",
        "\n",
        "        self.fc1 = nn.Linear(64 * 28 * 28, 16)  # Adjusted input size\n",
        "        self.fc2 = nn.Linear(16, 1)\n",
        "        self.dropout = nn.Dropout(0.5)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.inception_1(x)\n",
        "        x = self.inception_2(x)\n",
        "\n",
        "        x = self.conv1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.bn(x)\n",
        "        x = self.max_pool1(x)\n",
        "\n",
        "        x = self.conv3(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.conv4(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.max_pool3(x)\n",
        "\n",
        "        x = self.conv5(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.conv6(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.max_pool4(x)\n",
        "\n",
        "        x = self.flatten(x)\n",
        "\n",
        "        x = self.dropout(x)\n",
        "        x = self.relu(self.fc1(x))\n",
        "        x = self.dropout(x)\n",
        "        x = self.fc2(x)\n",
        "\n",
        "        return x\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mHlS_ifCipxi"
      },
      "outputs": [],
      "source": [
        "model = DeeperMesoInception4()\n",
        "criterion = nn.BCEWithLogitsLoss()  \n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gpK_Mm4Fj7gn"
      },
      "outputs": [],
      "source": [
        "def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=25, device='cpu'):\n",
        "    model.to(device)\n",
        "    train_losses, val_losses = [], []\n",
        "    train_accuracies, val_accuracies = [], []\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        model.train()\n",
        "        running_loss = 0.0\n",
        "        running_corrects = 0\n",
        "\n",
        "        for inputs, labels in tqdm(train_loader, desc=f\"Epoch {epoch + 1}/{num_epochs} - Training\", unit=\"batch\"):\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs)\n",
        "\n",
        "            labels = labels.unsqueeze(1).type(torch.float)  \n",
        "\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            running_loss += loss.item() * inputs.size(0)\n",
        "            preds = (outputs.sigmoid() > 0.5).float()  \n",
        "            running_corrects += torch.sum(preds == labels)\n",
        "\n",
        "        epoch_train_loss = running_loss / len(train_loader.dataset)\n",
        "        epoch_train_acc = running_corrects.double() / len(train_loader.dataset)\n",
        "        train_losses.append(epoch_train_loss)\n",
        "        train_accuracies.append(epoch_train_acc)\n",
        "\n",
        "        model.eval()\n",
        "        running_loss = 0.0\n",
        "        running_corrects = 0\n",
        "\n",
        "        with torch.no_grad():\n",
        "            for inputs, labels in tqdm(val_loader, desc=f\"Epoch {epoch + 1}/{num_epochs} - Validation\", unit=\"batch\"):\n",
        "                inputs, labels = inputs.to(device), labels.to(device)\n",
        "                labels = labels.unsqueeze(1).type(torch.float)  \n",
        "\n",
        "                outputs = model(inputs)\n",
        "                loss = criterion(outputs, labels)\n",
        "\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                preds = (outputs.sigmoid() > 0.5).float()\n",
        "                running_corrects += torch.sum(preds == labels)\n",
        "\n",
        "        epoch_val_loss = running_loss / len(val_loader.dataset)\n",
        "        epoch_val_acc = running_corrects.double() / len(val_loader.dataset)\n",
        "        val_losses.append(epoch_val_loss)\n",
        "        val_accuracies.append(epoch_val_acc)\n",
        "\n",
        "        print(f'Epoch {epoch + 1}/{num_epochs}')\n",
        "        print(f'Train Loss: {epoch_train_loss:.4f}, Train Acc: {epoch_train_acc:.4f}')\n",
        "        print(f'Validation Loss: {epoch_val_loss:.4f}, Val Acc: {epoch_val_acc:.4f}')\n",
        "\n",
        "    return model, (train_losses, train_accuracies, val_losses, val_accuracies)\n"
      ]
    },
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        "id": "8qFZI7ocj_Py",
        "outputId": "ba785d21-3504-4e07-eb96-a61e13b572df"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch.utils.data.dataset import random_split\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)  # Move the model to the correct device\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)  \n",
        "\n",
        "train_size = int(0.8 * len(train_dataset))  # 80% of the dataset for training\n",
        "val_size = len(train_dataset) - train_size  # 20% for validation\n",
        "\n",
        "train_dataset, val_dataset = random_split(train_dataset, [train_size, val_size])\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n",
        "\n",
        "trained_model, metrics = train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=15, device=device)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GmGhsGnnErt",
        "outputId": "16313e60-0226-4bf7-ad84-5b6b082fda94"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix\n",
        "from tqdm import tqdm\n",
        "\n",
        "def evaluate_model(model, data_loader, device):\n",
        "    model.eval()\n",
        "    predictions, true_labels = [], []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in tqdm(data_loader, desc=\"Evaluating\", unit=\"batch\"):\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(inputs)\n",
        "            preds = torch.sigmoid(outputs).round()  # Assuming binary classification\n",
        "\n",
        "            predictions.extend(preds.cpu().numpy())\n",
        "            true_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "    accuracy = accuracy_score(true_labels, predictions)\n",
        "    f1 = f1_score(true_labels, predictions)\n",
        "    precision = precision_score(true_labels, predictions)\n",
        "    recall = recall_score(true_labels, predictions)\n",
        "    conf_matrix = confusion_matrix(true_labels, predictions)\n",
        "\n",
        "    return accuracy, f1, precision, recall, conf_matrix\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "accuracy, f1, precision, recall, conf_matrix = evaluate_model(trained_model, val_loader, device)\n",
        "\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "print(f\"F1 Score: {f1:.4f}\")\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n"
      ]
    }
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