FaceForensics_XceptionNet_Code.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PtHpzm2dvuYq","outputId":"c6463539-1924-4930-b60b-48f188803b46"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torchvision\n","import torchvision.transforms as transforms\n","import torch.nn as nn\n","import torch.optim as optim\n","from torchvision import datasets\n","from torchvision.io import read_image\n","import glob\n","import cv2\n","import matplotlib.pyplot as plt\n","from torch.utils.data import Dataset, DataLoader\n","from PIL import Image\n","import timm\n","torch.manual_seed(42)\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(device)\n","import torchvision.models as models\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"oMsY8gQydb8U"},"source":["## Dataset extracted from Face Forensics Videos. DataLoader Creation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"luUItpGederJ"},"outputs":[],"source":["class CustomDataset(Dataset):\n","\tdef __init__(self, transform=None):\n","\t\tself.imgs_path = 'C:/Users/tkhuran3/Downloads/FF_Images/'\n","\t\tself.transform = transform\n","\t\tfile_list = glob.glob(self.imgs_path + \"*\")\n","\t\tself.data = []\n","\t\tfor class_path in file_list:\n","\t\t\tclass_name = class_path.split(\"\\\\\")[-1]\n","\t\t\tfor img_path in glob.glob(class_path + \"/*.jpg\"):\n","\t\t\t\tself.data.append([img_path, class_name])\n","\t\tself.class_map = {\"Real_Images\": 0, \"Fake_Images\": 1}\n","\t\n","\tdef __len__(self):\n","\t\treturn len(self.data)\n","\n","\tdef __getitem__(self, idx):\n","\t\timg_path, class_name = self.data[idx]\n","\t\timg = read_image(img_path)\n","\t\tclass_id = self.class_map[class_name]\n","\t\tclass_id = torch.tensor([class_id])\n","\t\tif self.transform:\n","\t\t\t\timg_tensor = self.transform(img)\n","\t\treturn img_tensor, class_id"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"307i1exx5vWk"},"outputs":[],"source":["train_transforms = transforms.Compose([\n","    transforms.ToPILImage(),\n","    transforms.Resize([128, 128]),\n","    transforms.ToTensor(),\n","    ])\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Training and Testing Split"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i_hhUKPTdetg"},"outputs":[],"source":["dataset = CustomDataset(transform=train_transforms)\n","train_size = int(0.8 * len(dataset))\n","val_size = len(dataset) - train_size\n","train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\t"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"93_xKYhpl2PN","outputId":"702e4895-02c8-4d71-e156-fb7cc08b0d98"},"outputs":[],"source":["len(dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"otvC3pkjl2PN","outputId":"a2eb4462-0456-4ada-ae0a-3de91aac2b89"},"outputs":[],"source":["train_data_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)\n","val_data_loader = DataLoader(val_dataset, batch_size=128, shuffle=True)\n","len(train_data_loader)"]},{"cell_type":"markdown","metadata":{"id":"DetHchcb43Gc"},"source":["## XceptionNet Model"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = timm.create_model('xception', pretrained=True, num_classes=1)\n","model = model.to(device)\n","print(model)\n"]},{"cell_type":"markdown","metadata":{"id":"TlETlqPcdGcs"},"source":["## Optimizer and Loss Function\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CJ5KcJeEHB71"},"outputs":[],"source":["optimizer = torch.optim.Adam(model.parameters(),lr = 0.0001)\n","criterion = nn.BCEWithLogitsLoss()"]},{"cell_type":"markdown","metadata":{"id":"t4dTS9b0dKen"},"source":["## Training code"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oyHEEPZMIdc4","outputId":"34266c38-74e8-4076-fdd2-1c0e7ec1fc35"},"outputs":[],"source":["train_losses, train_accs, val_losses, val_accs = [], [], [], []\n","\n","for epoch in range(20):\n","    print(\"=================\")\n","    print(f\"Epoch: {epoch+1}\")\n","\n","    model.train()\n","    avg_loss_train = 0\n","    correct = 0\n","    total = 0\n","  \n","    for i, data in enumerate(train_data_loader, 0):\n","    \n","        inputs,labels = data\n","        inputs = inputs.to(device)\n","        labels = labels.to(device)\n","        outputs = model(inputs.float())\n","        outputs = torch.sigmoid(outputs)\n","    \n","        optimizer.zero_grad()\n","        loss = criterion(outputs, labels.float())\n","        loss.backward()\n","        optimizer.step()\n","        avg_loss_train += loss.item()\n","        predicted = (outputs > 0.5).float() \n","        correct += (predicted == labels).sum().item()\n","        total += labels.size(0)\n","    epoch_loss = avg_loss_train / len(train_data_loader)\n","    epoch_acc = correct / total\n","\n","    train_losses.append(epoch_loss)\n","    train_accs.append(epoch_acc)\n","\n","    print(f\"Train Loss: {epoch_loss} | Train Accuracy: {epoch_acc}\")    \n","\n","    model.eval()\n","    running_loss = 0\n","    correct = 0\n","    total = 0\n","    with torch.no_grad():\n","        for data in val_data_loader:\n","            inputs,labels = data\n","            inputs = inputs.to(device)\n","            labels = labels.to(device)\n","            outputs = model(inputs.float())\n","            outputs = torch.sigmoid(outputs) \n","        \n","            optimizer.zero_grad()\n","            loss = criterion(outputs, labels.float())\n","            running_loss += loss.item()\n","            \n","            predicted = (outputs > 0.5).float() \n","            correct += (predicted == labels).sum().item()\n","            total += labels.size(0)\n","        epoch_loss = running_loss / len(val_data_loader)\n","        epoch_acc = correct / total\n","    val_losses.append(epoch_loss)\n","    val_accs.append(epoch_acc)\n","\n","    print(f\"Val Loss: {epoch_loss} | Val Accuracy: {epoch_acc}\")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["torch.save({\n","            'model': model,\n","            'model_state_dict': model.state_dict(),\n","            'train_losses' : train_losses,\n","            'train_accs' : train_accs,\n","            'val_losses' : val_losses,\n","            'val_accs' : val_accs,\n","            }, 'FFPP.pt')\n","print('model saved')\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def plot_curves(train_losses, train_accuracies, val_losses, val_accuracies):\n","\n","  fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(8, 8))\n","\n","  # Plot the train and test losses\n","  ax1.plot(train_losses, label='Train')\n","  ax1.plot(val_losses, label='Val')\n","  ax1.set_xlabel('Epoch')\n","  ax1.set_ylabel('Loss')\n","  ax1.set_title('Losses vs Epochs')\n","  ax1.legend()\n","\n","  # Plot the train and test accuracies\n","  ax2.plot(train_accuracies, label='Train')\n","  ax2.plot(val_accuracies, label='Val')\n","  ax2.set_xlabel('Epoch')\n","  ax2.set_ylabel('Accuracy')\n","  ax2.set_title('Accuracy vs Epochs')\n","  ax2.legend()\n","\n","  # Show the plot\n","  plt.show()\n","  "]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3.9.13 ('base')","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.10"},"vscode":{"interpreter":{"hash":"703fc444efd57af823539a0730dab3b55a3cc9cc45981e7b1b33eb3f139bc8b4"}}},"nbformat":4,"nbformat_minor":0}