diff --git "a/script/deepfake_vit.ipynb" "b/script/deepfake_vit.ipynb" new file mode 100644--- /dev/null +++ "b/script/deepfake_vit.ipynb" @@ -0,0 +1,10660 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "JmYRMwEOYkbU" + }, + "source": [ + "# `Deepfake Classification - ViT`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "c6rbUun0tdC5", + "outputId": "3069b396-fba6-479a-8145-0fd6ecb77353", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting evaluate\n", + " Downloading evaluate-0.4.3-py3-none-any.whl.metadata (9.2 kB)\n", + "Collecting datasets\n", + " Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n", + "Requirement already satisfied: accelerate in /usr/local/lib/python3.11/dist-packages (1.2.1)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from evaluate) 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nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, fsspec, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, nvidia-cusolver-cu12, datasets, evaluate\n", + " Attempting uninstall: nvidia-nvjitlink-cu12\n", + " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n", + " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n", + " Attempting uninstall: nvidia-curand-cu12\n", + " Found existing installation: nvidia-curand-cu12 10.3.6.82\n", + " Uninstalling nvidia-curand-cu12-10.3.6.82:\n", + " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n", + " Attempting uninstall: nvidia-cufft-cu12\n", + " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n", + " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n", + " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", + " Attempting uninstall: nvidia-cuda-runtime-cu12\n", + " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", + " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cuda-cupti-cu12\n", + " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", + " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", + " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", + " Attempting uninstall: nvidia-cublas-cu12\n", + " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", + " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", + " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", + " Attempting uninstall: fsspec\n", + " Found existing installation: fsspec 2024.10.0\n", + " Uninstalling fsspec-2024.10.0:\n", + " Successfully uninstalled fsspec-2024.10.0\n", + " Attempting uninstall: nvidia-cusparse-cu12\n", + " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", + " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", + " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", + " Attempting uninstall: nvidia-cudnn-cu12\n", + " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", + " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", + " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", + " Attempting uninstall: nvidia-cusolver-cu12\n", + " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", + " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", + " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 evaluate-0.4.3 fsspec-2024.9.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 xxhash-3.5.0\n", + "Collecting git+https://github.com/huggingface/transformers.git\n", + " Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-d1puyqe6\n", + " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-d1puyqe6\n", + " Resolved https://github.com/huggingface/transformers.git to commit 62db3e6ed67a74cc1ed1436acd9973915c0a4475\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers==4.49.0.dev0) (3.17.0)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.11/dist-packages (from transformers==4.49.0.dev0) (0.27.1)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers==4.49.0.dev0) (1.26.4)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from transformers==4.49.0.dev0) (24.2)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from transformers==4.49.0.dev0) (6.0.2)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers==4.49.0.dev0) (2024.11.6)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers==4.49.0.dev0) (2.32.3)\n", + "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers==4.49.0.dev0) (0.21.0)\n", + "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.11/dist-packages (from transformers==4.49.0.dev0) (0.5.2)\n", + "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.11/dist-packages (from transformers==4.49.0.dev0) (4.67.1)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers==4.49.0.dev0) (2024.9.0)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from 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sha256=fda839d34a1ba03a86e33d720ff522b980da93ff6e5b06d29c9853d650555b10\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-l9opoh_v/wheels/32/4b/78/f195c684dd3a9ed21f3b39fe8f85b48df7918581b6437be143\n", + "Successfully built transformers\n", + "Installing collected packages: transformers\n", + " Attempting uninstall: transformers\n", + " Found existing installation: transformers 4.47.1\n", + " Uninstalling transformers-4.47.1:\n", + " Successfully uninstalled transformers-4.47.1\n", + "Successfully installed transformers-4.49.0.dev0\n" + ] + } + ], + "source": [ + "!pip install evaluate datasets accelerate\n", + "!pip install git+https://github.com/huggingface/transformers.git" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "dhLosa2Utm5M" + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import gc\n", + "import numpy as np\n", + "import pandas as pd\n", + "import itertools\n", + "from collections import Counter\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix, classification_report, f1_score\n", + "from imblearn.over_sampling import RandomOverSampler\n", + "import evaluate\n", + "from datasets import Dataset, Image, ClassLabel\n", + "from transformers import (\n", + " TrainingArguments,\n", + " Trainer,\n", + " ViTImageProcessor,\n", + " ViTForImageClassification,\n", + " DefaultDataCollator\n", + ")\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from torchvision.transforms import (\n", + " CenterCrop,\n", + " Compose,\n", + " Normalize,\n", + " RandomRotation,\n", + " RandomResizedCrop,\n", + " RandomHorizontalFlip,\n", + " RandomAdjustSharpness,\n", + " Resize,\n", + " ToTensor\n", + ")\n", + "from PIL import Image as PILImage\n", + "from PIL import ImageFile\n", + "\n", + "# Enable loading truncated images\n", + "ImageFile.LOAD_TRUNCATED_IMAGES = True" + ] + 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EpochTraining LossValidation LossModel Preparation TimeAccuracy
10.2514000.3288440.0045000.901430
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Classification report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " Realism 0.9683 0.8708 0.9170 28001\n", + " Deepfake 0.8826 0.9715 0.9249 28000\n", + "\n", + " accuracy 0.9212 56001\n", + " macro avg 0.9255 0.9212 0.9210 56001\n", + "weighted avg 0.9255 0.9212 0.9210 56001\n", + "\n" + ] + } + ], + "source": [ + "# Extract the true labels from the model outputs\n", + "y_true = outputs.label_ids\n", + "\n", + "# Predict the labels by selecting the class with the highest probability\n", + "y_pred = outputs.predictions.argmax(1)\n", + "\n", + "# Define a function to plot a confusion matrix\n", + "def plot_confusion_matrix(cm, classes, title='Confusion Matrix', cmap=plt.cm.Blues, figsize=(10, 8)):\n", + " \"\"\"\n", + " This function plots a confusion matrix.\n", + "\n", + " Parameters:\n", + " cm (array-like): Confusion matrix as returned by sklearn.metrics.confusion_matrix.\n", + " classes (list): List of class names, e.g., ['Class 0', 'Class 1'].\n", + " title (str): Title for the plot.\n", + " cmap (matplotlib colormap): Colormap for the plot.\n", + " \"\"\"\n", + " # Create a figure with a specified size\n", + " plt.figure(figsize=figsize)\n", + "\n", + " # Display the confusion matrix as an image with a colormap\n", + " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", + " plt.title(title)\n", + " plt.colorbar()\n", + "\n", + " # Define tick marks and labels for the classes on the axes\n", + " tick_marks = np.arange(len(classes))\n", + " plt.xticks(tick_marks, classes, rotation=90)\n", + " plt.yticks(tick_marks, classes)\n", + "\n", + " fmt = '.0f'\n", + " # Add text annotations to the plot indicating the values in the cells\n", + " thresh = cm.max() / 2.0\n", + " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", + " plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n", + "\n", + " # Label the axes\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label')\n", + "\n", + " # Ensure the plot layout is tight\n", + " plt.tight_layout()\n", + " # Display the plot\n", + " plt.show()\n", + "\n", + "# Calculate accuracy and F1 score\n", + "accuracy = accuracy_score(y_true, y_pred)\n", + "f1 = f1_score(y_true, y_pred, average='macro')\n", + "\n", + "# Display accuracy and F1 score\n", + "print(f\"Accuracy: {accuracy:.4f}\")\n", + "print(f\"F1 Score: {f1:.4f}\")\n", + "\n", + "# Get the confusion matrix if there are a small number of labels\n", + "if len(labels_list) <= 150:\n", + " # Compute the confusion matrix\n", + " cm = confusion_matrix(y_true, y_pred)\n", + "\n", + " # Plot the confusion matrix using the defined function\n", + " plot_confusion_matrix(cm, labels_list, figsize=(8, 6))\n", + "\n", + "# Finally, display classification report\n", + "print()\n", + "print(\"Classification report:\")\n", + "print()\n", + "print(classification_report(y_true, y_pred, target_names=labels_list, digits=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "Qj1F9FLgIedG" + }, + "outputs": [], + "source": [ + "trainer.save_model()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "F-jYKwyWIiue" + }, + "outputs": [], + "source": [ + "# Import the 'pipeline' function from the 'transformers' library.\n", + "from transformers import pipeline\n", + "pipe = pipeline('image-classification', model=model_name, device=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Iy0WsizHIm_m" + }, + "outputs": [], + "source": [ + "image = test_data[1][\"image\"]\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TG7rBUMnIpXl" + }, + "outputs": [], + "source": [ + "pipe(image)" + ] + }, + { + "cell_type": "code", + "execution_count": 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