Model_and_train_csv.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_M_hIl_-C6P8",
        "outputId": "f95a24c6-d785-4311-cf5f-ed1ec1323e85"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#before running this please change the RUNTIME to GPU (Runtime -> Change runtime type -> set harware accelarotor as GPU)\n",
        "#Mount our google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mneMgm9j7ack"
      },
      "source": [
        "Note : Use the drive link for the processed dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tnFgwZBYCyYR"
      },
      "outputs": [],
      "source": [
        "#Note : only needed when you have to download the processed data to the environment\n",
        "#download and unzip the data from google drive Colab environment\n",
        "from google_drive_downloader import GoogleDriveDownloader as gdd\n",
        "#use only file id of the link\n",
        "#Note: Below link is just an example, Not an actual link. Actual Links are in ReadMe file\n",
        "#https://drive.google.com/file/d/1ubvKLzBDe5i1acxgGUK6ObeNBYCKUS07/view?usp=sharing\n",
        "url = '1ubvKLzBDe5i1acxgGUK6ObeNBYCKUS07'\n",
        "gdd.download_file_from_google_drive(file_id = url,dest_path='./data.zip',unzip=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7hlPaQS4e5VI",
        "outputId": "2b9ae13e-750b-48e6-c068-a93396d1a518"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting face_recognition\n",
            "  Downloading face_recognition-1.3.0-py2.py3-none-any.whl (15 kB)\n",
            "Collecting face-recognition-models>=0.3.0 (from face_recognition)\n",
            "  Downloading face_recognition_models-0.3.0.tar.gz (100.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100.1/100.1 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: Click>=6.0 in /usr/local/lib/python3.10/dist-packages (from face_recognition) (8.1.3)\n",
            "Requirement already satisfied: dlib>=19.7 in /usr/local/lib/python3.10/dist-packages (from face_recognition) (19.24.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from face_recognition) (1.22.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from face_recognition) (8.4.0)\n",
            "Building wheels for collected packages: face-recognition-models\n",
            "  Building wheel for face-recognition-models (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for face-recognition-models: filename=face_recognition_models-0.3.0-py2.py3-none-any.whl size=100566173 sha256=4ed2f12cf3aba21f4d34bab5b2bb68977c57d356cfcaa90a070c2d28c2829a91\n",
            "  Stored in directory: /root/.cache/pip/wheels/7a/eb/cf/e9eced74122b679557f597bb7c8e4c739cfcac526db1fd523d\n",
            "Successfully built face-recognition-models\n",
            "Installing collected packages: face-recognition-models, face_recognition\n",
            "Successfully installed face-recognition-models-0.3.0 face_recognition-1.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip3 install face_recognition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZ22Sj8d0JoT",
        "outputId": "bb23b98e-4d69-4377-884b-2386c0759563"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total no of videos : 699\n",
            "['/content/drive/My Drive/FF_REAL_Face_only_data/679_665.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id10_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id10_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id10_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id10_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id10_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id10_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id2_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id2_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id2_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id2_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id2_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id2_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id2_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id2_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id2_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id3_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id2_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id3_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id3_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id3_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id3_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id3_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id3_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id3_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id3_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id4_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id4_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id4_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id4_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id4_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id4_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id4_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id4_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id4_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id4_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id6_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id6_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id6_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id6_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id6_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id6_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id6_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id6_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id6_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id7_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id7_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id7_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id7_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id7_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id7_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id7_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id7_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id7_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id7_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id8_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id10_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id12_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0015.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id3_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id6_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id10_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id8_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id11_0000.mp4', '/content/drive/My 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Drive/FF_REAL_Face_only_data/id12_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id12_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0010.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0011.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0013.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0012.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id13_0014.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0011.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0010.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id16_0012.mp4', '/content/drive/My 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'/content/drive/My Drive/FF_REAL_Face_only_data/id8_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id8_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id9_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id9_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id9_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id9_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id9_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id9_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id9_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id9_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id9_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id9_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id2_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_id16_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id2_0002.mp4', '/content/drive/My 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'/content/drive/My Drive/FF_REAL_Face_only_data/id2_id9_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id2_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id3_0000.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id3_0001.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_id16_0006.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id3_0002.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_id16_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id3_0004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id3_0003.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id3_0005.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_id16_0008.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id0_id16_0009.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id3_0007.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/id1_id3_0006.mp4', '/content/drive/My 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'/content/drive/My Drive/FF_REAL_Face_only_data/032_944.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/944_032.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/948_965.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/965_948.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/816_649.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/649_816.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/665_679.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/982_004.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/004_982.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/246_258.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/635_642.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/258_246.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/642_635.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/568_628.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/628_568.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/615_687.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/687_615.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/960_999.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/361_448.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/999_960.mp4', '/content/drive/My Drive/FF_REAL_Face_only_data/448_361.mp4']\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "#THis code is to check if the video is corrupted or not..\n",
        "#If the video is corrupted delete the video.\n",
        "import glob\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data.dataset import Dataset\n",
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import face_recognition\n",
        "#Check if the file is corrupted or not\n",
        "def validate_video(vid_path,train_transforms):\n",
        "      transform = train_transforms\n",
        "      count = 20\n",
        "      video_path = vid_path\n",
        "      frames = []\n",
        "      a = int(100/count)\n",
        "      first_frame = np.random.randint(0,a)\n",
        "      temp_video = video_path.split('/')[-1]\n",
        "      for i,frame in enumerate(frame_extract(video_path)):\n",
        "        frames.append(transform(frame))\n",
        "        if(len(frames) == count):\n",
        "          break\n",
        "      frames = torch.stack(frames)\n",
        "      frames = frames[:count]\n",
        "      return frames\n",
        "#extract a from from video\n",
        "def frame_extract(path):\n",
        "  vidObj = cv2.VideoCapture(path)\n",
        "  success = 1\n",
        "  while success:\n",
        "      success, image = vidObj.read()\n",
        "      if success:\n",
        "          yield image\n",
        "\n",
        "im_size = 112\n",
        "mean = [0.485, 0.456, 0.406]\n",
        "std = [0.229, 0.224, 0.225]\n",
        "\n",
        "train_transforms = transforms.Compose([\n",
        "                                        transforms.ToPILImage(),\n",
        "                                        transforms.Resize((im_size,im_size)),\n",
        "                                        transforms.ToTensor(),\n",
        "                                        transforms.Normalize(mean,std)])\n",
        "video_fil =  glob.glob('/content/drive/My Drive/FF_REAL_Face_only_data/*.mp4')\n",
        "# video_fil += glob.glob('/content/drive/My Drive/Celeb_real_face_only/*.mp4')\n",
        "# video_fil += glob.glob('/content/drive/My Drive/DFDC_FAKE_Face_only_data/*.mp4')\n",
        "# video_fil += glob.glob('/content/drive/My Drive/DFDC_REAL_Face_only_data/*.mp4')\n",
        "# video_fil += glob.glob('/content/drive/My Drive/FF_Face_only_data/*.mp4')\n",
        "print(\"Total no of videos :\" , len(video_fil))\n",
        "print(video_fil)\n",
        "count = 0;\n",
        "for i in video_fil:\n",
        "  try:\n",
        "    count+=1\n",
        "    validate_video(i,train_transforms)\n",
        "  except:\n",
        "    print(\"Number of video processed: \" , count ,\" Remaining : \" , (len(video_fil) - count))\n",
        "    print(\"Corrupted video is : \" , i)\n",
        "    continue\n",
        "print((len(video_fil) - count))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CEIygy8uDFXc",
        "outputId": "e0f18100-55e0-4fc1-f144-2db06e43a4de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "frames are  [148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 134, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148]\n",
            "Total no of video:  699\n",
            "Average frame per video: 147.9799713876967\n"
          ]
        }
      ],
      "source": [
        "#to load preprocessod video to memory\n",
        "import json\n",
        "import glob\n",
        "import numpy as np\n",
        "import cv2\n",
        "import copy\n",
        "import random\n",
        "video_files =  glob.glob('/content/drive/My Drive/FF_REAL_Face_only_data/*.mp4')\n",
        "# video_files += glob.glob('/content/drive/My Drive/Celeb_real_face_only/*.mp4')\n",
        "# video_files += glob.glob('/content/drive/My Drive/DFDC_FAKE_Face_only_data/*.mp4')\n",
        "# video_files += glob.glob('/content/drive/My Drive/DFDC_REAL_Face_only_data/*.mp4')\n",
        "# video_files += glob.glob('/content/drive/My Drive/FF_Face_only_data/*.mp4')\n",
        "random.shuffle(video_files)\n",
        "random.shuffle(video_files)\n",
        "frame_count = []\n",
        "for video_file in video_files:\n",
        "  cap = cv2.VideoCapture(video_file)\n",
        "  if(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))<100):\n",
        "    video_files.remove(video_file)\n",
        "    continue\n",
        "  frame_count.append(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)))\n",
        "print(\"frames are \" , frame_count)\n",
        "print(\"Total no of video: \" , len(frame_count))\n",
        "print('Average frame per video:',np.mean(frame_count))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OqGXNkqhDKZU"
      },
      "outputs": [],
      "source": [
        "# load the video name and labels from csv\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data.dataset import Dataset\n",
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import face_recognition\n",
        "class video_dataset(Dataset):\n",
        "    def __init__(self,video_names,labels,sequence_length = 60,transform = None):\n",
        "        self.video_names = video_names\n",
        "        self.labels = labels\n",
        "        self.transform = transform\n",
        "        self.count = sequence_length\n",
        "    def __len__(self):\n",
        "        return len(self.video_names)\n",
        "    def __getitem__(self,idx):\n",
        "        video_path = self.video_names[idx]\n",
        "        frames = []\n",
        "        a = int(100/self.count)\n",
        "        first_frame = np.random.randint(0,a)\n",
        "        temp_video = video_path.split('/')[-1]\n",
        "        #print(temp_video)\n",
        "        label = self.labels.iloc[(labels.loc[labels[\"file\"] == temp_video].index.values[0]),1]\n",
        "        if(label == 'FAKE'):\n",
        "          label = 0\n",
        "        if(label == 'REAL'):\n",
        "          label = 1\n",
        "        for i,frame in enumerate(self.frame_extract(video_path)):\n",
        "          frames.append(self.transform(frame))\n",
        "          if(len(frames) == self.count):\n",
        "            break\n",
        "        frames = torch.stack(frames)\n",
        "        frames = frames[:self.count]\n",
        "        #print(\"length:\" , len(frames), \"label\",label)\n",
        "        return frames,label\n",
        "    def frame_extract(self,path):\n",
        "      vidObj = cv2.VideoCapture(path)\n",
        "      success = 1\n",
        "      while success:\n",
        "          success, image = vidObj.read()\n",
        "          if success:\n",
        "              yield image\n",
        "#plot the image\n",
        "def im_plot(tensor):\n",
        "    image = tensor.cpu().numpy().transpose(1,2,0)\n",
        "    b,g,r = cv2.split(image)\n",
        "    image = cv2.merge((r,g,b))\n",
        "    image = image*[0.22803, 0.22145, 0.216989] +  [0.43216, 0.394666, 0.37645]\n",
        "    image = image*255.0\n",
        "    plt.imshow(image.astype(int))\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1leMozhXa5LF"
      },
      "outputs": [],
      "source": [
        "#count the number of fake and real videos\n",
        "def number_of_real_and_fake_videos(data_list):\n",
        "  header_list = [\"file\",\"label\"]\n",
        "  lab = pd.read_csv('/content/drive/My Drive/Gobal_metadata.csv',names=header_list)\n",
        "  fake = 0\n",
        "  real = 0\n",
        "  for i in data_list:\n",
        "    temp_video = i.split('/')[-1]\n",
        "    label = lab.iloc[(labels.loc[labels[\"file\"] == temp_video].index.values[0]),1]\n",
        "    if(label == 'FAKE'):\n",
        "      fake+=1\n",
        "    if(label == 'REAL'):\n",
        "      real+=1\n",
        "  return real,fake"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "sWMZn0YHDO2b",
        "outputId": "a09562f9-492f-4a19-87ff-562f4befab15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train :  559\n",
            "test :  140\n",
            "TRAIN:  Real: 280  Fake: 279\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TEST:  Real: 69  Fake: 71\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# load the labels and video in data loader\n",
        "import random\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "header_list = [\"file\",\"label\"]\n",
        "labels = pd.read_csv('/content/drive/My Drive/Gobal_metadata.csv',names=header_list)\n",
        "#print(labels)\n",
        "train_videos = video_files[:int(0.8*len(video_files))]\n",
        "valid_videos = video_files[int(0.8*len(video_files)):]\n",
        "print(\"train : \" , len(train_videos))\n",
        "print(\"test : \" , len(valid_videos))\n",
        "# train_videos,valid_videos = train_test_split(data,test_size = 0.2)\n",
        "# print(train_videos)\n",
        "\n",
        "print(\"TRAIN: \", \"Real:\",number_of_real_and_fake_videos(train_videos)[0],\" Fake:\",number_of_real_and_fake_videos(train_videos)[1])\n",
        "print(\"TEST: \", \"Real:\",number_of_real_and_fake_videos(valid_videos)[0],\" Fake:\",number_of_real_and_fake_videos(valid_videos)[1])\n",
        "\n",
        "\n",
        "im_size = 112\n",
        "mean = [0.485, 0.456, 0.406]\n",
        "std = [0.229, 0.224, 0.225]\n",
        "\n",
        "train_transforms = transforms.Compose([\n",
        "                                        transforms.ToPILImage(),\n",
        "                                        transforms.Resize((im_size,im_size)),\n",
        "                                        transforms.ToTensor(),\n",
        "                                        transforms.Normalize(mean,std)])\n",
        "\n",
        "test_transforms = transforms.Compose([\n",
        "                                        transforms.ToPILImage(),\n",
        "                                        transforms.Resize((im_size,im_size)),\n",
        "                                        transforms.ToTensor(),\n",
        "                                        transforms.Normalize(mean,std)])\n",
        "train_data = video_dataset(train_videos,labels,sequence_length = 10,transform = train_transforms)\n",
        "#print(train_data)\n",
        "val_data = video_dataset(valid_videos,labels,sequence_length = 10,transform = train_transforms)\n",
        "train_loader = DataLoader(train_data,batch_size = 4,shuffle = True,num_workers = 4)\n",
        "valid_loader = DataLoader(val_data,batch_size = 4,shuffle = True,num_workers = 4)\n",
        "image,label = train_data[0]\n",
        "im_plot(image[0,:,:,:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UtOXSqyBDRnD"
      },
      "outputs": [],
      "source": [
        "#Model with feature visualization\n",
        "from torch import nn\n",
        "from torchvision import models\n",
        "class Model(nn.Module):\n",
        "    def __init__(self, num_classes,latent_dim= 2048, lstm_layers=1 , hidden_dim = 2048, bidirectional = False):\n",
        "        super(Model, self).__init__()\n",
        "        model = models.resnext50_32x4d(pretrained = True) #Residual Network CNN\n",
        "        self.model = nn.Sequential(*list(model.children())[:-2])\n",
        "        self.lstm = nn.LSTM(latent_dim,hidden_dim, lstm_layers,  bidirectional)\n",
        "        self.relu = nn.LeakyReLU()\n",
        "        self.dp = nn.Dropout(0.4)\n",
        "        self.linear1 = nn.Linear(2048,num_classes)\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d(1)\n",
        "    def forward(self, x):\n",
        "        batch_size,seq_length, c, h, w = x.shape\n",
        "        x = x.view(batch_size * seq_length, c, h, w)\n",
        "        fmap = self.model(x)\n",
        "        x = self.avgpool(fmap)\n",
        "        x = x.view(batch_size,seq_length,2048)\n",
        "        x_lstm,_ = self.lstm(x,None)\n",
        "        return fmap,self.dp(self.linear1(torch.mean(x_lstm,dim = 1)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WYNhn10tDV90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b0d9534-bd8e-4774-a2f1-f7b717a0c535"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNeXt50_32X4D_Weights.IMAGENET1K_V1`. You can also use `weights=ResNeXt50_32X4D_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth\" to /root/.cache/torch/hub/checkpoints/resnext50_32x4d-7cdf4587.pth\n",
            "100%|██████████| 95.8M/95.8M [00:00<00:00, 201MB/s]\n"
          ]
        }
      ],
      "source": [
        "model = Model(2).cuda()\n",
        "a,b = model(torch.from_numpy(np.empty((1,20,3,112,112))).type(torch.cuda.FloatTensor))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FKheLUWBDaNN"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch.autograd import Variable\n",
        "import time\n",
        "import os\n",
        "import sys\n",
        "import os\n",
        "def train_epoch(epoch, num_epochs, data_loader, model, criterion, optimizer):\n",
        "    model.train()\n",
        "    losses = AverageMeter()\n",
        "    accuracies = AverageMeter()\n",
        "    t = []\n",
        "    for i, (inputs, targets) in enumerate(data_loader):\n",
        "        if torch.cuda.is_available():\n",
        "            targets = targets.type(torch.cuda.LongTensor)\n",
        "            inputs = inputs.cuda()\n",
        "        _,outputs = model(inputs)\n",
        "        loss  = criterion(outputs,targets.type(torch.cuda.LongTensor))\n",
        "        acc = calculate_accuracy(outputs, targets.type(torch.cuda.LongTensor))\n",
        "        losses.update(loss.item(), inputs.size(0))\n",
        "        accuracies.update(acc, inputs.size(0))\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        sys.stdout.write(\n",
        "                \"\\r[Epoch %d/%d] [Batch %d / %d] [Loss: %f, Acc: %.2f%%]\"\n",
        "                % (\n",
        "                    epoch,\n",
        "                    num_epochs,\n",
        "                    i,\n",
        "                    len(data_loader),\n",
        "                    losses.avg,\n",
        "                    accuracies.avg))\n",
        "    torch.save(model.state_dict(),'/content/checkpoint.pt')\n",
        "    return losses.avg,accuracies.avg\n",
        "def test(epoch,model, data_loader ,criterion):\n",
        "    print('Testing')\n",
        "    model.eval()\n",
        "    losses = AverageMeter()\n",
        "    accuracies = AverageMeter()\n",
        "    pred = []\n",
        "    true = []\n",
        "    count = 0\n",
        "    with torch.no_grad():\n",
        "        for i, (inputs, targets) in enumerate(data_loader):\n",
        "            if torch.cuda.is_available():\n",
        "                targets = targets.cuda().type(torch.cuda.FloatTensor)\n",
        "                inputs = inputs.cuda()\n",
        "            _,outputs = model(inputs)\n",
        "            loss = torch.mean(criterion(outputs, targets.type(torch.cuda.LongTensor)))\n",
        "            acc = calculate_accuracy(outputs,targets.type(torch.cuda.LongTensor))\n",
        "            _,p = torch.max(outputs,1)\n",
        "            true += (targets.type(torch.cuda.LongTensor)).detach().cpu().numpy().reshape(len(targets)).tolist()\n",
        "            pred += p.detach().cpu().numpy().reshape(len(p)).tolist()\n",
        "            losses.update(loss.item(), inputs.size(0))\n",
        "            accuracies.update(acc, inputs.size(0))\n",
        "            sys.stdout.write(\n",
        "                    \"\\r[Batch %d / %d]  [Loss: %f, Acc: %.2f%%]\"\n",
        "                    % (\n",
        "                        i,\n",
        "                        len(data_loader),\n",
        "                        losses.avg,\n",
        "                        accuracies.avg\n",
        "                        )\n",
        "                    )\n",
        "        print('\\nAccuracy {}'.format(accuracies.avg))\n",
        "    return true,pred,losses.avg,accuracies.avg\n",
        "class AverageMeter(object):\n",
        "    \"\"\"Computes and stores the average and current value\"\"\"\n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "    def reset(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        self.val = val\n",
        "        self.sum += val * n\n",
        "        self.count += n\n",
        "        self.avg = self.sum / self.count\n",
        "def calculate_accuracy(outputs, targets):\n",
        "    batch_size = targets.size(0)\n",
        "\n",
        "    _, pred = outputs.topk(1, 1, True)\n",
        "    pred = pred.t()\n",
        "    correct = pred.eq(targets.view(1, -1))\n",
        "    n_correct_elems = correct.float().sum().item()\n",
        "    return 100* n_correct_elems / batch_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b8WneBZNfysN"
      },
      "outputs": [],
      "source": [
        "import seaborn as sn\n",
        "#Output confusion matrix\n",
        "def print_confusion_matrix(y_true, y_pred):\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    print('True positive = ', cm[0][0])\n",
        "    print('False positive = ', cm[0][1])\n",
        "    print('False negative = ', cm[1][0])\n",
        "    print('True negative = ', cm[1][1])\n",
        "    print('\\n')\n",
        "    df_cm = pd.DataFrame(cm, range(2), range(2))\n",
        "    sn.set(font_scale=1.4) # for label size\n",
        "    sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}) # font size\n",
        "    plt.ylabel('Actual label', size = 20)\n",
        "    plt.xlabel('Predicted label', size = 20)\n",
        "    plt.xticks(np.arange(2), ['Fake', 'Real'], size = 16)\n",
        "    plt.yticks(np.arange(2), ['Fake', 'Real'], size = 16)\n",
        "    plt.ylim([2, 0])\n",
        "    plt.show()\n",
        "    calculated_acc = (cm[0][0]+cm[1][1])/(cm[0][0]+cm[0][1]+cm[1][0]+ cm[1][1])\n",
        "    print(\"Calculated Accuracy\",calculated_acc*100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fExJLjt2AtV9"
      },
      "outputs": [],
      "source": [
        "def plot_loss(train_loss_avg,test_loss_avg,num_epochs):\n",
        "  loss_train = train_loss_avg\n",
        "  loss_val = test_loss_avg\n",
        "  print(num_epochs)\n",
        "  epochs = range(1,num_epochs+1)\n",
        "  plt.plot(epochs, loss_train, 'g', label='Training loss')\n",
        "  plt.plot(epochs, loss_val, 'b', label='validation loss')\n",
        "  plt.title('Training and Validation loss')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "def plot_accuracy(train_accuracy,test_accuracy,num_epochs):\n",
        "  loss_train = train_accuracy\n",
        "  loss_val = test_accuracy\n",
        "  epochs = range(1,num_epochs+1)\n",
        "  plt.plot(epochs, loss_train, 'g', label='Training accuracy')\n",
        "  plt.plot(epochs, loss_val, 'b', label='validation accuracy')\n",
        "  plt.title('Training and Validation accuracy')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rUe1XrYnDdit",
        "outputId": "7d9edd22-5e4c-4fa8-cbd4-dfd4091416e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 1/20] [Batch 139 / 140] [Loss: 0.679071, Acc: 57.25%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.652161, Acc: 57.86%]\n",
            "Accuracy 57.857142857142854\n",
            "[Epoch 2/20] [Batch 139 / 140] [Loss: 0.606321, Acc: 72.09%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.553164, Acc: 71.43%]\n",
            "Accuracy 71.42857142857143\n",
            "[Epoch 3/20] [Batch 139 / 140] [Loss: 0.500121, Acc: 79.96%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.442121, Acc: 82.86%]\n",
            "Accuracy 82.85714285714286\n",
            "[Epoch 4/20] [Batch 139 / 140] [Loss: 0.401847, Acc: 86.76%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.412812, Acc: 82.14%]\n",
            "Accuracy 82.14285714285714\n",
            "[Epoch 5/20] [Batch 139 / 140] [Loss: 0.401256, Acc: 86.23%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.405715, Acc: 82.14%]\n",
            "Accuracy 82.14285714285714\n",
            "[Epoch 6/20] [Batch 139 / 140] [Loss: 0.366082, Acc: 88.55%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.345375, Acc: 85.00%]\n",
            "Accuracy 85.0\n",
            "[Epoch 7/20] [Batch 139 / 140] [Loss: 0.350183, Acc: 88.55%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.378880, Acc: 82.14%]\n",
            "Accuracy 82.14285714285714\n",
            "[Epoch 8/20] [Batch 139 / 140] [Loss: 0.305200, Acc: 90.34%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.354422, Acc: 85.00%]\n",
            "Accuracy 85.0\n",
            "[Epoch 9/20] [Batch 139 / 140] [Loss: 0.343190, Acc: 88.37%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.418100, Acc: 82.86%]\n",
            "Accuracy 82.85714285714286\n",
            "[Epoch 10/20] [Batch 139 / 140] [Loss: 0.374379, Acc: 86.94%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.369188, Acc: 84.29%]\n",
            "Accuracy 84.28571428571429\n",
            "[Epoch 11/20] [Batch 139 / 140] [Loss: 0.359351, Acc: 89.62%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.325336, Acc: 85.71%]\n",
            "Accuracy 85.71428571428571\n",
            "[Epoch 12/20] [Batch 139 / 140] [Loss: 0.301580, Acc: 93.02%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.335515, Acc: 86.43%]\n",
            "Accuracy 86.42857142857143\n",
            "[Epoch 13/20] [Batch 139 / 140] [Loss: 0.289701, Acc: 93.92%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.336259, Acc: 85.00%]\n",
            "Accuracy 85.0\n",
            "[Epoch 14/20] [Batch 139 / 140] [Loss: 0.310341, Acc: 92.84%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.366984, Acc: 83.57%]\n",
            "Accuracy 83.57142857142857\n",
            "[Epoch 15/20] [Batch 139 / 140] [Loss: 0.247050, Acc: 94.81%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.477772, Acc: 82.86%]\n",
            "Accuracy 82.85714285714286\n",
            "[Epoch 16/20] [Batch 139 / 140] [Loss: 0.347551, Acc: 88.73%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.329540, Acc: 85.71%]\n",
            "Accuracy 85.71428571428571\n",
            "[Epoch 17/20] [Batch 139 / 140] [Loss: 0.292354, Acc: 91.06%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.363889, Acc: 85.71%]\n",
            "Accuracy 85.71428571428571\n",
            "[Epoch 18/20] [Batch 139 / 140] [Loss: 0.318826, Acc: 90.16%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.356828, Acc: 83.57%]\n",
            "Accuracy 83.57142857142857\n",
            "[Epoch 19/20] [Batch 139 / 140] [Loss: 0.276010, Acc: 92.84%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.387230, Acc: 82.86%]\n",
            "Accuracy 82.85714285714286\n",
            "[Epoch 20/20] [Batch 139 / 140] [Loss: 0.284321, Acc: 93.02%]Testing\n",
            "[Batch 34 / 35]  [Loss: 0.324146, Acc: 85.71%]\n",
            "Accuracy 85.71428571428571\n",
            "20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[63  8]\n",
            " [12 57]]\n",
            "True positive =  63\n",
            "False positive =  8\n",
            "False negative =  12\n",
            "True negative =  57\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Calculated Accuracy 85.71428571428571\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "#learning rate\n",
        "lr = 1e-5#0.001\n",
        "#number of epochs\n",
        "num_epochs = 20\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr= lr,weight_decay = 1e-5)\n",
        "\n",
        "#class_weights = torch.from_numpy(np.asarray([1,15])).type(torch.FloatTensor).cuda()\n",
        "#criterion = nn.CrossEntropyLoss(weight = class_weights).cuda()\n",
        "criterion = nn.CrossEntropyLoss().cuda()\n",
        "train_loss_avg =[]\n",
        "train_accuracy = []\n",
        "test_loss_avg = []\n",
        "test_accuracy = []\n",
        "for epoch in range(1,num_epochs+1):\n",
        "    l, acc = train_epoch(epoch,num_epochs,train_loader,model,criterion,optimizer)\n",
        "    train_loss_avg.append(l)\n",
        "    train_accuracy.append(acc)\n",
        "    true,pred,tl,t_acc = test(epoch,model,valid_loader,criterion)\n",
        "    test_loss_avg.append(tl)\n",
        "    test_accuracy.append(t_acc)\n",
        "plot_loss(train_loss_avg,test_loss_avg,len(train_loss_avg))\n",
        "plot_accuracy(train_accuracy,test_accuracy,len(train_accuracy))\n",
        "print(confusion_matrix(true,pred))\n",
        "print_confusion_matrix(true,pred)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.9.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}