Day #21 DeepFake_Detection_Kaggle_Challenge (2_2)_ipynb.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/skyprince999/100-Days-Of-ML/blob/master/Day_21_DeepFake_Detection_Kaggle_Challenge_(2_2)_ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uDX_CaKGsv8Y"
   },
   "source": [
    "This is the second notebook that was submitted for the DFDC Challenge\n",
    "\n",
    "Public Score: 0.43786\n",
    "\n",
    "Private Score: 0.53723"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-c4TNh__srsP"
   },
   "source": [
    "# Guide to Google Cloud Training, Ensembling & Learning Learning Rates\n",
    "\n",
    "The objectives of this kernel are to first provide a quick overview of the steps I followed to set up virtual machine (VM) instance on Google Cloud Platform, to download the required competition files, to train the models, how I changed the parameters to obtain the public score of <0.45, and also how to leave the instance running upon exiting the terminal.\n",
    "\n",
    "In particular, this will be my **last public kernel (with scores) until the competition ends**. The end of the competition is in about a month, and I figured that some insights on the tweeks to the existing training scripts could help those who have yet to make some sort of breakthrough at this point. \n",
    "\n",
    "\n",
    "Best regards,\n",
    "\n",
    "Wei Hao"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ycj1QRCbsrsS"
   },
   "source": [
    "# Google Cloud Platform\n",
    "\n",
    "Google Cloud Platform (GCP), offered by Google, is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube.[1] Alongside a set of management tools, it provides a series of modular cloud services including computing, data storage, data analytics and machine learning. Registration requires a credit card or bank account details. Google Cloud Platform provides infrastructure as a service, platform as a service, and serverless computing environments.\n",
    "\n",
    "In April 2008, Google announced App Engine, a platform for developing and hosting web applications in Google-managed data centers, which was the first cloud computing service from the company. The service became generally available in November 2011. Since the announcement of App Engine, Google added multiple cloud services to the platform.\n",
    "\n",
    "Google Cloud Platform is a part of Google Cloud, which includes the Google Cloud Platform public cloud infrastructure, as well as G Suite, enterprise versions of Android and Chrome OS, and application programming interfaces (APIs) for machine learning and enterprise mapping services.\n",
    "\n",
    "**Source:** Wikipedia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RJa7cZS_srsV"
   },
   "source": [
    "## Free Credits\n",
    "\n",
    "If you are a first time user, you can get around 300 USD free credits to play around with, which I personally feel is quite decent! To-date, I've only used 100 USD, since Jan 2020. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WiqX4s2gsrsX"
   },
   "source": [
    "## Compute Engine\n",
    "\n",
    "Compute Engine delivers configurable virtual machines running in Google’s data centers with access to high-performance networking infrastructure and block storage. It caters to:\n",
    "- Select the right VM for your needs, whether general purpose, or workload optimized, in predefined or custom machine sizes\n",
    "- Integrate compute with other Google Cloud services such as AI/ML and data analytics\n",
    "- Make reservations to help ensure your applications have the capacity they need as they scale. Save money just for running compute with sustained-use discounts, and achieve greater savings when you use committed-use discounts\n",
    "\n",
    "**Source:** https://cloud.google.com/compute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_kg_hide-input": true,
    "colab": {},
    "colab_type": "code",
    "id": "2LNhm-2CsrsZ"
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nSjPrecisrsl"
   },
   "source": [
    "## Launching and Navigating the Virtual Machine (VM) Instance\n",
    "\n",
    "Once you have created your VM instance with the desired CPU & GPU resources, go to 'VM Instances' and you should see something like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_kg_hide-input": true,
    "colab": {},
    "colab_type": "code",
    "id": "8glMcgOpsrsn",
    "outputId": "c3ed3fbd-f260-4cf2-b465-6a85afada20b"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../input/deepfake-kernel-data/google_cloud_compute_engine_launch_vm.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LjICuEdesrs0"
   },
   "source": [
    "Once you are able to see that, click on the dropdown icon beside SSH and click 'Open in broswer window' (see above). A console like the following should appear:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_kg_hide-input": true,
    "colab": {},
    "colab_type": "code",
    "id": "Bnqb25AXsrs2",
    "outputId": "43163aaa-1022-44ca-ab31-4c3eb9724737"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../input/deepfake-kernel-data/google_cloud_vm.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cYwsBUfQsrtA"
   },
   "source": [
    "You can refer to https://github.com/Kaggle/kaggle-api, where the README describes the CLI and API commands required to download the competition data and files into the VM's directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rsBgmyGqsrtC"
   },
   "source": [
    "# Model Training - Parameter Tuning\n",
    "\n",
    "For model training, I am using GreatGameDota's script found at https://www.kaggle.com/greatgamedota/xception-classifier-w-ffhq-training-lb-537."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "chZtjNgnsrtE"
   },
   "source": [
    "## Epochs, Factor, Learning Rates & Patience (IMPORTANT EXPERIMENTAL NOTES)\n",
    "\n",
    "I've tried various learning rates `lr` (0.001, 0.0015, 0.002, 0.004), `epochs` (10, 12, 20, 42), `patience` (2, 5, 7) and `factor` (0.1, 0.2, 0.5, 0.7). The result from these is the score of 0.50480 for the Xception single model, and 0.43846 for the Resnext + Xception esemble. Further tweeking of these parameters do help increase the public LB score by a bit. Also, yet interestingly, as the score of the single model Xception decreases, the higher scoring esembles have equal weights, i.e. 0.5 for Resnext and 0.5 for Xception. \n",
    "\n",
    "Feel free to tweek these parameters further! Note that the total time for each run of 30 epochs or more on Google Cloud Compute took **more than 20 hours**. So it may be wise to plan your parameters properly based on the insights gathered from past trainings!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q_Blzz_4srtF"
   },
   "source": [
    "## Some Xception (Single Model) Training Outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_kg_hide-input": true,
    "colab": {},
    "colab_type": "code",
    "id": "IUjdmNVesrtH",
    "outputId": "64a076ed-ef96-4685-e6e8-18ae3e807352"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "google_cloud_compute_engine_launch_vm.png\r\n",
      "google_cloud_vm.png\r\n",
      "google_cloud_vm_deepfake_training_screenshot.png\r\n",
      "lr_15e-2_epochs_42_patience_5.png\r\n",
      "lr_2e-3_epochs_10_patience_5.png\r\n",
      "lr_2e-3_epochs_20_patience_5.png\r\n",
      "lr_4e-3_epochs_12_patience_2.png\r\n",
      "lr_4e-3_epochs_30_patience_2.png\r\n",
      "model_50epochs_lr0001_patience5_factor01_batchsize32.pth\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../input/deepfake-kernel-data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9w0lakMKsrtR"
   },
   "source": [
    "### Learning Rate: 0.0015, Epochs: 42, Patience: 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_kg_hide-input": true,
    "colab": {},
    "colab_type": "code",
    "id": "ulc0SBGBsrtT",
    "outputId": "aa8858d6-4ace-4773-f140-0c67582936a4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../input/deepfake-kernel-data/lr_15e-2_epochs_42_patience_5.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "weQvQK5Ssrte"
   },
   "source": [
    "### Learning Rate: 0.002, Epochs: 10, Patience: 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_kg_hide-input": true,
    "colab": {},
    "colab_type": "code",
    "id": "fBzIsU16srtg",
    "outputId": "5e30e76a-efd0-45ef-bc5e-e81a8b739f8f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../input/deepfake-kernel-data/lr_2e-3_epochs_10_patience_5.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-bahB-uZsrts"
   },
   "source": [
    "### Learning Rate: 0.002, Epochs: 20, Patience: 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_kg_hide-input": true,
    "colab": {},
    "colab_type": "code",
    "id": "xnoavMtbsrtu",
    "outputId": "89749304-0973-4cc2-90d7-2f1ebc92aa64"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XuRyPAl4GnjTGvOC9ao+ct3L0LTmng77vBnOrTrSxu7qZjSV12tpXSo3KkEHfGNMD3A28CZQAfzLG7BGR+0RktbPY/UA88IKIFIuI9aFwLbAEWOc8Xiwic73/Z3jOWzn6lvSEaGeuvu9a+u/td4xzdPb0sfnQcZ89j1LqzOfRSKYxZgOwwe3YvS6PVwxw3dPA06OpoLd5K0ffYrMJWWNjqWz0XUt/U2k9mUkxNLd3s3FfHcvzx/vsuZRSZ7awm5HrzRx9S3ay79I2u3r62HzwOJfkpbNkehrvlNRhTL/j6EopNaQwDPrey9G3ZCf7blZuUUUjp7p6uXh6Gsvy0jna3MGemmafPJe//Z+XdrKx5Figq6FUWBl9onqI8WaOviU7OZbjrY5c/ZhIu9fuC7Bpfz2RdmHxWal0dPciAu/sq+OcrCSvPo+/VTe188etlTR39Gh3lVJ+FIYtfe/l6Ft8mbb5Xmk9BZNSiI+OIDU+mrk5Y8+I1nFhWQMA+2rPjG8tSoWKsAv63szRt+SkWEsse3cwt6apndJjLVw84+8T1pbnpfNp1UnqWjq8+lz+VljWCED58VM6m1kpPwqroO/tHH2L9SFS6eWWvpWqefGM9NPHrK6QTft8v1yFLxWWNxAbaafPwIFjrYGujlJhI6yCvrdz9C1p8dFE2W1eb+lvKq0jMymG6ePjTx/Ly0hgQlIMb4dwF8+x5g4qGtq4ap5jCaeSo9rFo5S/hFXQ93aOvsVmE7K8nMHT1dPHRwcbuHhGGiJ/X/5IRFieP54PDx4P2W6RwnJH1841BTnERtrZV9sS4BopFT7CKuj7Ikff4u20ze2HT9Da2cPS6emfO7csP522rl62OAdDQ01hWQPx0RGcMyGR6RkJ7NOWvlJ+E2ZB3/s5+pbs5Fiqvdi9897+eiJswgVnjfvcufOnjCM20s47++r6uTL4FZY3UpCbTITdRn5GAiW1zTrhTCk/Cbug7+0cfUt2chzHW7u8tiDaptI65k9KJiEm8nPnYiLtXDgtlY0hODv3eGsnB+taWTjZ8WGWl5HAibZu6lo6h7hSKeUNYRb0vZ+jb7Hu643B3KMnO9h3tOUzWTvuluelU+1M6QwlW539+QunpACQl5kIQInm6yvlF2EV9H2Ro2/x5gSt9/Y7um1c8/PdLctzfCBsLAmtLp7CsgbiouzMcs4ozs9wBP19R0Prw0upUBU2Qd9XOfqWHC+29DeV1pORGENeRsKAZdITY5idnRRys3MLyxuZPymZSLvjrZcUF8mEpBidmauUn4RN0PdVjr4lNT6aqAjbqFv63b19fHjgOEunfzZVsz/L8tLZUdlEQ2to9Ic3tXWx72gLC3JTPnM8LzNRW/pK+UnYBH1f5ehbbDYhe+zo0zZ3HGmipbOHpYN07VhW5I/HGHi3NDRm5/69P/+zGUl5GQkcrGulq+dz2ycrpbwsbIK+L3P0LdkpcaPu3tlUWofdJlxwVuqQZc+ekMj4xGje2RcaXTyF5Y1ER9iYk/PZFULzMhPp6TMcqtflGJTytTAK+r7L0bd4Y4LWptJ65k9MJin286ma7kSEZXnjeX//8ZBoJReWN3DuxLFER3x2+el859iFTtJSyvfCKuj7Kkffkp0cS8OpLk519ozo+rrmDvbWNnvUtWNZnpdOa2fP6a6TYNXc0c3emubT+fmuJqeOIcpu0+UYlPKDMAr6vsvRt1jjBdVNI2vtbzq9qqbnQf+Cs1KJjrCxMci7eIoqGukzf8/PdxVhtzFtfDwlOpirlM+FTdD3ZY6+ZbQTtN7bX09aQjQznROWPBEbZeeCs4J/dm5hWSORdmHexOR+z+dlJGraplJ+EBZB39c5+pa/B/3ht/R7evv4YH+9R6ma7pblpXOksS2oB0ILyxuZkz12wO0k8zMTqGvpDJn0U6VCVVgEfV/n6FvS4qOJHmGufnFlE80dPcPq2rEsz3fMzn07SGfnnursYVf1yX67dix5OjNXKb/wKOiLyEoRKRWRgyJyTz/nvyMie0Vkp4hsFJFJLufWisgB589ab1beU77O0beIiDODZ/jdO5tK67EJXHTW8IN+ZlIsMzMTeSdIg/72wyfo7TP9DuJa8jIdGTy6Bo9SvjVk0BcRO/AAcDkwE7hBRGa6FdsBFBhjZgMvAr90XpsC/AhYCCwAfiQi/Xfq+pA/cvQt2clxVDYOv6W/aX8d8yYmkxQ3dKpmf5bnp1N0uJETp7pGdL0vFZY3YLcJ8ycN/J8+NT6a1Phobekr5WOetPQXAAeNMWXGmC7gOWCNawFjzLvGGKt5uwXIdj6+DHjLGNNojDkBvAWs9E7VPeePHH3LSFr69S2d7K5uHlHXjmV5/nj6zN/31Q0mhWWNzMpKYkz04Omy+Zm6oYpSvuZJ0M8CKl1+r3IeG8htwBvDuVZE7hCRIhEpqq/3ftCqOtFGarxvc/Qt2clxnGjrpnUYufrv97MB+nDNzkoiNT6ajUG2sUp7Vy+fVjUN2p9vyc9MZP+xVnp6g3+imVKhypOg318qSb+5gSJyE1AA3D+ca40xDxljCowxBWlpI2/tDqTqRDtZPu7Pt1hdSNXDGMzdtL+e1PioYaVqurPZhGV5aWwqraM7iILmjiMn6O41LJw8dNDPy0igq6ePioZTfqiZUuHJk6BfBeS4/J4N1LgXEpEVwA+A1caYzuFc62tVJ3yfrmnJSbHW1fesi6e3z/DBgXqWTE/DZhteqqa7ZXnjaenooajixKju402F5Y3YBApyPQn61oYq2q+vlK94EvS3AdNEZLKIRAHXA+tdC4jIucDvcQR81/6FN4FLRSTZOYB7qfOY3/T1GefELP8E/eHm6hdXNtHU1j2qrh3LRdNSibLbgmqN/cLyBmZOSCSxn20f3U1NH0OETbRfXykfGjLoG2N6gLtxBOsS4E/GmD0icp+IrHYWux+IB14QkWIRWe+8thH4CY4Pjm3Afc5jflPf2klXb5/P0zUt48ZEERNpo7LRs5b+e6V12ASWTBt6Vc2hjImOYNHUcUGzYXpnTy87jjQNmqrpKjrCztS0eF2DRykf8mhk0xizAdjgduxel8crBrn2UeDRkVZwtPyZrglWrn6cxy399/bXMzdnLGPjorzy/Mvz0vnR+j2U1bcyJS3eK/ccqU8rT9LZ0+dRf74lLzMhqLqnlDrTnPEzcq3gm+OnoA/OtM2moVv6Da2d7Kw+ydLpo+/asVh75wZDa7+wrAERWDCcoJ+RSHVTOyfbun1YM6XCV9gE/ayx/uneAc/X1X//QD3GDG9VzaHkpMQxY3xCUGyYXljeyIzxCcP6FmPNzNV+faV8IwyCviNHPzaq/4W+fCEnOY6mtm5aOgZvrW4qrWfcmChmZSUNWm64luWns62ikZPtgWstd/f2sf3wiWF17QDk6xo8SvlUGAR9/+XoWzxZV7+3z/D+fu+karpbkZ9Oj/P+gbKr+iTt3b2f2w93KOMToxkbF6ktfaWGoaO71+OyYRH0/TWIazmdtjnIGjy7qk9yoq3bq107lrk5yaSMiQpo6mZhmSNJazj9+eAYCM/PSNRcfaWG4ekthz0ue0YHfX/n6Fus56scZILWptI6ROCiad4P+nabcPGMNDbtrw/YkgaF5Q2clR5Panz0sK/Ny0yg9GgLfX3BuymMUsGit8/w+OYKj8uf0UHf3zn6lpQxUcRG2gcdzN1UWs/s7LGkjPFOqqa75XnjaWrrZkdlk0/uP5ie3j6KKobfn2/Jz0ikvbuXIx7OdVAqnL2199iw9vAIuqDf48XWnb9z9C1DravfeKqLT6uauHi691v5liXTU4mwCW8HoItnb20zrZ09w+7Pt2gGj1Kee/Sj8mHFuKAL+qVHW9h86LhX7hWIHH3LYGmbH/ggVdNdQkwkC6ekBGRjFas/f6Qt/WnpCdhE1+BRaii7q0+ytbyRdYtzPb4m6IJ+pF24/Ykith8e/WoNgcjRt+SkDDwrd1NpPclxkczOHuvTOizLG8+BulaONPi3m6SwvIHccXGMT4wZ0fWxUXZyU8doS1+pITz6UTljouxce17O0IWdgi7oT0mNJz0hmnWPbmN39clR3SsQOfqW7ORYTrZ30+yWq9/nkqpp93KqprsVzr1zN+7zXxdPb59ha3mjx+vtDCQ/I1Fz9ZUaRF1LB699WsM1BTkeLWhoCbqgH2EXnvn6IhJjI/naHwopHcX/+IHI0bdYg8fuaZu7a07ScKrLp107lknjxjA1bYxfZ+eWHm2huaPHo01TBpOXkcDhhjZODWMzGqU8UVzZxP/dUDKs3PZg9PSWI/T0GdYOo2sHgjDoA2SNjeXZry8k0m7jxkcKKatvHdF9ApGjb/n7Esuf7VrZVOqYMOWLVM3+rMgfT2F5w5Czg72lsLwBYMSDuJa8TJ2Zq7zvj1uPcO2DH/PQ+2WsL/b71h5e09HdyzNbDrM8L53JqWOGdW1QBn1wtFKf/fpCjDHc+Eihx0sVWwKVo2853dJ369ffVFrH7OykEeWvj8SyvHS6ew0fHvDO4PhQCssayU6OJWvs6F73vAzN4FHe09nTyz1/3sn/eWkXC6ekMC09nsc3V2BMaM4Fee3TGhpOdXHrBZOHfW3QBn2As9ITeOq2hZzq7OHGRwo5erLD42sDlaNvSY6LJC7qs7n6TW1dFFf6NlXT3fxJySTFRvK2H7p4jDFsrWgc9izc/mQnxxIfHaFr66tRqz3ZzrW/38Jz2yq56+KpPH7LAm65YDJ7a5spOhx6y3gbY3j0owryMhI4f+rwv1EHddAHmDkhkSduXUBDayc3PrKF462dQ19E4HL0LSJCTnLcZ7p3PjhwnD4DS72wS5anIuw2x+zc0jp6fTzD9UBdK42nulg0ykFccLx+eRkJ2tJXo7KlrIErfvMhB4+18OBN8/j+yjzsNuHL504gMSZiWDNZg8WWskZKapu55YJcRIafDBL0QR/g3InJPLruPKqb2vnaH7bS1NY15DWBzNG3uOfqbyqtZ2xcJHNzfJuq6W5ZXjoNpxzfMnypsMzqzx99Sx8gPzORfbUtIfsVXAWOMYY/fFjOjY8UkhQXyat3X8DKczJPn4+LiuDaghze3H10WD0IweDRj8pJGRPFmrlZI7o+JII+OAYGH765gEN1rax9bNuQA5OBzNG3ZCfHnl5/p6/P8N7+ei6a5vtUTXcXT0/HbhPe8XHq5pbyRjISY5iY4p3XPC8zgZbOnkFXK1XKXXtXL99+vpifvL6X5XnpvPrNCzgrPeFz5W4+P5deY3i20PPFygLtcMMp3i45xo0LJxITObJU9JAJ+uDIeHngxnnsqT7JbY8X0dY1cDpfIHP0LdnJcbR09HCyvZu9tc0cb+1kqR/78y1JcZEUTEr2aeqmMc78/CkpI/rK2Z88a2197ddXHjrS0MaV/+8j1n9aw/cum8GDN80nYYAc9onj4lg2I51ntx6hsyc00jcf31xBhE24adGkEd8jpII+wBdmjue/rptL0eFG/uGp7QPm2gYyR9/imra5qdQRcAMR9AGW56ez72jLgOsBjVb58VPUt3SOelKWqxmawaOGYVNpHVf8zwfUnuzg8VsW8M1Lzhpyr4q1i3M53trFhl21fqrlyLV0dPNCURVXzJ4w4tnuEIJBH+BLcybwi6/M5oMDx7n72U/o7mf54EDm6Ftc0zY3ldZzTlYiaQn+SdV0tzx/PADv+mjv3MJy53o7XurPB4iPjmBiShwlmquvBtHXZ/jtOwe45fFtZCXH8drdF3rcuLrwrFSmpI7h8c3B38XzQlEVrZ09I0rTdBWSQR/gmoIcfrLmbN4uqePbzxd/JjMl0Dn6lpwUx/PvqWnmkyMnuNiLG6AP15TUMeSOi/NZ6mZhWQOp8dFMGeZEkaHkZSSwr1Zb+qp/LR3d/MPT2/nV3/azes4EXvrGYiaO8/wbvs0m3Hz+JD6tbPJ5osNoWGvmn5ebzKzs0W2v6lHQF5GVIlIqIgdF5J5+zi8RkU9EpEdErnY790sR2SMiJSLyG/FWhy/wtfNz+ZdVefxlZy3ff3Hn6U03Ap2jb0mKjSQ+OoIXiyrp8/GqmkMREZbnj+fjQw1eX9rAGENheSMLJ3uvP9+Sl5lI+fFTIT9lXnnfwboW1jzwEe8/nJFTAAAc8ElEQVTsq+NHX5rJr6+bO6IxvK/Mz2ZMlJ0ngzh9c2PJMY40tnHLKFv54EHQFxE78ABwOTATuEFEZroVOwKsA551u3YxcAEwGzgHOA9YOupau7hjyVS+vWIaf/6kinvX78YYE/AcfYu1rn7NyQ4SYyL8nqrpbnl+Ol29ffzPOwe9et/KxnZqT3Z4tWvHkp+RQJ+BA8dGthSHOjP9dXcta377Ec3t3Tx7+0JuuWDyiBscCTGRXD0/m9d31no8D8jfHv2onKyxsVw6c/yo7+VJS38BcNAYU2aM6QKeA9a4FjDGVBhjdgLunesGiAGigGggEvB63uC3lk/jH5ZO4ektR/j3N/ZR2Rj4HH2L9cFz0bQ0IuyB7U07f8o4bliQw4PvHeI3Gw947b5brPV2vDiIa7HW4CnRLh6Fo5vjl3/dx51Pf8K08Qm89o8XjnqdJ3D0GnT19vHHwiNeqKV37ak5yZayRtYunuSVGBLhQZksoNLl9ypgoSc3N8Z8LCLvArWAAL81xpQMu5ZDEBHuWZlHR1cvD71fRq6zTy+QOfoWq4tpaQC7diwiws++PIvOnj7+8639REXYuHPp1FHfd2t5I8lxkUxLj/dCLT9rYkocsZF2SjSDJ+wZY/iHp4p4u6SOGxZM5MerZxId4Z2U7LPS47loWipPFx7mzounEhngBpqrxz6qIC7KznUFE71yP0/+sv6+M3k0RVJEzgLygWwcHx7LRGRJP+XuEJEiESmqr6/35Nb9PRc/+tLZXDM/m4qGwOfoW6aNjyfKbvPrejuDsdmE+6+ew+o5E/j5G/v4w4flo75nYXkDCyanDJkeNxJ2mzA9I0Fz9RUfHjzO2yV1fH/lDP79qlleC/iWtefncqy5k7/t8f8WowOpb+lkfXENV8/PJinO8zXzB+NJS78KcN2WJRvwdE3SK4EtxphWABF5A1gEvO9ayBjzEPAQQEFBwYjn3Ntsws+/Mhu7TQiWmfvXFuSwZFoa6aPIq/U2u034z2vn0N3bx09e30tUhI2vjXCyR01TO5WN7axbPPoBpoHkZyTw5p6jGGO8PlCsQscjH5STlhDN7RdO8cn9L8lLJycllic2V/DF2ZlDX+AHzxYeoau3b1jbIQ7Fk5b+NmCaiEwWkSjgemC9h/c/AiwVkQgRicQxiOv17h1Xdmfg/8XVs335NB6LtNvI8dKyBN4UYbfx39efy4r8dH74ym6e3zayvszT6+d7YWXNgeRnJnKirZu6luAcZFO+t/9YC+/tr2ft+ZOIivBN14vdJty8KJetFY3srQl8d2JnTy9PbTnMsrx0pqR5r+t0yFfPGNMD3A28iSNg/8kYs0dE7hOR1QAicp6IVAHXAL8XkT3Oy18EDgG7gE+BT40xr3mt9mpUoiJsPHDjPJZOT+Oel3bx0idVw75HYVkjCTER5DsHXH3BWltfB3PD16MflhMTaeOrC0e+/IAnri3IISbSxhNBkL75+qeObKJbLsj16n09+sg0xmwwxkw3xkw1xvzMeexeY8x65+NtxphsY8wYY8w4Y8zZzuO9xph/MMbkG2NmGmO+49Xaq1GLjrDz+6/NZ/HUcXz3hU957dPh7SZUWN7IgtwUny4id3oNHp2ZG5YaWjt5aUc1X5mXTcqYKJ8+V1JcJFeem8UrxdWcODX0ar6+4lgzv5xp6fFceFaqV+8dPEPUKmBiIu08fHMBBZNS+Pbzxfx191GPrqtr7qD8+Cmf5Oe7SoqLZEJSjM7MDVNPbzlCV08ft17ou3EjV2sX59LZ08efiiqHLuwjW8sb2VPTzK0Xjnz+wUA06CvAsb74o7ecx5zsJP7xj5+wsWToDIbT6+34ID/fXV5morb0w1BHdy9PbalgWV46U73Yrz2YvIxEFk5O4akth32+8dBAHv2onGTntw5v06CvTouPjuDxWxeQn5nIN57+hPf2D54+W1jewJgoO2dP8F1/viUvI4GDda109Xx+cT3Vv20VjRysC+0PyvXFNRxv7eJ2P7XyLesW51J1ot2jxo+3VTa28be9x/jqKNbMH4wGffUZiTGRPHnrAs5Kj+eOJ4vYfHDgDdULyxqZn5vil5nGeZmJ9PQZDtXrcgye6Oju5dbHtvGt54pDducxYwyPfFhGfmbiiPaCHY0vzBxPZlIMT3xc4dfnBXhicwV2Eb62KNcn99egrz5nbFwUT9++kEnj4rjtiSK2OrtxXDW0dnKgrtWnqZqu8nVt/WF5Z18dLZ097KlpDurVIwfz4cHj7D/Wym0+6NceSoTdxk2LJvHRwQYOHPPft6XWzh6e31bJqlmZZCT5Zm6PBn3Vr5QxUTxz+yImjI3hlse28smRE585b30QLPLxIK5lcuoYouw2SnRmrkde2VFNanw0Y6LsPL0l+NaT8YQ1GetLcwIzUer683KIirDx5Mf+W2v/xaJKWjp7fDporUFfDSgtIZpnv76ItIRo1j66lV1VJ0+fKyxvJCbSxqws/6wcGmG3MW18vObqe+BkWzebSutZM3cCXz43i9d31gQ0/XAkXCdjeXu5BU+Ni4/mS7Mn8OdPqmgeYk9ub+jrMzy2uYJ5E8f6dEVeDfpqUOMTY3j264tIio3kpj8Unp6puKWsgfmTkn02O7I/eRmaweOJN3bX0tXbx5q5E7hp0SQ6e/p4cfvwJ94Fkr8mYw1l3eJc2rp6ebHI96/fO/vqONzQ5vPUVA36akgTxsbyx68vYkyUnZv+UMi2ikZKj7X4JVXTVX5mAvUtnUG75nmweKW4mimpY5iVlUR+ZiIFk5J5pvDw6U2Ggp01GesqP0zGGsqs7CTmTRzLkx9X+Pz1e/SjciYkxbDy7AyfPo8GfeWRnJQ4nv36IiJswlcf3oIxsMBPg7gWa6mHUm3tD6j2ZDuF5Y2smZt1evDzpkWTqGho48NBMrGCyenJWF7YJcob1i7OpaKhjfcOjGwFYE+U1Daz+VADNy/O9Xk2nAZ95bHc1DGnu3qiI2x+3wlM1+AZ2vriGoyBNXMnnD52+awMUsZE8fSW4N/823Uy1lk+2J9hJC4/J5O0hGifbqf4+EcVxEbauf68nKELj5IGfTUsZ6XH8/JdF/DM7Qt9MnFkMOPio0lLiNZ+/UG8UlzD3Jyx5LpsUB8dYefaghzeLjlGTVN7AGs3tEBNxhpMVISNry6YyKb99VQcP+X1+ze0dvJycTVXzctibJzvu7M06Kthy0mJoyDXv107lryMBM3VH8D+Yy2U1DbzZZdWvuXGhRMxwHNbgzd905qMlZeR4PfJWEO5ceFE7CI+Sd98ttDRneXt1TQHokFfhZT8zET2H2ulp1eXY3D3yo5q7DbhijmfD/o5KXFcPD2NP26rpDtIXztrMtbtF00Jus1y0hNjWDUrkxeKKjnV2eO1++6taebJLYdZOj2Ns9ITvHbfwWjQVyElLyOBrp4+Khq8/zU7lPX1GV4truHCs1JJjY/ut8zXzp9EfUtwbQfoKtCTsYaydvEkWjp7eGlH9ajvtf3wCW59fBurfvMB7V29/K/l07xQQ89o0FchxVpbX2fmftb2Iyeobmrny+d+vpVvWTo9nayxsUE5oBsMk7GGMm9iMudkJfLk5ooRrWdkjOGDA/Vc/9DHfOV3m9lx5AT/9IXpfPTPy5g/KdkHNe6fBn0VUqamjyHCJprB4+bV4mpiIm18YebAOd52m3Djool8XNYQdKtvPvphOdERgZ+MNRgRYe35uRyoa+XjQw0eX9fXZ/jr7qOseeAjvvaHrZQfP8UPr5jJR/cs4x+XT/Pahuee0qCvQkp0hJ2pafGaweOiu7ePv+ys5QszM4iPjhi07LUFOUTaJajW4zm9M9b8wE/GGsqX5kwgZUwUj3uQvtnT28fLO6q47Nfvc+fT22lq6+bfr5rF+9+/hNsunExc1OD/rXwlMM+q1CjkZSawrZ+VP8PV+/vrOdHW3W/WjrvU+GhWzcrkz9ur+P7KGQELPK6CbTLWYGKcufQPvneIqhNtZCfHfa5MR3cvL26v4vfvH6KysZ0Z4xP47+vn8sVZmX5Zhnwoga+BUsOUl5FIzckOTrb5fhGsUPBKcQ3JcZEsmZ7mUfmbFjkGJNcXD28/ZF+wJmNdMiMtaCZjDeXGRY4uqKfcxkZOdfbw8PtlLPnlu/zrK7tJGRPNwzcX8Ma3LmLN3KygCPigLX0VgvIz/762/sIpwZXP7W+tnT28tfcoV8/PJtLDoFIwKZm8jASe2nKY687LCWh65OnJWBdNCVgdhitrbCyXzszg+W2V/O8V0+no7uXxzRU8vrmCprZuFk8dx39dN5fFU8cFXeopaNBXIchag2ff0ZawD/p/23OUju4+vjzX871URYQbF03ih6/spriyiXMn+i9zxJXrZKzFQTYZayhrF+fy1z1HueOp7WyvaORUVy8r8sdz1yVTmReg19NTwfF9Q6lhSE+IJjkuUmfm4ujayU6OHXbK35XnZjEmyv65Lgp/CubJWENZNCWF/MxEPjxQz4qZ4/nrty/ikbUFQR/wwcOgLyIrRaRURA6KyD39nF8iIp+ISI+IXO12bqKI/E1ESkRkr4jkeqfqKlyJCHkZiWGfq1/f0smHBxybpQw3aMZHR3DlvCxe31kbsA1WHvmgnNT44J2MNRgR4anbFvDBPy/jv68/9/T8kVAwZNAXETvwAHA5MBO4QURmuhU7AqwDnu3nFk8C9xtj8oEFQN1oKqwUODJ4So+2hMwa8b7w+s4a+gzD6tpxddOiSXQFaIOVUJiMNZTU+GiyxsYGuhrD5klLfwFw0BhTZozpAp4D1rgWMMZUGGN2Ap9Z1MP54RBhjHnLWa7VGNPmnaqrcJafkUh7dy9HGsP37fRqcQ35mYlMGz+yNVvyMhI5LzeZpwOwwYo1GcvKhFH+40nQzwIqXX6vch7zxHSgSUReEpEdInK/85uDUqOS55LBE44qjp+iuLLJo9z8wdy0aBKH/bzBSihNxjoTeRL0++ss9LRZEAFcBHwXOA+YgqMb6LNPIHKHiBSJSFF9ve92p1FnjmnpCdgkfNfgebW4BhFYPcqgv/KcDMaNifLrgG4oTcY6E3kS9KsA1+1csgFPZ3VUATucXUM9wCvAPPdCxpiHjDEFxpiCtDTPJpio8BYbZSc3dUxYrsFjjOHV4moWTk4hM2l0fcrREXauPS+HjX7aYCUUJ2OdaTwJ+tuAaSIyWUSigOuB9R7efxuQLCJWJF8G7B1+NZX6vPyMxLBcg2dX9UnKjp8a8QCuu68ucGyw8kc/bLASipOxzjRDBn1nC/1u4E2gBPiTMWaPiNwnIqsBROQ8EakCrgF+LyJ7nNf24uja2Sgiu3B0FT3smz9FhZu8jASONLbR6sVNLULBKztqiLLbuHyWd1Idc1LiuGRGOs9tq6Srx3cbrITyZKwziUd5+saYDcaY6caYqcaYnzmP3WuMWe98vM0Yk22MGWOMGWeMOdvl2reMMbONMbOMMeucGUBKjZo1M7c0jFr7vX2G13bWcEleGkmx3luS92uLnBus7D3qtXu6syZj3Xbh5JCbjHUm0Rm5KmSFYwbP5kPHqW/p9FrXjmXJ9DSyk327wYo1GWu0g89qdDToq5CVNTaWhOgI9oVRBs8rO2pIiI7gkrx0r97XbhNuXDiJLWWNPtlg5UyYjHWm0KCvQpaIkJeZEDYt/Y7uXt7cc5SV52QQE+n9wHltQTZRdpvXN1jZU3OSbz1XrJOxgoQGfRXS8jIS2VfbMqI9S0PNxpI6Wjt7+PK53u3asYyLj2bVrAz+vL2Ktq7RD453dPdy/5v7WP3bj6hv6eSBr87TyVhBQIO+Cml5mQm0dPZw+xNFPPZROfuONp+x6/G8UlxNekI0i3y4nLS1wcqro9xgZVtFI6t+8wEPvHuIK8/N4u3vLGHFzPFeqqUaDV1PX4W0Vedksrv6JB8dbGDjPsdafuPGRLFo6jgWTx3H+VPGMTl1TMhnizS1dbGptI615+dit/nub5lvbbDy8WGuH8EGK62dPfzyr/t48uPDZCfH8uStCzze0Uv5hwZ9FdKSx0Tx71fNBqCysY2PyxrYcqiBjw4d5y87awHISIxxfAA4f/rb1zTYbdh1lO5e47OuHYuIcNOiSfzrK7vZUdk0rPXh3y2t4wcv7aK2uYNbLsjlu5fOYMwQG7Ur/9P/IuqMkZMSR05KHNcW5GCMofz4KTYfauDjsgY27a/npR3VAExMifvMh0B6QkyAaz60V4qrmZo2hrMn+H7d9i+fm8XP39jH01sOexT0G0918ZPX9/Lyjmqmpcfz4p2Lh72pi/IfDfrqjCQiTEmLZ0paPDctmkRfn2F/XQubDzo+BP6yq5bntjkWjz0rPZ7FU8exZFoaF05L9UlmzGhUN7WztbyRf/rCdL90U8VHR3DluVk8X1TJD784k+QBBl+NMby+s5Yfr99Dc0c331o+jbsumaopmUFOg74KCzabY7etvIxEbr1wMr19hj01Jx3fBA418EJRFU9+fJj46AiW56dz+TmZXDwjLSg+ANY7B1XXeHlC1mBuWjSJp7Yc5oXtldyxZOrnzteebOeHr+zm7ZI65mQn8YurF4bU7lHhTIO+Ckt2mzA7eyyzs8dy59KpdPX08XFZAxt21vLm3qO8WlxDXJSdZXnprJrl+ACIiwrM/y6vFldz7sSxTBznv7GIGRkJLMhN4ZnCI9x+4RRszsHjvj7DH7cd4ecb9tHd18e/fjGfWy6Y7NPBZeVdGvSVAqIibCydnsbS6Wn8tPccCssa2bC7ljd3H+X1nbXERNq4ZIbjA2BZXrrfBij3HW1m39EW/m312UMX9rIbF03kW88V88HB4yydnkb58VPc8+edFJY3snjqOH5+1Wy/fhAp79Cgr5SbSLuNC6elcuG0VO5bfTZbKxp5Y9dR3tjt+Il2fkCsmpXJ8vx0EmK8t/CZu1eLa7DbhC/O9v/m4SvPySA1PoonNlewr7aZ/3xrP1ERNn75ldlcU5Ad8mmw4UqDvlKDiLDbWDw1lcVTU/nx6rPZfvgEG3bV8sbuWv629xhRdhtLpqdy+TmZrJg53qsrX/b1GdYX13DRtFRS46O9dl9PRUfYubYgh/+36RDv7KvjsrPH85M155CeGPzZTmpgGvSV8pDdJiyYnMKCySnce8VMdlSeYMOuo7yxq5a3S+qItAsXnJXKqnMyufTs8YyNG92SA0WHT1Dd1M73Lpvhpb9g+NYuzqWktplrC3K8tn6/CiwJtjVLCgoKTFFRUaCroZTHjDF8WnWSDbtq2bCrlqoT7UTYhPOnjuNy5wfASFrq//LyLl7+pJqif12hk5zUkERkuzGmYMhyGvSV8h5jDLurm9mwu5Y3dtVS0dCGTWDh5HGsmpXBZedkeDQZrKunjwX/922WTk/jv68/1w81V6HO06CvzQelvEhEmJWdxKzsJL5/2QxKalt4Y3ctf9lVyw9f3cO96/dwXm4Kq87JYOU5mWQk9f8B8N7+epraur2+WYpS2tJXyg+MMRyoa3UMAu86Sukxx0Yl8yclc/k5GVw+K5OssbGny3/z2U/4+FADhf+ynEi7LoarhqYtfaWCiIgwfXwC08cn8O0V0zlY18pfd9eyYddRfvqXEn76lxLm5Ixl1TkZXDQtjbf3HuPaghwN+MrrtKWvVIBVHD/lnANQy86qk6eP//kb5zN/UkoAa6ZCibb0lQoRualj+MbFU/nGxVOpbGzjr7uP0tTeNaxljZXylAZ9pYJITkocX18yJdDVUGcwjzoMRWSliJSKyEERuaef80tE5BMR6RGRq/s5nygi1SLyW29UWiml1MgMGfRFxA48AFwOzARuEJGZbsWOAOuAZwe4zU+A90ZeTaWUUt7gSUt/AXDQGFNmjOkCngPWuBYwxlQYY3YCfe4Xi8h8YDzwNy/UVyml1Ch4EvSzgEqX36ucx4YkIjbgP4DvDVHuDhEpEpGi+vp6T26tlFJqBDwJ+v2tn+ppnuddwAZjTOVghYwxDxljCowxBWlpaR7eWiml1HB5kr1TBeS4/J4N1Hh4//OBi0TkLiAeiBKRVmPM5waDlVJK+Z4nQX8bME1EJgPVwPXAVz25uTHmRuuxiKwDCjTgK6VU4AzZvWOM6QHuBt4ESoA/GWP2iMh9IrIaQETOE5Eq4Brg9yKyx5eVVkopNTJBtwyDiLQApYGuxzClAscDXYlh0Pr6ltbXt7S+/ZtkjBlyUDQYZ+SWerJ+RDARkaJQqrPW17e0vr6l9R0dXcJPKaXCiAZ9pZQKI8EY9B8KdAVGINTqrPX1La2vb2l9RyHoBnKVUkr5TjC29JVSSvlIwIK+B8s1R4vI887zhSKS6/9anq5Ljoi8KyIlIrJHRL7VT5mLReSkiBQ7f+4NRF1d6lMhIrucdfncVmTi8Bvn67tTROYFop7Ousxwed2KRaRZRL7tVibgr6+IPCoidSKy2+VYioi8JSIHnP/2u/OJiKx1ljkgImsDWN/7RWSf87/5yyIydoBrB33/+LG+P3Yuy279d181wLWDxhM/1vd5l7pWiEjxANf6/fU9zRjj9x/ADhwCpgBRwKfATLcydwEPOh9fDzwfiLo6nz8TmOd8nADs76e+FwOvB6qO/dS5Akgd5Pwq4A0caystAgoDXWeX98ZRHDnHQfX6AkuAecBul2O/BO5xPr4H+EU/16UAZc5/k52PkwNU30uBCOfjX/RXX0/eP36s74+B73rwnhk0nvirvm7n/wO4N1heX+snUC39IZdrdv7+hPPxi8ByEelv8TefM8bUGmM+cT5uwTEz2aOVRoPYGuBJ47AFGCsimYGuFLAcOGSMORzoirgzxrwPNLoddn2fPgF8uZ9LLwPeMsY0GmNOAG8BK31WUaf+6muM+ZtxzLIH2IJjLa2gMMDr6wlP4onXDVZfZ6y6Fvijr+sxXIEK+p4s13y6jPNNehIY55faDcLZzXQuUNjP6fNF5FMReUNEzvZrxT7PAH8Tke0ickc/50e8ZLaPXc/A/6ME0+trGW+MqQVH4wBI76dMsL7Wt+L4ttefod4//nS3szvq0QG6z4Lx9b0IOGaMOTDA+YC9voEK+p4s1zyaJZ19QkTigT8D3zbGNLud/gRHl8Qc4H+AV/xdPzcXGGPm4djx7JsissTtfDC+vlHAauCFfk4H2+s7HMH4Wv8A6AGeGaDIUO8ff/kdMBWYC9Ti6DJxF3SvL3ADg7fyA/b6Biroe7Jc8+kyIhIBJDGyr35eISKROAL+M8aYl9zPG2OajTGtzscbgEgRSfVzNV3rU+P8tw54GcdXYFejWTLbVy4HPjHGHHM/EWyvr4tjVreY89+6fsoE1WvtHEi+ArjRODuY3Xnw/vELY8wxY0yvMaYPeHiAegTb6xsBXAU8P1CZQL6+gQr6p5drdrburgfWu5VZD1hZDlcD7wz0BvU1Z//cH4ASY8x/DlAmwxpzEJEFOF7bBv/V8jN1GSMiCdZjHIN3u92KrQdudmbxLAJOWt0UATRg6yiYXl83ru/TtcCr/ZR5E7hURJKd3ROXOo/5nYisBP4ZWG2MaRugjCfvH79wG2e6coB6eBJP/GkFsM8YU9XfyYC/voEYPXbG7lU4smAOAT9wHrsPx5sRIAbH1/yDwFZgSgDreiGOr4s7gWLnzyrgTuBOZ5m7gT04Mge2AIsDWN8pznp86qyT9fq61ldwbHh/CNiFY6+DQL4f4nAE8SSXY0H1+uL4QKoFunG0Lm/DMc60ETjg/DfFWbYAeMTl2lud7+WDwC0BrO9BHP3f1vvYypCbgGOXuwHfPwGq71PO9+dOHIE8072+zt8/F08CUV/n8cet961L2YC/vtaPzshVSqkwojNylVIqjGjQV0qpMKJBXymlwogGfaWUCiMa9JVSKoxo0FdKqTCiQV8ppcKIBn2llAoj/x+zqVfbA8TLEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../input/deepfake-kernel-data/lr_2e-3_epochs_20_patience_5.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yXmqUh3Usrt2"
   },
   "source": [
    "### Learning Rate: 0.004, Epochs: 12, Patience: 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_kg_hide-input": true,
    "colab": {},
    "colab_type": "code",
    "id": "kF0B49I5srt4",
    "outputId": "227a5407-2050-4be0-83a0-d769a11cd265"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../input/deepfake-kernel-data/lr_4e-3_epochs_12_patience_2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HdsZlRlUsruB"
   },
   "source": [
    "### Learning Rate: 0.004, Epochs: 30, Patience: 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_kg_hide-input": true,
    "colab": {},
    "colab_type": "code",
    "id": "vN0XBqL0sruC",
    "outputId": "8347a3ee-887a-4187-caf1-f8009050545c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../input/deepfake-kernel-data/lr_4e-3_epochs_30_patience_2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4d6eNJjjsruN"
   },
   "source": [
    "## Some Training Outputs in the VM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "_kg_hide-input": true,
    "colab": {},
    "colab_type": "code",
    "id": "7khch3HvsruP",
    "outputId": "e5464674-0514-4a11-aabf-e12f2e78e56f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../input/deepfake-kernel-data/google_cloud_vm_deepfake_training_screenshot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2u0hDnbNsruY"
   },
   "source": [
    "# Running the Instance in the Backend with Screen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "taYvQHR-sruZ"
   },
   "source": [
    "To leave the VM instance running in the backend, you can use `screen`. For installation details and how to execute it, instructions can be found in the source link below, and at https://stackoverflow.com/questions/48221807/google-cloud-instance-terminate-after-close-browser.\n",
    "\n",
    "Screen or GNU Screen is a terminal multiplexer. In other words, it means that you can start a screen session and then open any number of windows (virtual terminals) inside that session. Processes running in Screen will continue to run when their window is not visible even if you get disconnected.\n",
    "\n",
    "**Source:** https://linuxize.com/post/how-to-use-linux-screen/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vDch6cVpsrub"
   },
   "source": [
    "# Resnext & Xception Ensemble\n",
    "\n",
    "- The following have been taken from https://www.kaggle.com/khoongweihao/xception-resnext-ensemble-inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ka8zTaz_sruc"
   },
   "source": [
    "## Resnext Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b0fBU0cHsrue"
   },
   "outputs": [],
   "source": [
    "import os, sys, time\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fKMOkMTUsrup",
    "outputId": "c9a68e4f-16f0-4ed2-c0de-26395d9ec72c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dir = \"/kaggle/input/deepfake-detection-challenge/test_videos/\"\n",
    "\n",
    "test_videos = sorted([x for x in os.listdir(test_dir) if x[-4:] == \".mp4\"])\n",
    "frame_h = 5\n",
    "frame_l = 5\n",
    "len(test_videos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7R4_Iyemsru2",
    "outputId": "ce95dc0a-5940-4c78-aa33-4b4895e7fcbd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch version: 1.3.0\n",
      "CUDA version: 10.0.130\n",
      "cuDNN version: 7603\n"
     ]
    }
   ],
   "source": [
    "print(\"PyTorch version:\", torch.__version__)\n",
    "print(\"CUDA version:\", torch.version.cuda)\n",
    "print(\"cuDNN version:\", torch.backends.cudnn.version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AelVKTg3srvB",
    "outputId": "a70bf182-ed0b-4dd4-e773-f603a3056e57"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpu = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PNPma2FhsrvJ"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, \"/kaggle/input/blazeface-pytorch\")\n",
    "sys.path.insert(0, \"/kaggle/input/deepfakes-inference-demo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PlNpzITJsrvR"
   },
   "outputs": [],
   "source": [
    "from blazeface import BlazeFace\n",
    "facedet = BlazeFace().to(gpu)\n",
    "facedet.load_weights(\"/kaggle/input/blazeface-pytorch/blazeface.pth\")\n",
    "facedet.load_anchors(\"/kaggle/input/blazeface-pytorch/anchors.npy\")\n",
    "_ = facedet.train(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sJba01SvsrvY"
   },
   "outputs": [],
   "source": [
    "from helpers.read_video_1 import VideoReader\n",
    "from helpers.face_extract_1 import FaceExtractor\n",
    "\n",
    "frames_per_video = 64 #frame_h * frame_l\n",
    "video_reader = VideoReader()\n",
    "video_read_fn = lambda x: video_reader.read_frames(x, num_frames=frames_per_video)\n",
    "face_extractor = FaceExtractor(video_read_fn, facedet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YXvBLVaosrvf"
   },
   "outputs": [],
   "source": [
    "input_size = 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VMxQxxSUsrvl"
   },
   "outputs": [],
   "source": [
    "from torchvision.transforms import Normalize\n",
    "\n",
    "mean = [0.485, 0.456, 0.406]\n",
    "std = [0.229, 0.224, 0.225]\n",
    "normalize_transform = Normalize(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HROEXzV_srvs"
   },
   "outputs": [],
   "source": [
    "def isotropically_resize_image(img, size, resample=cv2.INTER_AREA):\n",
    "    h, w = img.shape[:2]\n",
    "    if w > h:\n",
    "        h = h * size // w\n",
    "        w = size\n",
    "    else:\n",
    "        w = w * size // h\n",
    "        h = size\n",
    "\n",
    "    resized = cv2.resize(img, (w, h), interpolation=resample)\n",
    "    return resized\n",
    "\n",
    "\n",
    "def make_square_image(img):\n",
    "    h, w = img.shape[:2]\n",
    "    size = max(h, w)\n",
    "    t = 0\n",
    "    b = size - h\n",
    "    l = 0\n",
    "    r = size - w\n",
    "    return cv2.copyMakeBorder(img, t, b, l, r, cv2.BORDER_CONSTANT, value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ibjBfVy_srvy"
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torchvision.models as models\n",
    "\n",
    "class MyResNeXt(models.resnet.ResNet):\n",
    "    def __init__(self, training=True):\n",
    "        super(MyResNeXt, self).__init__(block=models.resnet.Bottleneck,\n",
    "                                        layers=[3, 4, 6, 3], \n",
    "                                        groups=32, \n",
    "                                        width_per_group=4)\n",
    "        self.fc = nn.Linear(2048, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k13pXLBfsrv9"
   },
   "outputs": [],
   "source": [
    "checkpoint = torch.load(\"/kaggle/input/deepfakes-inference-demo/resnext.pth\", map_location=gpu)\n",
    "\n",
    "model = MyResNeXt().to(gpu)\n",
    "model.load_state_dict(checkpoint)\n",
    "_ = model.eval()\n",
    "\n",
    "del checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ejUYe-sYsrwE"
   },
   "outputs": [],
   "source": [
    "def predict_on_video(video_path, batch_size):\n",
    "    try:\n",
    "        # Find the faces for N frames in the video.\n",
    "        faces = face_extractor.process_video(video_path)\n",
    "\n",
    "        # Only look at one face per frame.\n",
    "        face_extractor.keep_only_best_face(faces)\n",
    "        \n",
    "        if len(faces) > 0:\n",
    "            # NOTE: When running on the CPU, the batch size must be fixed\n",
    "            # or else memory usage will blow up. (Bug in PyTorch?)\n",
    "            x = np.zeros((batch_size, input_size, input_size, 3), dtype=np.uint8)\n",
    "\n",
    "            # If we found any faces, prepare them for the model.\n",
    "            n = 0\n",
    "            for frame_data in faces:\n",
    "                for face in frame_data[\"faces\"]:\n",
    "                    # Resize to the model's required input size.\n",
    "                    # We keep the aspect ratio intact and add zero\n",
    "                    # padding if necessary.                    \n",
    "                    resized_face = isotropically_resize_image(face, input_size)\n",
    "                    resized_face = make_square_image(resized_face)\n",
    "\n",
    "                    if n < batch_size:\n",
    "                        x[n] = resized_face\n",
    "                        n += 1\n",
    "                    else:\n",
    "                        print(\"WARNING: have %d faces but batch size is %d\" % (n, batch_size))\n",
    "                    \n",
    "                    # Test time augmentation: horizontal flips.\n",
    "                    # TODO: not sure yet if this helps or not\n",
    "                    #x[n] = cv2.flip(resized_face, 1)\n",
    "                    #n += 1\n",
    "\n",
    "            if n > 0:\n",
    "                x = torch.tensor(x, device=gpu).float()\n",
    "\n",
    "                # Preprocess the images.\n",
    "                x = x.permute((0, 3, 1, 2))\n",
    "\n",
    "                for i in range(len(x)):\n",
    "                    x[i] = normalize_transform(x[i] / 255.)\n",
    "\n",
    "                # Make a prediction, then take the average.\n",
    "                with torch.no_grad():\n",
    "                    y_pred = model(x)\n",
    "                    y_pred = torch.sigmoid(y_pred.squeeze())\n",
    "                    return y_pred[:n].mean().item()\n",
    "\n",
    "    except Exception as e:\n",
    "        print(\"Prediction error on video %s: %s\" % (video_path, str(e)))\n",
    "\n",
    "    return 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aF3Q5QKHsrwK"
   },
   "outputs": [],
   "source": [
    "from concurrent.futures import ThreadPoolExecutor\n",
    "\n",
    "def predict_on_video_set(videos, num_workers):\n",
    "    def process_file(i):\n",
    "        filename = videos[i]\n",
    "        y_pred = predict_on_video(os.path.join(test_dir, filename), batch_size=frames_per_video)\n",
    "        return y_pred\n",
    "\n",
    "    with ThreadPoolExecutor(max_workers=num_workers) as ex:\n",
    "        predictions = ex.map(process_file, range(len(videos)))\n",
    "\n",
    "    return list(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PP0SWuYpsrwQ"
   },
   "outputs": [],
   "source": [
    "speed_test = False  # you have to enable this manually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0qZfJgFLsrwW"
   },
   "outputs": [],
   "source": [
    "if speed_test:\n",
    "    start_time = time.time()\n",
    "    speedtest_videos = test_videos[:5]\n",
    "    predictions = predict_on_video_set(speedtest_videos, num_workers=4)\n",
    "    elapsed = time.time() - start_time\n",
    "    print(\"Elapsed %f sec. Average per video: %f sec.\" % (elapsed, elapsed / len(speedtest_videos)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tq1caHytsrwe"
   },
   "outputs": [],
   "source": [
    "predictions = predict_on_video_set(test_videos, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XIBPYyu_srwl"
   },
   "outputs": [],
   "source": [
    "submission_df_resnext = pd.DataFrame({\"filename\": test_videos, \"label\": predictions})\n",
    "submission_df_resnext.to_csv(\"submission_resnext.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FeG5Lyncsrws"
   },
   "source": [
    "## Xception Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UBbCso9Qsrwu"
   },
   "outputs": [],
   "source": [
    "!pip install ../input/deepfake-xception-trained-model/pytorchcv-0.0.55-py2.py3-none-any.whl --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BTgy0xy8srw0",
    "outputId": "6b7806c5-27a2-4028-ec02-ac5a09824d7e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dir = \"/kaggle/input/deepfake-detection-challenge/test_videos/\"\n",
    "\n",
    "test_videos = sorted([x for x in os.listdir(test_dir) if x[-4:] == \".mp4\"])\n",
    "len(test_videos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9Dglf45rsrw7",
    "outputId": "76ee6e48-4f50-47ee-e4e1-14fec1568ae0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpu = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TDoOTF8ksrxD"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, \"/kaggle/input/blazeface-pytorch\")\n",
    "sys.path.insert(0, \"/kaggle/input/deepfakes-inference-demo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vCTauWx0srxK"
   },
   "outputs": [],
   "source": [
    "from blazeface import BlazeFace\n",
    "facedet = BlazeFace().to(gpu)\n",
    "facedet.load_weights(\"/kaggle/input/blazeface-pytorch/blazeface.pth\")\n",
    "facedet.load_anchors(\"/kaggle/input/blazeface-pytorch/anchors.npy\")\n",
    "_ = facedet.train(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "71iDmpyksrxS"
   },
   "outputs": [],
   "source": [
    "from helpers.read_video_1 import VideoReader\n",
    "from helpers.face_extract_1 import FaceExtractor\n",
    "\n",
    "frames_per_video = 64 # originally 4\n",
    "\n",
    "video_reader = VideoReader()\n",
    "video_read_fn = lambda x: video_reader.read_frames(x, num_frames=frames_per_video)\n",
    "face_extractor = FaceExtractor(video_read_fn, facedet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XMjKmwIAsrxY"
   },
   "outputs": [],
   "source": [
    "input_size = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iAVNE9GVsrxg"
   },
   "outputs": [],
   "source": [
    "from torchvision.transforms import Normalize\n",
    "\n",
    "mean = [0.485, 0.456, 0.406]\n",
    "std = [0.229, 0.224, 0.225]\n",
    "normalize_transform = Normalize(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gDY2m_6vsrxn"
   },
   "outputs": [],
   "source": [
    "def isotropically_resize_image(img, size, resample=cv2.INTER_AREA):\n",
    "    h, w = img.shape[:2]\n",
    "    if w > h:\n",
    "        h = h * size // w\n",
    "        w = size\n",
    "    else:\n",
    "        w = w * size // h\n",
    "        h = size\n",
    "\n",
    "    resized = cv2.resize(img, (w, h), interpolation=resample)\n",
    "    return resized\n",
    "\n",
    "\n",
    "def make_square_image(img):\n",
    "    h, w = img.shape[:2]\n",
    "    size = max(h, w)\n",
    "    t = 0\n",
    "    b = size - h\n",
    "    l = 0\n",
    "    r = size - w\n",
    "    return cv2.copyMakeBorder(img, t, b, l, r, cv2.BORDER_CONSTANT, value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3QSKD508srxt",
    "outputId": "e621b2c3-ed26-4c50-f5c5-138394806ecd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model.pth     model_v2.1.pth  pytorchcv-0.0.55-py2.py3-none-any.whl\r\n",
      "model_v0.pth  model_v2.pth\r\n",
      "model_v1.pth  model_v3.pth\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../input/deepfake-xception-trained-model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1MobIn4Dsrx1",
    "outputId": "3a2c76ba-7619-46f9-897e-7645025b8249"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "google_cloud_compute_engine_launch_vm.png\r\n",
      "google_cloud_vm.png\r\n",
      "google_cloud_vm_deepfake_training_screenshot.png\r\n",
      "lr_15e-2_epochs_42_patience_5.png\r\n",
      "lr_2e-3_epochs_10_patience_5.png\r\n",
      "lr_2e-3_epochs_20_patience_5.png\r\n",
      "lr_4e-3_epochs_12_patience_2.png\r\n",
      "lr_4e-3_epochs_30_patience_2.png\r\n",
      "model_50epochs_lr0001_patience5_factor01_batchsize32.pth\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../input/deepfake-kernel-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5lfdrEMPsrx-"
   },
   "outputs": [],
   "source": [
    "from pytorchcv.model_provider import get_model\n",
    "model = get_model(\"xception\", pretrained=False)\n",
    "model = nn.Sequential(*list(model.children())[:-1]) # Remove original output layer\n",
    "\n",
    "class Pooling(nn.Module):\n",
    "  def __init__(self):\n",
    "    super(Pooling, self).__init__()\n",
    "    \n",
    "    self.p1 = nn.AdaptiveAvgPool2d((1,1))\n",
    "    self.p2 = nn.AdaptiveMaxPool2d((1,1))\n",
    "\n",
    "  def forward(self, x):\n",
    "    x1 = self.p1(x)\n",
    "    x2 = self.p2(x)\n",
    "    return (x1+x2) * 0.5\n",
    "\n",
    "model[0].final_block.pool = nn.Sequential(nn.AdaptiveAvgPool2d((1,1)))\n",
    "\n",
    "class Head(torch.nn.Module):\n",
    "  def __init__(self, in_f, out_f):\n",
    "    super(Head, self).__init__()\n",
    "    \n",
    "    self.f = nn.Flatten()\n",
    "    self.l = nn.Linear(in_f, 512)\n",
    "    self.d = nn.Dropout(0.5)\n",
    "    self.o = nn.Linear(512, out_f)\n",
    "    self.b1 = nn.BatchNorm1d(in_f)\n",
    "    self.b2 = nn.BatchNorm1d(512)\n",
    "    self.r = nn.ReLU()\n",
    "\n",
    "  def forward(self, x):\n",
    "    x = self.f(x)\n",
    "    x = self.b1(x)\n",
    "    x = self.d(x)\n",
    "\n",
    "    x = self.l(x)\n",
    "    x = self.r(x)\n",
    "    x = self.b2(x)\n",
    "    x = self.d(x)\n",
    "\n",
    "    out = self.o(x)\n",
    "    return out\n",
    "\n",
    "class FCN(torch.nn.Module):\n",
    "  def __init__(self, base, in_f):\n",
    "    super(FCN, self).__init__()\n",
    "    self.base = base\n",
    "    self.h1 = Head(in_f, 1)\n",
    "  \n",
    "  def forward(self, x):\n",
    "    x = self.base(x)\n",
    "    return self.h1(x)\n",
    "\n",
    "net = []\n",
    "model = FCN(model, 2048)\n",
    "model = model.cuda()\n",
    "model.load_state_dict(torch.load('../input/deepfake-kernel-data/model_50epochs_lr0001_patience5_factor01_batchsize32.pth')) # new, updated\n",
    "net.append(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jn_Y__CWsryL"
   },
   "source": [
    "### Prediction Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l6xxIEaCsryM"
   },
   "outputs": [],
   "source": [
    "def predict_on_video(video_path, batch_size):\n",
    "    try:\n",
    "        # Find the faces for N frames in the video.\n",
    "        faces = face_extractor.process_video(video_path)\n",
    "\n",
    "        # Only look at one face per frame.\n",
    "        face_extractor.keep_only_best_face(faces)\n",
    "        \n",
    "        if len(faces) > 0:\n",
    "            # NOTE: When running on the CPU, the batch size must be fixed\n",
    "            # or else memory usage will blow up. (Bug in PyTorch?)\n",
    "            x = np.zeros((batch_size, input_size, input_size, 3), dtype=np.uint8)\n",
    "\n",
    "            # If we found any faces, prepare them for the model.\n",
    "            n = 0\n",
    "            for frame_data in faces:\n",
    "                for face in frame_data[\"faces\"]:\n",
    "                    # Resize to the model's required input size.\n",
    "                    # We keep the aspect ratio intact and add zero\n",
    "                    # padding if necessary.                    \n",
    "                    resized_face = isotropically_resize_image(face, input_size)\n",
    "                    resized_face = make_square_image(resized_face)\n",
    "\n",
    "                    if n < batch_size:\n",
    "                        x[n] = resized_face\n",
    "                        n += 1\n",
    "                    else:\n",
    "                        print(\"WARNING: have %d faces but batch size is %d\" % (n, batch_size))\n",
    "                    \n",
    "                    # Test time augmentation: horizontal flips.\n",
    "                    # TODO: not sure yet if this helps or not\n",
    "                    #x[n] = cv2.flip(resized_face, 1)\n",
    "                    #n += 1\n",
    "\n",
    "            if n > 0:\n",
    "                x = torch.tensor(x, device=gpu).float()\n",
    "\n",
    "                # Preprocess the images.\n",
    "                x = x.permute((0, 3, 1, 2))\n",
    "\n",
    "                for i in range(len(x)):\n",
    "                    x[i] = normalize_transform(x[i] / 255.)\n",
    "#                     x[i] = x[i] / 255.\n",
    "\n",
    "                # Make a prediction, then take the average.\n",
    "                with torch.no_grad():\n",
    "                    y_pred = model(x)\n",
    "                    y_pred = torch.sigmoid(y_pred.squeeze())\n",
    "                    return y_pred[:n].mean().item()\n",
    "\n",
    "    except Exception as e:\n",
    "        print(\"Prediction error on video %s: %s\" % (video_path, str(e)))\n",
    "\n",
    "    return 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cB5vEvyesryS"
   },
   "outputs": [],
   "source": [
    "from concurrent.futures import ThreadPoolExecutor\n",
    "\n",
    "def predict_on_video_set(videos, num_workers):\n",
    "    def process_file(i):\n",
    "        filename = videos[i]\n",
    "        y_pred = predict_on_video(os.path.join(test_dir, filename), batch_size=frames_per_video)\n",
    "        return y_pred\n",
    "\n",
    "    with ThreadPoolExecutor(max_workers=num_workers) as ex:\n",
    "        predictions = ex.map(process_file, range(len(videos)))\n",
    "\n",
    "    return list(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tV14ZC6psryZ"
   },
   "outputs": [],
   "source": [
    "speed_test = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lDjTaxtwsryg"
   },
   "outputs": [],
   "source": [
    "if speed_test:\n",
    "    start_time = time.time()\n",
    "    speedtest_videos = test_videos[:5]\n",
    "    predictions = predict_on_video_set(speedtest_videos, num_workers=4)\n",
    "    elapsed = time.time() - start_time\n",
    "    print(\"Elapsed %f sec. Average per video: %f sec.\" % (elapsed, elapsed / len(speedtest_videos)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GvzhJLAAsryl",
    "outputId": "d0b4b727-1136-4db8-dcfd-ebb51c7bbb11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 47min 49s, sys: 1min 33s, total: 49min 22s\n",
      "Wall time: 25min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model.eval()\n",
    "predictions = predict_on_video_set(test_videos, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CDNEhUtXsrys"
   },
   "outputs": [],
   "source": [
    "submission_df_xception = pd.DataFrame({\"filename\": test_videos, \"label\": predictions})\n",
    "submission_df_xception.to_csv(\"submission_xception.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-CJ0dcdEsryy",
    "outputId": "229fab7d-76b1-44d3-9eb5-9051132b2f83"
   },
   "outputs": [
    {
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       "      <th>filename</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aassnaulhq.mp4</td>\n",
       "      <td>0.514175</td>\n",
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       "      <th>1</th>\n",
       "      <td>aayfryxljh.mp4</td>\n",
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       "      <th>2</th>\n",
       "      <td>acazlolrpz.mp4</td>\n",
       "      <td>0.725788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adohdulfwb.mp4</td>\n",
       "      <td>0.060911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ahjnxtiamx.mp4</td>\n",
       "      <td>0.880215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         filename     label\n",
       "0  aassnaulhq.mp4  0.514175\n",
       "1  aayfryxljh.mp4  0.004443\n",
       "2  acazlolrpz.mp4  0.725788\n",
       "3  adohdulfwb.mp4  0.060911\n",
       "4  ahjnxtiamx.mp4  0.880215"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_df_resnext.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XKJvN5-tsry7",
    "outputId": "003ffe3a-d321-4d9a-a4e3-e90d6e4e6677"
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>aayfryxljh.mp4</td>\n",
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       "      <td>adohdulfwb.mp4</td>\n",
       "      <td>0.005191</td>\n",
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      "text/plain": [
       "         filename     label\n",
       "0  aassnaulhq.mp4  0.971394\n",
       "1  aayfryxljh.mp4  0.009612\n",
       "2  acazlolrpz.mp4  0.871579\n",
       "3  adohdulfwb.mp4  0.005191\n",
       "4  ahjnxtiamx.mp4  0.748403"
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     "execution_count": 48,
     "metadata": {
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     "output_type": "execute_result"
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   ],
   "source": [
    "submission_df_xception.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2GKdPeI6srzE"
   },
   "source": [
    "## Ensemble of Resnext & Xception\n",
    "\n",
    "- Resnext single model public score: 0.46441\n",
    "- Xception single model public score: 0.50480"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Nf7By2O4srzG"
   },
   "outputs": [],
   "source": [
    "submission_df = pd.DataFrame({\"filename\": test_videos})\n",
    "submission_df[\"label\"] = 0.58*submission_df_resnext[\"label\"] + 0.42*submission_df_xception[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n-fg_19csrzU"
   },
   "outputs": [],
   "source": [
    "submission_df.to_csv(\"submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4vcAWT_Vsrza"
   },
   "source": [
    "### Thanks for checking this out and I hope it will help in some way. May Gauss bless us all."
   ]
  }
 ],
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