Face_Resto_Playground.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPdyojkmx9NP5yeYIWdHBQP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mohripan/Machine-Learning/blob/main/Face_Resto_Playground.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5_UzJ3yOocL",
        "outputId": "ba9c78cf-4db6-47a1-9958-7a87c6ae8834"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/172.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m \u001b[32m163.8/172.5 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m172.5/172.5 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m299.2/299.2 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m79.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.3/250.3 kB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for basicsr (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting albumentations\n",
            "  Downloading albumentations-1.3.1-py3-none-any.whl (125 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m125.7/125.7 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: albumentations\n",
            "  Attempting uninstall: albumentations\n",
            "    Found existing installation: albumentations 1.2.1\n",
            "    Uninstalling albumentations-1.2.1:\n",
            "      Successfully uninstalled albumentations-1.2.1\n",
            "Successfully installed albumentations-1.3.1\n"
          ]
        }
      ],
      "source": [
        "!pip -q install basicsr\n",
        "!pip install --upgrade --force-reinstall --no-deps albumentations"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygZQ9tnJmSWD",
        "outputId": "bc1e7eab-e608-4774-d709-edf655fc82d9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%shell\n",
        "cp '/content/gdrive/My Drive/KaggleAPI/kaggle.json' kaggle.json\n",
        "pip install -q kaggle\n",
        "mkdir ~/.kaggle\n",
        "cp kaggle.json ~/.kaggle/\n",
        "chmod 600 ~/.kaggle/kaggle.json\n",
        "kaggle datasets download -d sergiovirahonda/shape-predictor-68-face-landmarksdat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfM0IWDPmpT6",
        "outputId": "68d6193f-fcba-4af5-9387-02e12ac36d0d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading shape-predictor-68-face-landmarksdat.zip to /content\n",
            " 97% 67.0M/69.3M [00:03<00:00, 23.6MB/s]\n",
            "100% 69.3M/69.3M [00:03<00:00, 19.3MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/shape-predictor-68-face-landmarksdat.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lxrxnyrmyFu",
        "outputId": "1f9feb5e-4e50-4b6f-84ff-e9fc119b23f3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/shape-predictor-68-face-landmarksdat.zip\n",
            "  inflating: shape_predictor_68_face_landmarks.dat  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn import init as init\n",
        "from torch.nn.modules.batchnorm import _BatchNorm\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import cv2\n",
        "from PIL import Image\n",
        "from torch.nn.utils import spectral_norm"
      ],
      "metadata": {
        "id": "T6oLUVQqOwIJ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomDownsampleUnshuffle(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(CustomDownsampleUnshuffle, self).__init__()\n",
        "    self.pixel_unshuffle_2x = nn.PixelUnshuffle(2)\n",
        "    self.pixel_unshuffle_4x = nn.PixelUnshuffle(4)\n",
        "\n",
        "  def forward(self, x):\n",
        "    # Downsample the image\n",
        "    x2 = F.interpolate(x, scale_factor = 0.5, mode = 'bilinear', align_corners = False)\n",
        "    x4 = F.interpolate(x, scale_factor = 0.25, mode = 'bilinear', align_corners = False)\n",
        "\n",
        "    # Pixel Unshuffle\n",
        "    x_unshuffled_4x = self.pixel_unshuffle_4x(x)\n",
        "    x2_unshuffled_2x = self.pixel_unshuffle_2x(x2)\n",
        "\n",
        "    # Concatenate along the channel dimension\n",
        "    x_concat = torch.cat((x_unshuffled_4x, x2_unshuffled_2x, x4), dim = 1)\n",
        "    return x_concat"
      ],
      "metadata": {
        "id": "C3jYWLv7O_fi"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import files\n",
        "import shutil\n",
        "\n",
        "upload_folder = 'upload'\n",
        "result_folder = 'results'\n",
        "\n",
        "if os.path.isdir(upload_folder):\n",
        "    shutil.rmtree(upload_folder)\n",
        "if os.path.isdir(result_folder):\n",
        "    shutil.rmtree(result_folder)\n",
        "os.mkdir(upload_folder)\n",
        "os.mkdir(result_folder)\n",
        "\n",
        "# upload images\n",
        "uploaded = files.upload()\n",
        "for filename in uploaded.keys():\n",
        "  dst_path = os.path.join(upload_folder, filename)\n",
        "  print(f'move {filename} to {dst_path}')\n",
        "  shutil.move(filename, dst_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "-ylhex9cPAxj",
        "outputId": "225227dc-2023-4392-94c1-b2a91253f5ce"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0b5be1db-0d24-4ff5-90c5-fb3e4ed5d85a\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-0b5be1db-0d24-4ff5-90c5-fb3e4ed5d85a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Sabina-Altynbekova-HD-Wallpaper.jpg to Sabina-Altynbekova-HD-Wallpaper.jpg\n",
            "Saving waifu.jpg to waifu.jpg\n",
            "move Sabina-Altynbekova-HD-Wallpaper.jpg to upload/Sabina-Altynbekova-HD-Wallpaper.jpg\n",
            "move waifu.jpg to upload/waifu.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def display(img):\n",
        "  image = Image.open(img)\n",
        "  image = np.array(image)\n",
        "  plt.imshow(image)\n",
        "\n",
        "input_folder = 'upload'\n",
        "input_list = sorted(glob.glob(os.path.join(input_folder, '*')))\n",
        "\n",
        "for input_path in input_list:\n",
        "  display(input_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "T2mAYe_VPCMT",
        "outputId": "9828440a-a1be-4974-d84c-416c5675e86e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def change_to_torch(img):\n",
        "  with torch.no_grad():\n",
        "    img_copy = np.copy(img)\n",
        "    img_copy = torch.from_numpy(img_copy)\n",
        "    img_copy = img_copy.float()\n",
        "    img_copy = img_copy / 255\n",
        "    img_copy = img_copy.permute(2, 0, 1)\n",
        "    return img_copy\n",
        "\n",
        "image = Image.open(input_list[0])\n",
        "image = np.array(image)\n",
        "\n",
        "image = change_to_torch(image)\n",
        "print(image.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bOz6YbrIPNOY",
        "outputId": "889d037c-fe6d-4053-8f98-41d88db3de1d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3, 1280, 1920])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "dir = 'upload'\n",
        "\n",
        "images_dir = sorted([os.path.join(dir, f) for f in os.listdir(dir)])\n",
        "images_filepaths = [*images_dir]\n",
        "\n",
        "imgs = []\n",
        "\n",
        "for i in images_filepaths:\n",
        "  img = np.array(Image.open(i))\n",
        "  img = cv2.resize(img, (512, 512), interpolation = cv2.INTER_AREA)\n",
        "  imgs.append(img)"
      ],
      "metadata": {
        "id": "UMZeunWOPRNq"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir dataset"
      ],
      "metadata": {
        "id": "7szHCY0lUekJ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from skimage.io import imsave\n",
        "import os\n",
        "\n",
        "# Create the dataset directory if it doesn't exist\n",
        "if not os.path.exists('dataset'):\n",
        "    os.makedirs('dataset')\n",
        "\n",
        "# Iterate over the imgs list and save each image\n",
        "for i, img in enumerate(imgs):\n",
        "    # Create the filename\n",
        "    file_name = f'dataset/image_{i}.png'\n",
        "    # Save the image\n",
        "    imsave(file_name, img)"
      ],
      "metadata": {
        "id": "h_XxcNCdUgWd"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_layer(basic_block, num_basic_block, **kwarg):\n",
        "    \"\"\"Make layers by stacking the same blocks.\n",
        "\n",
        "    Args:\n",
        "        basic_block (nn.module): nn.module class for basic block.\n",
        "        num_basic_block (int): number of blocks.\n",
        "\n",
        "    Returns:\n",
        "        nn.Sequential: Stacked blocks in nn.Sequential.\n",
        "    \"\"\"\n",
        "    layers = []\n",
        "    for _ in range(num_basic_block):\n",
        "        layers.append(basic_block(**kwarg))\n",
        "    return nn.Sequential(*layers)"
      ],
      "metadata": {
        "id": "iG7yKENTQa7S"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@torch.no_grad()\n",
        "def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):\n",
        "    \"\"\"Initialize network weights.\n",
        "\n",
        "    Args:\n",
        "        module_list (list[nn.Module] | nn.Module): Modules to be initialized.\n",
        "        scale (float): Scale initialized weights, especially for residual\n",
        "            blocks. Default: 1.\n",
        "        bias_fill (float): The value to fill bias. Default: 0\n",
        "        kwargs (dict): Other arguments for initialization function.\n",
        "    \"\"\"\n",
        "    if not isinstance(module_list, list):\n",
        "        module_list = [module_list]\n",
        "    for module in module_list:\n",
        "        for m in module.modules():\n",
        "            if isinstance(m, nn.Conv2d):\n",
        "                init.kaiming_normal_(m.weight, **kwargs)\n",
        "                m.weight.data *= scale\n",
        "                if m.bias is not None:\n",
        "                    m.bias.data.fill_(bias_fill)\n",
        "            elif isinstance(m, nn.Linear):\n",
        "                init.kaiming_normal_(m.weight, **kwargs)\n",
        "                m.weight.data *= scale\n",
        "                if m.bias is not None:\n",
        "                    m.bias.data.fill_(bias_fill)\n",
        "            elif isinstance(m, _BatchNorm):\n",
        "                init.constant_(m.weight, 1)\n",
        "                if m.bias is not None:\n",
        "                    m.bias.data.fill_(bias_fill)"
      ],
      "metadata": {
        "id": "BOtkL6C2Qkei"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ResidualDenseBlock(nn.Module):\n",
        "    \"\"\"Residual Dense Block.\n",
        "\n",
        "    Used in RRDB block in ESRGAN.\n",
        "\n",
        "    Args:\n",
        "        num_feat (int): Channel number of intermediate features.\n",
        "        num_grow_ch (int): Channels for each growth.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, num_feat=64, num_grow_ch=32):\n",
        "        super(ResidualDenseBlock, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)\n",
        "        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)\n",
        "        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)\n",
        "        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)\n",
        "        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)\n",
        "\n",
        "        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
        "\n",
        "        # initialization\n",
        "        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x1 = self.lrelu(self.conv1(x))\n",
        "        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))\n",
        "        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))\n",
        "        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))\n",
        "        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))\n",
        "        # Empirically, we use 0.2 to scale the residual for better performance\n",
        "        return x5 * 0.2 + x"
      ],
      "metadata": {
        "id": "gEbn0u_WQmXr"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class RRDB(nn.Module):\n",
        "    \"\"\"Residual in Residual Dense Block.\n",
        "\n",
        "    Used in RRDB-Net in ESRGAN.\n",
        "\n",
        "    Args:\n",
        "        num_feat (int): Channel number of intermediate features.\n",
        "        num_grow_ch (int): Channels for each growth.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, num_feat, num_grow_ch=32):\n",
        "        super(RRDB, self).__init__()\n",
        "        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)\n",
        "        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)\n",
        "        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.rdb1(x)\n",
        "        out = self.rdb2(out)\n",
        "        out = self.rdb3(out)\n",
        "        # Empirically, we use 0.2 to scale the residual for better performance\n",
        "        return out * 0.2 + x"
      ],
      "metadata": {
        "id": "-pRiGbUSQozi"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class RRDBNet(nn.Module):\n",
        "    \"\"\"Networks consisting of Residual in Residual Dense Block, which is used\n",
        "    in ESRGAN.\n",
        "\n",
        "    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.\n",
        "\n",
        "    We extend ESRGAN for scale x2 and scale x1.\n",
        "    Note: This is one option for scale 1, scale 2 in RRDBNet.\n",
        "    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size\n",
        "    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.\n",
        "\n",
        "    Args:\n",
        "        num_in_ch (int): Channel number of inputs.\n",
        "        num_out_ch (int): Channel number of outputs.\n",
        "        num_feat (int): Channel number of intermediate features.\n",
        "            Default: 64\n",
        "        num_block (int): Block number in the trunk network. Defaults: 23\n",
        "        num_grow_ch (int): Channels for each growth. Default: 32.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, num_in_ch, num_out_ch, num_feat=64, num_block=23, num_grow_ch=32):\n",
        "        super(RRDBNet, self).__init__()\n",
        "        self.pixel_unshuffle = CustomDownsampleUnshuffle()\n",
        "        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)\n",
        "        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)\n",
        "        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n",
        "        # upsample\n",
        "        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n",
        "        self.conv_afterup = nn.Conv2d(num_feat, 48, 3, 1, 1)\n",
        "        self.conv_up2 = nn.PixelShuffle(4)\n",
        "        self.conv_hr = nn.Conv2d(3, 3, 3, 1, 1)\n",
        "        self.conv_last = nn.Conv2d(3, num_out_ch, 3, 1, 1)\n",
        "\n",
        "        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
        "\n",
        "    def forward(self, x):\n",
        "        feat = self.pixel_unshuffle(x)\n",
        "        feat = self.conv_first(feat)\n",
        "        body_feat = self.conv_body(self.body(feat))\n",
        "        feat = feat + body_feat\n",
        "        # upsample\n",
        "        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))\n",
        "        feat = self.lrelu(self.conv_afterup(feat))\n",
        "        feat = self.conv_up2(feat)\n",
        "        feat = self.lrelu(self.conv_hr(feat))\n",
        "        out = self.conv_last(self.lrelu(self.conv_hr(feat)))\n",
        "        return out"
      ],
      "metadata": {
        "id": "4K5HUIknQugU"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RRDBNet(63, 3, num_block = 1)\n",
        "img = torch.randn(1, 3, 512, 512)\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-xTrIY3RA_H",
        "outputId": "af81b85f-335d-4406-cd79-a2cfcef0bb5a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 3, 512, 512])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def display_from_torch(img):\n",
        "  img = img.squeeze(0)\n",
        "  img = img.permute(1, 2, 0)\n",
        "  img = img.cpu().numpy()\n",
        "\n",
        "  plt.imshow(img)"
      ],
      "metadata": {
        "id": "x_o96NxRQ_BD"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = Image.open(input_list[0])\n",
        "img = np.array(img)\n",
        "\n",
        "img = cv2.resize(img, (img.shape[1] // 2, img.shape[0] // 2))\n",
        "\n",
        "print(img.shape)\n",
        "\n",
        "img = change_to_torch(img)\n",
        "img = img.unsqueeze(0)\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsZEud-gRIN3",
        "outputId": "6581950d-a16d-4a8e-830f-7aefbad6fe14"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(640, 960, 3)\n",
            "torch.Size([1, 3, 640, 960])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = change_to_torch(imgs[0])\n",
        "img = img.unsqueeze(0)\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6m-RcxD2RJxx",
        "outputId": "c14a15d1-d0bf-46c4-e684-b5e6d96405da"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 3, 512, 512])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RRDBNet(63, 3, num_block = 1)\n",
        "image = model(img)"
      ],
      "metadata": {
        "id": "T9-kAl5XRW6p"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image = image.squeeze(0)\n",
        "image = image.permute(1, 2, 0)\n",
        "image = image + torch.abs(image.min())\n",
        "image = image / image.max()\n",
        "image = image.detach().numpy()\n",
        "\n",
        "plt.imshow(image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "GpJPZyzdRadk",
        "outputId": "cf301983-fe28-4cc9-9bff-fb3febea1818"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb1c5967b20>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class VGG19Discriminator(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(VGG19Discriminator, self).__init__()\n",
        "\n",
        "        self.features = nn.Sequential(\n",
        "            # Block 1\n",
        "            nn.Conv2d(3, 64, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.AvgPool2d(kernel_size=2, stride=2),\n",
        "\n",
        "            # Block 2\n",
        "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.AvgPool2d(kernel_size=2, stride=2),\n",
        "\n",
        "            # Block 3\n",
        "            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.AvgPool2d(kernel_size=2, stride=2),\n",
        "\n",
        "            # Block 4\n",
        "            nn.Conv2d(256, 512, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.AvgPool2d(kernel_size=2, stride=2),\n",
        "\n",
        "            # Block 5\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.AvgPool2d(kernel_size=2, stride=2),\n",
        "        )\n",
        "\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Linear(512 * 16 * 16, 4096),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Dropout(),\n",
        "            nn.Linear(4096, 4096),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Dropout(),\n",
        "            nn.Linear(4096, 1),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.features(x)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.classifier(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "2MohGXD8hNLT"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def logistic_loss(y_real, y_fake):\n",
        "    real_loss = F.softplus(-y_real)  # -log( sigmoid(y_real) )\n",
        "    fake_loss = F.softplus(y_fake)   # -log( 1 - sigmoid(y_fake) )\n",
        "    return real_loss + fake_loss"
      ],
      "metadata": {
        "id": "JuuIpFAbhUOZ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "discriminator = VGG19Discriminator().to(device)  # Move model to GPU memory\n",
        "\n",
        "img = Image.open(input_list[0])\n",
        "img = np.array(img)\n",
        "\n",
        "img = cv2.resize(img, (512, 512))\n",
        "\n",
        "real_images = change_to_torch(img).unsqueeze(0)\n",
        "fake_images = torch.clone(real_images)\n",
        "\n",
        "with torch.no_grad():\n",
        "  # Assuming you have a real image batch `real_images` and generated `fake_images`\n",
        "  real_images = real_images.to(device)  # Move tensors to GPU memory\n",
        "  fake_images = fake_images.to(device)\n",
        "\n",
        "  real_outputs = discriminator(real_images)\n",
        "  fake_outputs = discriminator(fake_images)\n",
        "\n",
        "  adv_loss = logistic_loss(real_outputs, fake_outputs)\n",
        "  print(adv_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tOCwh3qiRlm",
        "outputId": "cbf016c2-9138-413d-ac63-b0644d604059"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1.3832]], device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torchvision.models import vgg19\n",
        "from torch import nn\n",
        "\n",
        "# Load VGG19 without the fully-connected layers and set to evaluation mode\n",
        "vgg = vgg19(pretrained=True).features[:23].eval()\n",
        "for param in vgg.parameters():\n",
        "    param.requires_grad = False\n",
        "if torch.cuda.is_available():\n",
        "    vgg = vgg.cuda()\n",
        "\n",
        "class ReconstructionPerceptualLoss(nn.Module):\n",
        "    def __init__(self, vgg):\n",
        "      super(ReconstructionPerceptualLoss, self).__init__()\n",
        "      self.vgg = vgg\n",
        "      self.loss_fn = nn.L1Loss()\n",
        "\n",
        "    def forward(self, x, y):\n",
        "      reconstruction_loss = self.loss_fn(x, y)\n",
        "      x_vgg, y_vgg = self.vgg(x), self.vgg(y)\n",
        "      loss = self.loss_fn(x_vgg, y_vgg)\n",
        "      return reconstruction_loss * 0.1 + loss"
      ],
      "metadata": {
        "id": "jm4rmLqNxSyj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1902742f-3777-4feb-8df9-09fc6e592a54"
      },
      "execution_count": 27,
      "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=VGG19_Weights.IMAGENET1K_V1`. You can also use `weights=VGG19_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n",
            "100%|██████████| 548M/548M [00:06<00:00, 87.1MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reconstruction_loss = ReconstructionPerceptualLoss(vgg).to(device)  # Move model to GPU memory\n",
        "\n",
        "img = Image.open(input_list[0])\n",
        "img = np.array(img)\n",
        "\n",
        "img = cv2.resize(img, (512, 512))\n",
        "\n",
        "real_images = change_to_torch(img).unsqueeze(0)\n",
        "fake_images = torch.clone(real_images)\n",
        "\n",
        "with torch.no_grad():\n",
        "  # Assuming you have a real image batch `real_images` and generated `fake_images`\n",
        "  real_images = real_images.to(device)  # Move tensors to GPU memory\n",
        "  fake_images = fake_images.to(device)\n",
        "\n",
        "  rec_loss = reconstruction_loss(real_images, fake_images)\n",
        "  print(rec_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IszBljkbjOYO",
        "outputId": "30093065-c9dc-415f-91a9-0b776916760b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0., device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import dlib\n",
        "\n",
        "# Initialize dlib's face detector (HOG-based)\n",
        "detector = dlib.get_frontal_face_detector()\n",
        "\n",
        "# Load the predictor\n",
        "predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')\n",
        "\n",
        "# Load the image using OpenCV\n",
        "img = cv2.imread(input_list[0])\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "# Convert to grayscale\n",
        "gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
        "\n",
        "# Detect faces in the image\n",
        "faces = detector(gray)\n",
        "\n",
        "for face in faces:\n",
        "    # Get the landmarks/parts for the face in box d.\n",
        "    shape = predictor(gray, face)\n",
        "\n",
        "    # Draw the face landmarks on the screen.\n",
        "    for i in range(1, 68):  # There are 68 landmark points on each face\n",
        "        cv2.circle(img, (shape.part(i).x, shape.part(i).y), 2, (0, 255, 0), -1)\n",
        "\n",
        "plt.imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "sWk0DGJaladM",
        "outputId": "8bdb2baa-a098-4bea-ea83-dfeb090b5c49"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb1b079b730>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def torch_to_numpy(tensor):\n",
        "    # Permutes the tensor dimensions to BxHxWxC\n",
        "    tensor = tensor.permute(0, 2, 3, 1)\n",
        "    # Detaches the tensor from the graph and converts it to a numpy array\n",
        "    return tensor.detach().cpu().numpy()\n",
        "\n",
        "def get_roi_boxes(shape):\n",
        "    # Define the indices for the eyes and mouth landmarks\n",
        "    left_eye_indices = list(range(36, 42))\n",
        "    right_eye_indices = list(range(42, 48))\n",
        "    mouth_indices = list(range(48, 68))\n",
        "\n",
        "    roi_lists = [left_eye_indices, right_eye_indices, mouth_indices]\n",
        "\n",
        "    boxes = []\n",
        "    for roi_list in roi_lists:\n",
        "        xs = [shape.part(i).x for i in roi_list]\n",
        "        ys = [shape.part(i).y for i in roi_list]\n",
        "        x1, y1 = min(xs), min(ys)\n",
        "        x2, y2 = max(xs), max(ys)\n",
        "        boxes.append((x1, y1, x2, y2))\n",
        "\n",
        "    return boxes\n",
        "\n",
        "def resize_images(image_batch):\n",
        "    image_batch = torch_to_numpy(image_batch)\n",
        "    res_images = []\n",
        "    for img in image_batch:\n",
        "        res_img = cv2.resize(img, (32, 32))\n",
        "        res_images.append(res_img)\n",
        "    return np.array(res_images)\n",
        "\n",
        "def get_boxes_batch(image_batch):\n",
        "  # Load the predictor\n",
        "  predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')\n",
        "\n",
        "  boxes_batch = []\n",
        "\n",
        "  for img in image_batch:\n",
        "    # Convert to grayscale\n",
        "    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
        "\n",
        "    # Detect faces in the image\n",
        "    faces = detector(gray)\n",
        "\n",
        "    for face in faces:\n",
        "        # Get the landmarks/parts for the face in box d.\n",
        "        shape = predictor(gray, face)\n",
        "\n",
        "\n",
        "    boxes = get_roi_boxes(shape)\n",
        "    boxes_batch.append(boxes)\n",
        "\n",
        "  return np.array(boxes_batch)\n",
        "\n",
        "def crop_rois(image_batch, boxes_batch):\n",
        "    all_rois = []\n",
        "    for image, boxes in zip(image_batch, boxes_batch):\n",
        "        rois = []\n",
        "        for box in boxes:\n",
        "            x1, y1, x2, y2 = box\n",
        "            roi = image[y1:y2, x1:x2, :]\n",
        "            roi = cv2.resize(roi, (32, 32))\n",
        "            rois.append(roi)\n",
        "        all_rois.append(rois)\n",
        "    return np.array(all_rois)\n",
        "\n",
        "def gram_matrix(input):\n",
        "    a, b, c, d = input.size()  # a=batch size\n",
        "    # b=number of feature maps\n",
        "    # (c,d)=dimensions of a f. map (N=c*d)\n",
        "\n",
        "    features = input.view(a, b, c * d)  # reshape the tensor to vectorize the feature maps\n",
        "\n",
        "    G = torch.bmm(features, features.transpose(1, 2))  # compute the Gram product\n",
        "\n",
        "    # normalize the values of the gram matrix\n",
        "    # by dividing by the number of element in each feature maps\n",
        "    G = G.div(b * c * d)\n",
        "\n",
        "    return G"
      ],
      "metadata": {
        "id": "sNSHALtknMez"
      },
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dlib\n",
        "from torchvision.models import vgg19\n",
        "\n",
        "# Load VGG19 without the fully-connected layers and set to evaluation mode\n",
        "vgg = vgg19(pretrained=True).features[:23].eval()\n",
        "\n",
        "class LocalDiscriminator(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(LocalDiscriminator, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)\n",
        "        self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)\n",
        "        self.fc = nn.Linear(8192, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.leaky_relu(self.conv1(x), 0.2)\n",
        "        x = F.leaky_relu(self.conv2(x), 0.2)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.fc(x)\n",
        "        return x\n",
        "\n",
        "class FacialComponentLoss(nn.Module):\n",
        "    def __init__(self, local_discriminators, device = 'cuda', vgg = vgg):\n",
        "        super(FacialComponentLoss, self).__init__()\n",
        "        self.local_discriminators = local_discriminators\n",
        "        self.loss_fn = nn.BCEWithLogitsLoss()\n",
        "        self.device = device\n",
        "        self.vgg = vgg\n",
        "\n",
        "    def forward(self, real_images, fake_images):\n",
        "        total_loss = 0\n",
        "\n",
        "        real_images = torch_to_numpy(real_images) * 255\n",
        "        real_images = real_images.astype(np.uint8)\n",
        "        fake_images = torch_to_numpy(fake_images) * 255\n",
        "        fake_images = fake_images.astype(np.uint8)\n",
        "\n",
        "        boxes_batch = get_boxes_batch(real_images)\n",
        "\n",
        "        print(boxes_batch.shape)\n",
        "\n",
        "        real_rois = crop_rois(real_images, boxes_batch)\n",
        "        fake_rois = crop_rois(fake_images, boxes_batch)\n",
        "\n",
        "        for i, discriminator in enumerate(self.local_discriminators):\n",
        "            real_roi = real_rois[:, i]\n",
        "            fake_roi = fake_rois[:, i]\n",
        "\n",
        "            real_roi = torch.tensor(real_roi, dtype = torch.float32).to(self.device)\n",
        "            fake_roi = torch.tensor(fake_roi, dtype = torch.float32).to(self.device)\n",
        "\n",
        "            real_roi = real_roi / 255.0\n",
        "            fake_roi = fake_roi / 255.0\n",
        "\n",
        "            real_roi = real_roi.permute(0, 3, 1, 2)\n",
        "            fake_roi = fake_roi.permute(0, 3, 1, 2)\n",
        "\n",
        "            discriminator = discriminator.to(self.device)\n",
        "\n",
        "            # Local discriminative loss\n",
        "            local_disc_loss = self.loss_fn(real_outputs, torch.ones_like(real_outputs)) + \\\n",
        "                              self.loss_fn(fake_outputs, torch.zeros_like(fake_outputs))\n",
        "\n",
        "            # Feature style loss\n",
        "            real_features = self.vgg(real_roi)\n",
        "            fake_features = self.vgg(fake_roi)\n",
        "            real_gram = gram_matrix(real_features)\n",
        "            fake_gram = gram_matrix(fake_features)\n",
        "            feature_style_loss = F.l1_loss(fake_gram, real_gram)\n",
        "\n",
        "            total_loss += local_disc_loss + feature_style_loss * 200\n",
        "\n",
        "        return total_loss"
      ],
      "metadata": {
        "id": "G77Hf-J-kskd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17c8ec15-ebc9-46f0-cbda-13d036b579c1"
      },
      "execution_count": 254,
      "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=VGG19_Weights.IMAGENET1K_V1`. You can also use `weights=VGG19_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "real_img = np.array(Image.open(input_list[0]))\n",
        "real_img = cv2.resize(real_img, (512, 512))\n",
        "\n",
        "real_img = change_to_torch(real_img).unsqueeze(0)\n",
        "fake_img = torch.clone(real_img)\n",
        "\n",
        "# Initialize a local discriminator for each facial component\n",
        "local_discriminators = [LocalDiscriminator().to(device) for _ in range(3)]\n",
        "\n",
        "# Initialize the FacialComponentLoss with the local discriminators\n",
        "facial_component_loss = FacialComponentLoss(local_discriminators).to(device)\n",
        "\n",
        "# Run a forward pass of the FacialComponentLoss with your example images and ROIs\n",
        "# For the purpose of the example, we will use the same images and ROIs for the real and fake inputs\n",
        "loss = facial_component_loss(real_img, fake_img)\n",
        "print(loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WsZiJwunrF-1",
        "outputId": "0437b8c9-8530-45c8-84c0-0086c00a2380"
      },
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 3, 4)\n",
            "tensor(4.1495, device='cuda:0', grad_fn=<AddBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import random\n",
        "from albumentations.core.transforms_interface import ImageOnlyTransform\n",
        "\n",
        "class JpegCompression(ImageOnlyTransform):\n",
        "    def __init__(self, quality_lower=99, quality_upper=100, always_apply=False, p=0.5):\n",
        "        super().__init__(always_apply, p)\n",
        "        self.quality_lower = quality_lower\n",
        "        self.quality_upper = quality_upper\n",
        "\n",
        "    def apply(self, img, **params):\n",
        "        # Randomly choose a quality for jpeg compression\n",
        "        quality = random.randint(self.quality_lower, self.quality_upper)\n",
        "\n",
        "        # Encode image to jpeg\n",
        "        encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]\n",
        "        result, encoded_img = cv2.imencode('.jpg', img, encode_param)\n",
        "\n",
        "        # If successful, decode the image back to numpy array\n",
        "        if result:\n",
        "            img = cv2.imdecode(encoded_img, 1)\n",
        "        return img\n",
        "\n",
        "    @classmethod\n",
        "    def get_transform_init_args_names(cls):\n",
        "        return (\"quality_lower\", \"quality_upper\", \"always_apply\", \"p\")"
      ],
      "metadata": {
        "id": "fsqWlDFyRn0r"
      },
      "execution_count": 256,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import albumentations as A\n",
        "import torch.nn.functional as F\n",
        "from albumentations.core.transforms_interface import ImageOnlyTransform\n",
        "\n",
        "class SincFilterTransform(ImageOnlyTransform):\n",
        "    def __init__(self, size=5, always_apply=True, p=1.0):\n",
        "        super().__init__(always_apply, p)\n",
        "        self.size = size\n",
        "\n",
        "    def apply(self, img, **params):\n",
        "        return self.sinc_filter(img, self.size)\n",
        "\n",
        "    @staticmethod\n",
        "    def sinc_filter(img, size):\n",
        "        class SincFilter:\n",
        "          def __init__(self, size):\n",
        "              self.size = size\n",
        "              self.filter = self.create_sinc_filter()\n",
        "\n",
        "          def create_sinc_filter(self):\n",
        "              k = torch.linspace(-1, 1, self.size)\n",
        "              x = torch.outer(torch.sinc(k), torch.sinc(k))\n",
        "              return x\n",
        "\n",
        "          def __call__(self, img):\n",
        "            img = torch.from_numpy(img)\n",
        "            if img.dim() == 2:\n",
        "              img = img.unsqueeze(-1)  # Adds a channel dimension\n",
        "            img = img.permute(2, 0, 1).unsqueeze(0).float() # CxHxW\n",
        "            sinc_filter = self.filter.unsqueeze(0).unsqueeze(0)  # 1x1xHxW\n",
        "            filtered_image = []\n",
        "            for i in range(img.shape[1]):\n",
        "              channel = img[:, i:i+1, :, :]  # 1x1xHxW\n",
        "              filtered_channel = F.conv2d(channel, sinc_filter, padding=self.size//2)\n",
        "              filtered_image.append(filtered_channel)\n",
        "            filtered_image = torch.cat(filtered_image, dim=1)  # concatenate along the channel dimension\n",
        "            return filtered_image.squeeze().permute(1, 2, 0).numpy()  # HxWxC\n",
        "\n",
        "        sinc_filter = SincFilter(size)\n",
        "        return sinc_filter(img)\n",
        "\n",
        "    @classmethod\n",
        "    def get_transform_init_args_names(cls):\n",
        "        return (\"size\", \"always_apply\", \"p\")"
      ],
      "metadata": {
        "id": "vfQAeDiqTVEF"
      },
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import albumentations as A\n",
        "from albumentations import Compose, OneOf, GaussianBlur, Downscale, GaussNoise\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "downscale_factor = 200 / 256\n",
        "\n",
        "combined_augmentations = Compose([\n",
        "    # First Order Operation\n",
        "    GaussianBlur(blur_limit=(1, 3), p=1.0), # generalized isotropic Gaussian filter\n",
        "    A.Resize(256, 256, interpolation = cv2.INTER_NEAREST, p = 1.0),\n",
        "    OneOf([\n",
        "       Downscale(scale_min=0.25, scale_max=0.5, interpolation=1, p=1.0), # downsampling - using bicubic\n",
        "       Downscale(scale_min=0.25, scale_max=0.5, interpolation=1, p=1.0)  # downsampling - using bilinear\n",
        "    ], p=1.0),\n",
        "    # A.augmentations.transforms.GaussNoise(p = 1.0), # Gaussian noise\n",
        "    #JpegCompression(quality_lower=99, quality_upper=100, p=1.0), # JPEG compression\n",
        "    # Middle Operation\n",
        "    # A.Downscale(scale_min=downscale_factor, scale_max=downscale_factor, p=1.0),\n",
        "    # A.Resize(256, 256, interpolation=cv2.INTER_NEAREST, p = 1.0),\n",
        "    # Second Order Operation\n",
        "    # GaussianBlur(blur_limit=(1, 3), p=1.0), # blur\n",
        "    #OneOf([\n",
        "    #    Downscale(scale_min=0.1, scale_max=0.25, interpolation=1, p=1.0), # downsampling - using bicubic\n",
        "    #    Downscale(scale_min=0.1, scale_max=0.25, interpolation=1, p=1.0)  # downsampling - using bilinear\n",
        "    #], p=1.0),\n",
        "    # GaussNoise(var_limit=(10.0, 75.0), mean=0, p=1.0), # noise\n",
        "    #JpegCompression(quality_lower=99, quality_upper=100, p=1.0), # JPEG compression\n",
        "    #SincFilterTransform(size=5),\n",
        "], p=1)"
      ],
      "metadata": {
        "id": "tKpfa5_GTX7o"
      },
      "execution_count": 258,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import albumentations as A\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "\n",
        "class PairedTransform:\n",
        "    def __init__(self, transform):\n",
        "        self.transform = transform\n",
        "\n",
        "    def __call__(self, hr_image, lr_image):\n",
        "        seed = random.randint(1, 10000)\n",
        "        random.seed(seed)\n",
        "        hr_transform = self.transform(image=hr_image)\n",
        "        hr_image = hr_transform['image']\n",
        "\n",
        "        random.seed(seed)\n",
        "        lr_transform = self.transform(image=lr_image)\n",
        "        lr_image = lr_transform['image']\n",
        "\n",
        "        return hr_image, lr_image\n",
        "\n",
        "transform = A.Compose([\n",
        "    A.HorizontalFlip(p=0.5),\n",
        "    # A.GaussNoise(),\n",
        "    A.ColorJitter(),\n",
        "    A.CoarseDropout(max_holes = 1, max_height=16, max_width=16, min_holes = 1, min_height=16, min_width=16, fill_value=0, p=0.5),\n",
        "    ToTensorV2()\n",
        "])\n",
        "\n",
        "\n",
        "paired_transform = PairedTransform(transform)"
      ],
      "metadata": {
        "id": "gz1bMYz5Tarw"
      },
      "execution_count": 259,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "real_img = imgs[0]\n",
        "real_img = real_img.astype(np.float32)\n",
        "augmented = combined_augmentations(image = real_img)\n",
        "\n",
        "fake_img = augmented['image']\n",
        "fake_img = fake_img / 255.0\n",
        "\n",
        "plt.imshow(fake_img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "gJhUbfghTb1w",
        "outputId": "f3b61da0-8967-429f-faa4-5d082c8cfee8"
      },
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb02c1f9cc0>"
            ]
          },
          "metadata": {},
          "execution_count": 260
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "real_img = imgs[0]\n",
        "real_img = real_img.astype(np.float32)\n",
        "\n",
        "augmented = combined_augmentations(image = real_img)\n",
        "\n",
        "fake_img = augmented['image']\n",
        "fake_img = cv2.resize(fake_img, (512, 512), interpolation = cv2.INTER_AREA)\n",
        "fake_img = fake_img.astype(np.float32)\n",
        "\n",
        "real_img = change_to_torch(real_img).unsqueeze(0)\n",
        "fake_img = change_to_torch(fake_img).unsqueeze(0)\n",
        "\n",
        "real_img, fake_img = real_img.to(device), fake_img.to(device)\n",
        "perceptual_loss = ReconstructionPerceptualLoss(vgg).to(device)\n",
        "loss = perceptual_loss(real_img, fake_img)\n",
        "print(loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xTUtrSVnxffe",
        "outputId": "b5270291-2438-49db-a285-a0a62b72e784"
      },
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0.3229, device='cuda:0', grad_fn=<AddBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "real_img = imgs[0]\n",
        "real_img = real_img.astype(np.float32)\n",
        "\n",
        "augmented = combined_augmentations(image = real_img)\n",
        "\n",
        "fake_img = augmented['image']\n",
        "fake_img = cv2.resize(fake_img, (512, 512), interpolation = cv2.INTER_AREA)\n",
        "fake_img = fake_img.astype(np.float32)"
      ],
      "metadata": {
        "id": "VzqU8fu9VF4M"
      },
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "paired_transform = PairedTransform(transform)\n",
        "\n",
        "hr_image, lr_image = paired_transform(real_img, fake_img)"
      ],
      "metadata": {
        "id": "JeTTk9NFThyc"
      },
      "execution_count": 263,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_transform = A.Compose([\n",
        "    A.HorizontalFlip(p=0.5),\n",
        "    A.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),\n",
        "    ToTensorV2()\n",
        "])"
      ],
      "metadata": {
        "id": "X-XycMNkTnIG"
      },
      "execution_count": 264,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_transform = A.Compose([\n",
        "    ToTensorV2()\n",
        "])"
      ],
      "metadata": {
        "id": "OBPlyABeTz5c"
      },
      "execution_count": 265,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from PIL import Image\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, image_filenames, image_dir, combined_augmentations, transform):\n",
        "        self.image_dir = image_dir\n",
        "        self.image_filenames = os.listdir(image_dir)\n",
        "        self.combined_augmentations = combined_augmentations\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.image_filenames)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        image_path = os.path.join(self.image_dir, self.image_filenames[idx])\n",
        "        hr_image = np.array(Image.open(image_path))\n",
        "        hr_image = hr_image.astype(np.float32)\n",
        "\n",
        "        # Create the fake image (low resolution)\n",
        "        augmented = self.combined_augmentations(image=hr_image)\n",
        "        lr_image = augmented['image']\n",
        "\n",
        "        # Normalize LR Image\n",
        "        lr_image = lr_image / 255.0\n",
        "\n",
        "        # Apply the paired transform to the high resolution and low resolution images\n",
        "        paired_transform = PairedTransform(self.transform)\n",
        "        hr_image, lr_image = paired_transform(hr_image, lr_image)\n",
        "\n",
        "        # Normalize the HR image\n",
        "        hr_image = hr_image.float() / 255\n",
        "\n",
        "        return hr_image, lr_image"
      ],
      "metadata": {
        "id": "G66SwowyTp_r"
      },
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def create_train_val_datasets(image_dir, combined_augmentations, train_transform, val_transform, val_split=0.2):\n",
        "    # List all image filenames\n",
        "    all_image_filenames = os.listdir(image_dir)\n",
        "\n",
        "    # Split into training and validation filenames\n",
        "    train_filenames, val_filenames = train_test_split(all_image_filenames, test_size=val_split, random_state=42)\n",
        "\n",
        "    # Create separate datasets for training and validation\n",
        "    train_dataset = CustomDataset(train_filenames,\n",
        "                                  image_dir,\n",
        "                                  combined_augmentations,\n",
        "                                  train_transform)\n",
        "\n",
        "    val_dataset = CustomDataset(val_filenames,\n",
        "                                image_dir,\n",
        "                                combined_augmentations,\n",
        "                                val_transform)\n",
        "\n",
        "    return train_dataset, val_dataset"
      ],
      "metadata": {
        "id": "kWCN8B0fTsQK"
      },
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset, val_dataset = create_train_val_datasets(image_dir=\"dataset\",\n",
        "                                                       combined_augmentations=combined_augmentations,\n",
        "                                                       train_transform=train_transform,\n",
        "                                                       val_transform=val_transform)\n",
        "\n",
        "# Then you can use a DataLoader to create batches and iterate over the dataset\n",
        "train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False)"
      ],
      "metadata": {
        "id": "lMIvJMgMTut_"
      },
      "execution_count": 268,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for hr, lr in train_dataloader:\n",
        "  hr = hr\n",
        "  hr = hr.to(device)\n",
        "  # Initialize a local discriminator for each facial component\n",
        "  local_discriminators = [LocalDiscriminator().to(device) for _ in range(3)]\n",
        "\n",
        "  # Initialize the FacialComponentLoss with the local discriminators\n",
        "  facial_component_loss = FacialComponentLoss(local_discriminators).to(device)\n",
        "\n",
        "  # Run a forward pass of the FacialComponentLoss with your example images and ROIs\n",
        "  # For the purpose of the example, we will use the same images and ROIs for the real and fake inputs\n",
        "  loss = facial_component_loss(hr, hr)\n",
        "  print(loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PUjZ_aMvT2u3",
        "outputId": "a0afad90-7d50-472f-c00d-d1898c4d2658"
      },
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 3, 4)\n",
            "tensor(4.1495, device='cuda:0', grad_fn=<AddBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_checkpoint(filepath, gen, disc):\n",
        "    checkpoint = torch.load(filepath)\n",
        "    gen.load_state_dict(checkpoint['gen_state_dict'])\n",
        "    disc.load_state_dict(checkpoint['disc_state_dict'])"
      ],
      "metadata": {
        "id": "LJQyAMRmUK9s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "gen = RRDBNet(63, 3, num_block = 23).to(device)\n",
        "disc = UNetDiscriminatorSN(num_in_ch = 3).to(device)"
      ],
      "metadata": {
        "id": "0H1bvg4JWYJy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir saved_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8jFTBB4cW0w",
        "outputId": "b4d2c6b5-c450-41f9-87bd-02e6d8469233"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘saved_model’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "import torch.optim as optim\n",
        "from torch.nn import BCEWithLogitsLoss\n",
        "from torch.autograd import Variable\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "save_path = '/content/results'\n",
        "\n",
        "# Initializing the losses and optimizers\n",
        "style_content_loss = StyleContentLoss()\n",
        "adversarial_loss = BCEWithLogitsLoss()\n",
        "\n",
        "optimizer_G = optim.AdamW(gen.parameters(), lr=0.0003)\n",
        "optimizer_D = optim.AdamW(disc.parameters(), lr=0.000003)\n",
        "\n",
        "epochs = 5000\n",
        "\n",
        "# Training loop\n",
        "for epoch in range(epochs):\n",
        "    print(f'Epoch: {epoch+1}/{epochs}')\n",
        "    print('-' * 10)\n",
        "\n",
        "    # Training\n",
        "    gen.train()  # Set model to training mode\n",
        "    disc.train()\n",
        "\n",
        "    running_loss = 0.0\n",
        "\n",
        "    for hr_image, lr_image in tqdm(train_dataloader):\n",
        "        # Move the data to the device\n",
        "        hr_image = hr_image.to(device)\n",
        "        lr_image = lr_image.to(device)\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer_G.zero_grad()\n",
        "        optimizer_D.zero_grad()\n",
        "        G_losses = []\n",
        "        D_losses = []\n",
        "\n",
        "        # forward\n",
        "        with torch.set_grad_enabled(True):\n",
        "            # Generate a batch of images\n",
        "            gen_imgs = gen(lr_image)\n",
        "\n",
        "            # Adversarial ground truths\n",
        "            valid = Variable(torch.Tensor(np.ones((hr_image.size(0), 1, hr_image.size(2), hr_image.size(3)))), requires_grad=False).to(device)\n",
        "            fake = Variable(torch.Tensor(np.zeros((hr_image.size(0), 1, hr_image.size(2), hr_image.size(3)))), requires_grad=False).to(device)\n",
        "\n",
        "            # Adversarial loss\n",
        "            validity_real = disc(hr_image)\n",
        "            validity_fake = disc(gen_imgs.detach())\n",
        "\n",
        "            D_real_loss = adversarial_loss(validity_real, valid)\n",
        "            D_fake_loss = adversarial_loss(validity_fake, fake)\n",
        "\n",
        "            D_loss = (D_real_loss + D_fake_loss) / 2\n",
        "\n",
        "            # Generator loss\n",
        "            G_loss_content, G_loss_style = style_content_loss(gen_imgs, hr_image)\n",
        "            G_loss_adv = adversarial_loss(disc(gen_imgs), valid)\n",
        "\n",
        "            G_loss = 0.01 * G_loss_style + G_loss_content + 0.0001 * G_loss_adv\n",
        "\n",
        "            G_loss.backward()\n",
        "            D_loss.backward()\n",
        "\n",
        "            optimizer_G.step()\n",
        "            optimizer_D.step()\n",
        "\n",
        "            # Print losses\n",
        "            G_losses.append(G_loss.item())\n",
        "            D_losses.append(D_loss.item())\n",
        "\n",
        "            # # Save losses to file\n",
        "            # with open(f\"{epoch+1}_train_loss.txt\", \"a\") as file:\n",
        "            #     file.write(f'G_loss: {G_loss.item()}, D_loss: {D_loss.item()}\\n')\n",
        "\n",
        "    # Save the generator\n",
        "    G_losses = np.mean(G_losses)\n",
        "    D_losses = np.mean(D_losses)\n",
        "    print(f'G Loss: {G_losses:.4f}, D Loss: {D_losses:.4f}')\n",
        "    torch.save(gen.state_dict(), f'saved_model/FaceRestoration.pth')\n",
        "\n",
        "    # Save the discriminator\n",
        "    torch.save(disc.state_dict(), f'saved_model/Discriminator.pth')\n",
        "\n",
        "    # after each epoch, we can save and display some results\n",
        "    if (epoch + 1) % 10 == 0:\n",
        "        # assuming hr_image, lr_image, gen_imgs are your most recent batch data\n",
        "\n",
        "        # switch the data to cpu for visualization\n",
        "        hr_image = hr_image.cpu().detach()\n",
        "        lr_image = lr_image.cpu().detach()\n",
        "        gen_imgs = gen_imgs.cpu().detach()\n",
        "\n",
        "        # get the first images from the batch\n",
        "        hr_image = hr_image[0]\n",
        "        lr_image = lr_image[0]\n",
        "        gen_image = gen_imgs[0]\n",
        "\n",
        "        # convert tensors to numpy arrays and transpose from (C, H, W) to (H, W, C)\n",
        "        hr_image_np = hr_image.permute(1, 2, 0).numpy()\n",
        "        lr_image_np = lr_image.permute(1, 2, 0).numpy()\n",
        "        gen_image_np = gen_image.permute(1, 2, 0).numpy()\n",
        "\n",
        "        # Plotting\n",
        "        fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n",
        "\n",
        "        axs[0].imshow(lr_image_np)\n",
        "        axs[0].set_title('LR Image')\n",
        "\n",
        "        axs[1].imshow(hr_image_np)\n",
        "        axs[1].set_title('HR Image')\n",
        "\n",
        "        axs[2].imshow(gen_image_np)\n",
        "        axs[2].set_title('Generated Image')\n",
        "\n",
        "        # Saving the plots\n",
        "        plt.savefig(save_path + '/output_epoch_{}.png'.format(epoch + 1))\n",
        "        plt.close()\n",
        "\n",
        "    # Validation every 5 epochs\n",
        "    # if (epoch + 1) % 5 == 0:\n",
        "    #     gen.eval()  # Set model to evaluation mode\n",
        "    #     disc.eval()\n",
        "\n",
        "    #     with torch.no_grad():\n",
        "    #         val_losses = []\n",
        "    #         for hr_image, lr_image in tqdm(val_dataloader):\n",
        "    #             hr_image = hr_image.to(device)\n",
        "    #             lr_image = lr_image.to(device)\n",
        "\n",
        "    #             # forward\n",
        "    #             gen_imgs = gen(lr_image)\n",
        "\n",
        "    #             # Generator loss\n",
        "    #             G_loss_content, G_loss_style = style_content_loss(gen_imgs, hr_image)\n",
        "    #             G_loss = 0.001 * G_loss_style + G_loss_content\n",
        "\n",
        "    #             val_losses.append(G_loss.item())\n",
        "\n",
        "    #         val_loss = np.mean(val_losses)\n",
        "    #         print(f'Validation Loss: {val_loss}')\n",
        "\n",
        "    #         # Save losses to file\n",
        "    #         with open(f\"{epoch+1}_val_loss.txt\", \"a\") as file:\n",
        "    #             file.write(f'G_loss: {G_loss.item()}\\n')\n",
        "\n",
        "    #         # Save validation loss to file\n",
        "    #         with open(f\"{epoch+1}_val_loss.txt\", \"a\") as file:\n",
        "    #             file.write(f'Validation Loss: {val_loss}\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rD9frleMWbQE",
        "outputId": "c6b26ff1-3b5e-45bf-926a-fd2d1a0ec0ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.07it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 253376240.0000, D Loss: 0.0006\n",
            "Epoch: 2/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.47it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 565538368.0000, D Loss: 0.0001\n",
            "Epoch: 3/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 75296208.0000, D Loss: 0.0002\n",
            "Epoch: 4/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 36319456.0000, D Loss: 0.0002\n",
            "Epoch: 5/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 36731268.0000, D Loss: 0.0002\n",
            "Epoch: 6/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.56it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 33582048.0000, D Loss: 0.0002\n",
            "Epoch: 7/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 80662728.0000, D Loss: 0.0001\n",
            "Epoch: 8/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 56528044.0000, D Loss: 0.0001\n",
            "Epoch: 9/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 49703600.0000, D Loss: 0.0001\n",
            "Epoch: 10/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 14575232.0000, D Loss: 0.0002\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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": [
            "Epoch: 11/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.48it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 15168484.0000, D Loss: 0.0002\n",
            "Epoch: 12/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.48it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 32940614.0000, D Loss: 0.0001\n",
            "Epoch: 13/5000\n",
            "----------\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 28264306.0000, D Loss: 0.0001\n",
            "Epoch: 14/5000\n",
            "----------\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
        },
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 26636220.0000, D Loss: 0.0001\n",
            "Epoch: 15/5000\n",
            "----------\n"
          ]
        },
        {
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            "100%|██████████| 2/2 [00:00<00:00,  5.52it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 12091131.0000, D Loss: 0.0002\n",
            "Epoch: 16/5000\n",
            "----------\n"
          ]
        },
        {
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            "100%|██████████| 2/2 [00:00<00:00,  5.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 11931098.0000, D Loss: 0.0002\n",
            "Epoch: 17/5000\n",
            "----------\n"
          ]
        },
        {
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          "name": "stderr",
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            "100%|██████████| 2/2 [00:00<00:00,  5.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 11798046.0000, D Loss: 0.0002\n",
            "Epoch: 18/5000\n",
            "----------\n"
          ]
        },
        {
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          "name": "stderr",
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            "100%|██████████| 2/2 [00:00<00:00,  5.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 11715280.0000, D Loss: 0.0002\n",
            "Epoch: 19/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "100%|██████████| 2/2 [00:00<00:00,  5.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 11771863.0000, D Loss: 0.0002\n",
            "Epoch: 20/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 21128310.0000, D Loss: 0.0001\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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": [
            "Epoch: 21/5000\n",
            "----------\n"
          ]
        },
        {
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            "100%|██████████| 2/2 [00:00<00:00,  5.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 20599070.0000, D Loss: 0.0001\n",
            "Epoch: 22/5000\n",
            "----------\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 11848323.0000, D Loss: 0.0001\n",
            "Epoch: 23/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 19319270.0000, D Loss: 0.0001\n",
            "Epoch: 24/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 18692704.0000, D Loss: 0.0001\n",
            "Epoch: 25/5000\n",
            "----------\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 12235266.0000, D Loss: 0.0001\n",
            "Epoch: 26/5000\n",
            "----------\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 12264464.0000, D Loss: 0.0001\n",
            "Epoch: 27/5000\n",
            "----------\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "G Loss: 12468963.0000, D Loss: 0.0001\n",
            "Epoch: 28/5000\n",
            "----------\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 12575099.0000, D Loss: 0.0001\n",
            "Epoch: 29/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 12697527.0000, D Loss: 0.0001\n",
            "Epoch: 30/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.55it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 12774728.0000, D Loss: 0.0001\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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": [
            "Epoch: 31/5000\n",
            "----------\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 12789124.0000, D Loss: 0.0001\n",
            "Epoch: 32/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 14663622.0000, D Loss: 0.0001\n",
            "Epoch: 33/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 13026287.0000, D Loss: 0.0001\n",
            "Epoch: 34/5000\n",
            "----------\n"
          ]
        },
        {
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        },
        {
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          "name": "stdout",
          "text": [
            "G Loss: 13139162.0000, D Loss: 0.0001\n",
            "Epoch: 35/5000\n",
            "----------\n"
          ]
        },
        {
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 13976994.0000, D Loss: 0.0001\n",
            "Epoch: 36/5000\n",
            "----------\n"
          ]
        },
        {
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          "name": "stderr",
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 13524228.0000, D Loss: 0.0001\n",
            "Epoch: 37/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 13202669.0000, D Loss: 0.0001\n",
            "Epoch: 38/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 13015789.0000, D Loss: 0.0001\n",
            "Epoch: 39/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "100%|██████████| 2/2 [00:00<00:00,  5.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 12859934.0000, D Loss: 0.0001\n",
            "Epoch: 40/5000\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G Loss: 13320683.0000, D Loss: 0.0001\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-332-6b2a2a7e87ce>\u001b[0m in \u001b[0;36m<cell line: 21>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0mD_losses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD_losses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'G Loss: {G_losses:.4f}, D Loss: {D_losses:.4f}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'saved_model/FaceRestoration.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;31m# Save the discriminator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_use_new_zipfile_serialization\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m             \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 291\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_like\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_end_of_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = imgs[0]\n",
        "img = change_to_torch(img)\n",
        "img = img.to(device)\n",
        "\n",
        "img = img.unsqueeze(0)\n",
        "\n",
        "fake = gen(img)"
      ],
      "metadata": {
        "id": "NS3FytiBWpQw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fake = fake.squeeze(0)\n",
        "fake = fake.permute(1, 2, 0)\n",
        "fake = fake.cpu().detach().numpy()\n",
        "plt.imshow(fake)"
      ],
      "metadata": {
        "id": "L1r8ms-IakhD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_QLQmQB_a4HK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}