sketch_to_face.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "sketch_to_face.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/M0hammad-Kashif/SketchToFace/blob/main/sketch_to_face.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Downloading"
      ],
      "metadata": {
        "id": "N6gCWSE53Yq8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!echo '{\"username\":\"mohammadkashif20\",\"key\":\"029e913c85378ab69c8d83b71b3f3bcc\"}' >> kaggle.json\n",
        "\n",
        "! pip install kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "!kaggle datasets download -d yewtsing/pretty-face\n",
        "\n",
        "\n",
        "import zipfile\n",
        "with zipfile.ZipFile(\"/content/pretty-face.zip\",\"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"Dataset\")\n",
        "\n",
        "\n",
        "\n",
        "!cp -R '/content/Dataset/face/face' ~+\n",
        "!cp -R '/content/Dataset/layout/layout' ~+\n",
        "!cp -R '/content/Dataset/sketch/sketch' ~+\n",
        "\n",
        "!rm -rf '/content/Dataset'\n",
        "!rm -rf '/content/sample_data'\n",
        "!rm  '/content/pretty-face.zip'\n",
        "!rm '/content/kaggle.json'"
      ],
      "metadata": {
        "id": "oyFX2sPQ_1tx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50ea3bc5-c1c1-4747-875e-6cb00e579f35"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Downloading pretty-face.zip to /content\n",
            " 98% 1.17G/1.19G [00:06<00:00, 208MB/s]\n",
            "100% 1.19G/1.19G [00:06<00:00, 183MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OjdiaTB1DdVW",
        "outputId": "eb8ca833-972a-4869-bd4c-de9cb4cdbc66"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully installed GitPython-3.1.27 docker-pycreds-0.4.0 gitdb-4.0.9 pathtools-0.1.2 sentry-sdk-1.9.0 setproctitle-1.3.2 shortuuid-1.0.9 smmap-5.0.0 wandb-0.13.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
        "id": "K_HbR7Wv3bjy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy.random import randint\n",
        "from PIL import Image\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "import torch\n",
        "from tqdm import tqdm\n",
        "import wandb\n",
        "import time\n",
        "import gc\n",
        "from torchvision.utils import make_grid"
      ],
      "metadata": {
        "id": "g47MIgMtAW6q"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Configurations"
      ],
      "metadata": {
        "id": "pQ5B7CRT32Mt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CFG:\n",
        "    DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "    LR = 1e-3\n",
        "    BATCH_SIZE = 128\n",
        "    IMG_CHANNEL = 3\n",
        "    L1_LAMBDA = 100\n",
        "    LAMBDA_GP = 10\n",
        "    EPOCHS = 500\n",
        "    IMG_SIZE = 256"
      ],
      "metadata": {
        "id": "TcpogU7T34Kw"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataloader and Transformations"
      ],
      "metadata": {
        "id": "Z5WLe8DX3dOV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "face_path = '/content/face'\n",
        "sketch_path = '/content/sketch'\n",
        "\n",
        "face_file = os.listdir(face_path)\n",
        "sketch_file = os.listdir(sketch_path)\n",
        "print(len(face_file))\n",
        "print(len(sketch_file))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRxJkbVXAl_h",
        "outputId": "c2ef1e88-4670-48f4-f61c-a22320d285c4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3318\n",
            "3318\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ix = randint(0,len(face_file))\n",
        "file_name = face_file[ix]\n",
        "face_img = Image.open(os.path.join(face_path, file_name))\n",
        "face_img = np.array(face_img)\n",
        "print(face_img.shape)\n",
        "img_no = file_name.split('.')[0]\n",
        "sketch_img = Image.open(os.path.join(sketch_path, img_no+'_edges.jpg'))\n",
        "sketch_img = np.array(sketch_img)\n",
        "print(sketch_img.shape)\n",
        "plt.subplot(1,2,1)\n",
        "plt.imshow(face_img)\n",
        "plt.subplot(1,2,2)\n",
        "plt.imshow(sketch_img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "y8M-uWOyBj2X",
        "outputId": "f740e217-5397-49b0-bf4b-7563e126577e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(512, 512, 3)\n",
            "(512, 512, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f05aeab14d0>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "from torch.utils.data import Dataset, dataloader\n",
        "from torch.utils.data import DataLoader"
      ],
      "metadata": {
        "id": "s6CWk_s-B5O_"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IMAGE_SIZE = 256\n",
        "IMG_CHANNELS = 3\n",
        "BATCH_SIZE = 64"
      ],
      "metadata": {
        "id": "8Xkf9jQCEoJp"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import albumentations as A\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "\n",
        "transforms = A.Compose(\n",
        "    [   \n",
        "        A.Resize(CFG.IMG_SIZE, CFG.IMG_SIZE),\n",
        "        A.Normalize(\n",
        "         [0.5 for _ in range(CFG.IMG_CHANNEL)], [0.5 for _ in range(CFG.IMG_CHANNEL)]\n",
        "        ),\n",
        "        ToTensorV2(),\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "KgoShIixElrj"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def img_arr(path, file):\n",
        "  img = Image.open(os.path.join(path, file))\n",
        "  img = np.array(img)\n",
        "\n",
        "  return img"
      ],
      "metadata": {
        "id": "Wj8q0T30F4Vh"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomImageDataset(Dataset):\n",
        "  def __init__(self, face_dir, sketch_dir, transform=None):\n",
        "    self.face_dir = face_dir\n",
        "    self.sketch_dir = sketch_dir\n",
        "    self.face_files = os.listdir(self.face_dir)\n",
        "    self.sketch_files = os.listdir(self.sketch_dir)\n",
        "    self.transform = transform\n",
        "\n",
        "  def __len__(self):\n",
        "     return len(self.face_files)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    face_file = self.face_files[idx]\n",
        "    face_img = img_arr(self.face_dir, face_file)\n",
        "    img_no = face_file.split('.')[0]\n",
        "    sketch_img = img_arr(self.sketch_dir, img_no+'_edges.jpg')\n",
        "\n",
        "    if self.transform:\n",
        "      face_img = self.transform(image=face_img)['image']\n",
        "      sketch_img = self.transform(image=sketch_img)['image']\n",
        "\n",
        "    return sketch_img, face_img  \n",
        "\n"
      ],
      "metadata": {
        "id": "jNAkaEpMCzNp"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = CustomImageDataset(face_path, sketch_path,transforms)\n",
        "dataloader = DataLoader(dataset, batch_size = CFG.BATCH_SIZE)"
      ],
      "metadata": {
        "id": "K0ePFDt_GiBQ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for idx , (sketch_img, face_img ) in enumerate(dataloader):\n",
        "  print(sketch_img.shape)\n",
        "  print(face_img.shape)\n",
        "  plt.subplot(1,2,1)\n",
        "  plt.imshow(sketch_img[4].permute(1,2,0))\n",
        "  plt.axis(\"off\")\n",
        "  plt.subplot(1,2,2)\n",
        "  plt.imshow(face_img[4].permute(1,2,0))\n",
        "  plt.axis(\"off\")\n",
        "  break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "zxXgesJQG5wS",
        "outputId": "60ad7091-0284-474c-c7a9-23c06869a50e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "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",
            "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": [
            "torch.Size([128, 3, 256, 256])\n",
            "torch.Size([128, 3, 256, 256])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generator"
      ],
      "metadata": {
        "id": "VRb-fCfH3kL3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "\n",
        "class Block(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, down=True, act=\"relu\", use_dropout=False):\n",
        "        super(Block, self).__init__()\n",
        "        self.conv = nn.Sequential(\n",
        "            nn.Conv2d(in_channels, out_channels, 4, 2, 1, bias=False, padding_mode=\"reflect\")\n",
        "            if down\n",
        "            else nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1, bias=False),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU() if act == \"relu\" else nn.LeakyReLU(0.2),\n",
        "        )\n",
        "\n",
        "        self.use_dropout = use_dropout\n",
        "        self.dropout = nn.Dropout(0.5)\n",
        "        self.down = down\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv(x)\n",
        "        return self.dropout(x) if self.use_dropout else x\n",
        "\n",
        "\n",
        "class Generator(nn.Module):\n",
        "    def __init__(self, in_channels=3, features=64):\n",
        "        super().__init__()\n",
        "        self.initial_down = nn.Sequential(\n",
        "            nn.Conv2d(in_channels, features, 4, 2, 1, padding_mode=\"reflect\"),\n",
        "            nn.LeakyReLU(0.2),\n",
        "        )\n",
        "        self.down1 = Block(features, features * 2, down=True, act=\"leaky\", use_dropout=False)\n",
        "        self.down2 = Block(\n",
        "            features * 2, features * 4, down=True, act=\"leaky\", use_dropout=False\n",
        "        )\n",
        "        self.down3 = Block(\n",
        "            features * 4, features * 8, down=True, act=\"leaky\", use_dropout=False\n",
        "        )\n",
        "        self.down4 = Block(\n",
        "            features * 8, features * 8, down=True, act=\"leaky\", use_dropout=False\n",
        "        )\n",
        "        self.down5 = Block(\n",
        "            features * 8, features * 8, down=True, act=\"leaky\", use_dropout=False\n",
        "        )\n",
        "        self.down6 = Block(\n",
        "            features * 8, features * 8, down=True, act=\"leaky\", use_dropout=False\n",
        "        )\n",
        "        self.bottleneck = nn.Sequential(\n",
        "            nn.Conv2d(features * 8, features * 8, 4, 2, 1), nn.ReLU()\n",
        "        )\n",
        "\n",
        "        self.up1 = Block(features * 8, features * 8, down=False, act=\"relu\", use_dropout=True)\n",
        "        self.up2 = Block(\n",
        "            features * 8 * 2, features * 8, down=False, act=\"relu\", use_dropout=True\n",
        "        )\n",
        "        self.up3 = Block(\n",
        "            features * 8 * 2, features * 8, down=False, act=\"relu\", use_dropout=True\n",
        "        )\n",
        "        self.up4 = Block(\n",
        "            features * 8 * 2, features * 8, down=False, act=\"relu\", use_dropout=False\n",
        "        )\n",
        "        self.up5 = Block(\n",
        "            features * 8 * 2, features * 4, down=False, act=\"relu\", use_dropout=False\n",
        "        )\n",
        "        self.up6 = Block(\n",
        "            features * 4 * 2, features * 2, down=False, act=\"relu\", use_dropout=False\n",
        "        )\n",
        "        self.up7 = Block(features * 2 * 2, features, down=False, act=\"relu\", use_dropout=False)\n",
        "        self.final_up = nn.Sequential(\n",
        "            nn.ConvTranspose2d(features * 2, in_channels, kernel_size=4, stride=2, padding=1),\n",
        "            nn.Tanh(),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        d1 = self.initial_down(x)\n",
        "        d2 = self.down1(d1)\n",
        "        d3 = self.down2(d2)\n",
        "        d4 = self.down3(d3)\n",
        "        d5 = self.down4(d4)\n",
        "        d6 = self.down5(d5)\n",
        "        d7 = self.down6(d6)\n",
        "        bottleneck = self.bottleneck(d7)\n",
        "        up1 = self.up1(bottleneck)\n",
        "        up2 = self.up2(torch.cat([up1, d7], 1))\n",
        "        up3 = self.up3(torch.cat([up2, d6], 1))\n",
        "        up4 = self.up4(torch.cat([up3, d5], 1))\n",
        "        up5 = self.up5(torch.cat([up4, d4], 1))\n",
        "        up6 = self.up6(torch.cat([up5, d3], 1))\n",
        "        up7 = self.up7(torch.cat([up6, d2], 1))\n",
        "        return self.final_up(torch.cat([up7, d1], 1))\n",
        "\n"
      ],
      "metadata": {
        "id": "eFo6K0LuHNLK"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Discriminator"
      ],
      "metadata": {
        "id": "Wjhv0swT4l9K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "\n",
        "class CNNBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, stride):\n",
        "        super(CNNBlock, self).__init__()\n",
        "        self.conv = nn.Sequential(\n",
        "            nn.Conv2d(\n",
        "                in_channels, out_channels, 4, stride, 1, bias=False, padding_mode=\"reflect\"\n",
        "            ),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.LeakyReLU(0.2),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.conv(x)\n",
        "\n",
        "\n",
        "class Discriminator(nn.Module):\n",
        "    def __init__(self, in_channels=3, features=[64, 128, 256, 512]):\n",
        "        super().__init__()\n",
        "        self.initial = nn.Sequential(\n",
        "            nn.Conv2d(\n",
        "                in_channels * 2,\n",
        "                features[0],\n",
        "                kernel_size=4,\n",
        "                stride=2,\n",
        "                padding=1,\n",
        "                padding_mode=\"reflect\",\n",
        "            ),\n",
        "            nn.LeakyReLU(0.2),\n",
        "        )\n",
        "\n",
        "        layers = []\n",
        "        in_channels = features[0]\n",
        "        for feature in features[1:]:\n",
        "            layers.append(\n",
        "                CNNBlock(in_channels, feature, stride=1 if feature == features[-1] else 2),\n",
        "            )\n",
        "            in_channels = feature\n",
        "\n",
        "        layers.append(\n",
        "            nn.Conv2d(\n",
        "                in_channels, 1, kernel_size=4, stride=1, padding=1, padding_mode=\"reflect\"\n",
        "            ),\n",
        "        )\n",
        "\n",
        "        self.model = nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x, y):\n",
        "        x = torch.cat([x, y], dim=1)\n",
        "        x = self.initial(x)\n",
        "        x = self.model(x)\n",
        "        return x\n"
      ],
      "metadata": {
        "id": "skXJVEny4lSm"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loss and Optimizers"
      ],
      "metadata": {
        "id": "ev_0BQOg4wwQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "disc = Discriminator(in_channels=CFG.IMG_CHANNEL).to(CFG.DEVICE).to(CFG.DEVICE)\n",
        "gen = Generator(in_channels=CFG.IMG_CHANNEL, features=64).to(CFG.DEVICE).to(CFG.DEVICE)\n",
        "\n",
        "opt_disc = optim.Adam(disc.parameters(), lr=CFG.LR, betas=(0.5, 0.999))\n",
        "opt_gen = optim.Adam(gen.parameters(), lr=CFG.LR, betas=(0.5, 0.999))\n",
        "\n",
        "\n",
        "BCE = nn.BCEWithLogitsLoss()\n",
        "L1_LOSS = nn.L1Loss()"
      ],
      "metadata": {
        "id": "xg_VJhXn4wbt"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training part"
      ],
      "metadata": {
        "id": "xCUrKWj45mOz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_one_epoch(gen,disc,loader, opt_disc, opt_gen, l1_loss, bce, g_scaler, d_scaler):\n",
        "\n",
        "  pBar = tqdm(enumerate(loader),total=len(loader))\n",
        "\n",
        "  for idx, (sketch, image) in pBar:\n",
        "\n",
        "    sketch = sketch.to(CFG.DEVICE)\n",
        "    image = image.to(CFG.DEVICE)  \n",
        "\n",
        "    # training Discriminator\n",
        "    with torch.cuda.amp.autocast():\n",
        "      image_fake = gen(sketch)\n",
        "\n",
        "      D_real = disc(sketch,image)\n",
        "      D_real_loss = BCE(D_real,torch.ones_like(D_real))\n",
        "\n",
        "      D_fake = disc(sketch , image_fake.detach())\n",
        "      D_fake_loss = BCE(D_fake,torch.zeros_like(D_fake))\n",
        "\n",
        "      D_loss = D_fake_loss + D_real_loss\n",
        "\n",
        "    disc.zero_grad()\n",
        "    d_scaler.scale(D_loss).backward()\n",
        "    d_scaler.step(opt_disc)\n",
        "    d_scaler.update()\n",
        "\n",
        "    # Training Generator\n",
        "    with torch.cuda.amp.autocast():\n",
        "      D_fake = disc(sketch,image_fake)\n",
        "      G_fake_loss = BCE(D_fake,torch.ones_like(D_fake))\n",
        "      L1 = L1_LOSS(image_fake,image)*CFG.L1_LAMBDA\n",
        "      G_loss = G_fake_loss + L1\n",
        "\n",
        "    opt_gen.zero_grad()\n",
        "    g_scaler.scale(G_loss).backward()\n",
        "    g_scaler.step(opt_gen)\n",
        "    g_scaler.update()\n",
        "\n",
        "    if idx % 10 == 0:      \n",
        "      grid1 = make_grid(sketch[:4])\n",
        "      grid2 = make_grid(image_fake[:4])\n",
        "      \n",
        "      grid1 = wandb.Image(grid1, caption=\"Sketch\")\n",
        "      grid2 = wandb.Image(grid2, caption=\"Produced Image\")\n",
        "      \n",
        "      wandb.log({\"Sketch\": grid1})\n",
        "      wandb.log({\"Produced Image\": grid2})\n",
        "      \n",
        "  \n",
        "    mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0\n",
        "    pBar.set_postfix(G_loss=f'{G_loss.item():0.4f}',\n",
        "                       D_loss=f'{D_loss.item():0.4f}',\n",
        "                      gpu_mem=f'{mem:0.2f} GB')\n",
        "    torch.cuda.empty_cache()\n",
        "    gc.collect()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "xcNnNtln4rLG"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(CFG=CFG,loader=dataloader):\n",
        "\n",
        "  wandb.init(\"Sketch2Face\")\n",
        "  g_scaler = torch.cuda.amp.GradScaler()\n",
        "  d_scaler = torch.cuda.amp.GradScaler()\n",
        "\n",
        "  if torch.cuda.is_available():\n",
        "    print(\"cuda: {}\\n\".format(torch.cuda.get_device_name()))\n",
        "\n",
        "  start = time.time()\n",
        "\n",
        "  for epoch in range(CFG.EPOCHS):\n",
        "\n",
        "    print(f\"Epoch {epoch + 1}\")\n",
        "\n",
        "    train_one_epoch(gen,disc,dataloader,opt_disc,opt_gen,L1_LOSS,BCE,g_scaler,d_scaler)\n",
        "    print(\"-\"*100)\n",
        "    print(\"\\n\")\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DWa0NpXFAZLO"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Pcc2XTStFJ6g",
        "outputId": "5a67ea18-098a-491b-b950-2bef4cfc93f4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.13.1"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20220813_141506-hok344bh</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href=\"https://wandb.ai/mkashif/uncategorized/runs/hok344bh\" target=\"_blank\">woven-flower-32</a></strong> to <a href=\"https://wandb.ai/mkashif/uncategorized\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cuda: Tesla P100-PCIE-16GB\n",
            "\n",
            "Epoch 1\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [02:09<00:00,  4.99s/it, D_loss=1.3660, G_loss=40.2653, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 2\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [02:02<00:00,  4.70s/it, D_loss=1.4343, G_loss=33.2661, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 3\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [02:00<00:00,  4.64s/it, D_loss=1.3252, G_loss=31.0083, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 4\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [02:00<00:00,  4.63s/it, D_loss=1.3606, G_loss=26.0745, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 5\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [02:00<00:00,  4.63s/it, D_loss=1.2896, G_loss=24.9468, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 6\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [02:00<00:00,  4.65s/it, D_loss=1.3751, G_loss=22.9105, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 7\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.61s/it, D_loss=1.5006, G_loss=23.3051, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 8\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [02:00<00:00,  4.63s/it, D_loss=1.2891, G_loss=22.4296, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 9\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [02:00<00:00,  4.62s/it, D_loss=1.3033, G_loss=20.3229, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 10\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.61s/it, D_loss=1.3557, G_loss=20.8438, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 11\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [02:00<00:00,  4.62s/it, D_loss=1.3779, G_loss=20.1380, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 12\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [02:00<00:00,  4.63s/it, D_loss=1.3488, G_loss=19.3234, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 13\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.61s/it, D_loss=1.4477, G_loss=18.8170, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 14\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.59s/it, D_loss=1.1691, G_loss=18.0313, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 15\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:58<00:00,  4.56s/it, D_loss=2.1234, G_loss=18.7077, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 16\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.58s/it, D_loss=1.3336, G_loss=18.3108, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 17\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:58<00:00,  4.57s/it, D_loss=1.2683, G_loss=17.5839, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 18\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.58s/it, D_loss=1.2481, G_loss=17.1571, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 19\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:58<00:00,  4.57s/it, D_loss=1.4803, G_loss=16.6106, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 20\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.60s/it, D_loss=1.4498, G_loss=16.2262, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 21\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.61s/it, D_loss=1.2385, G_loss=16.1480, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 22\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.59s/it, D_loss=1.2031, G_loss=15.5918, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 23\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.60s/it, D_loss=1.2737, G_loss=15.6566, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 24\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.58s/it, D_loss=1.2863, G_loss=15.0688, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 25\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:59<00:00,  4.58s/it, D_loss=1.2628, G_loss=15.4140, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 26\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26/26 [01:58<00:00,  4.57s/it, D_loss=1.2369, G_loss=14.9079, gpu_mem=12.41 GB]\n"
          ]
        },
        {
          "metadata": {
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 117\n"
          ]
        },
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            "100%|██████████| 26/26 [01:58<00:00,  4.57s/it, D_loss=1.3403, G_loss=8.5912, gpu_mem=12.41 GB]\n"
          ]
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 118\n"
          ]
        },
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          "name": "stderr",
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            "100%|██████████| 26/26 [01:59<00:00,  4.58s/it, D_loss=1.3285, G_loss=8.6874, gpu_mem=12.41 GB]\n"
          ]
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 119\n"
          ]
        },
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            "100%|██████████| 26/26 [01:59<00:00,  4.59s/it, D_loss=1.0381, G_loss=8.9144, gpu_mem=12.41 GB]\n"
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 120\n"
          ]
        },
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            "100%|██████████| 26/26 [01:58<00:00,  4.57s/it, D_loss=4.6800, G_loss=12.0588, gpu_mem=12.41 GB]\n"
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 121\n"
          ]
        },
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            "100%|██████████| 26/26 [01:58<00:00,  4.57s/it, D_loss=1.2843, G_loss=8.2085, gpu_mem=12.41 GB]\n"
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 122\n"
          ]
        },
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            "100%|██████████| 26/26 [01:58<00:00,  4.57s/it, D_loss=1.3234, G_loss=8.3345, gpu_mem=12.41 GB]\n"
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 123\n"
          ]
        },
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            "100%|██████████| 26/26 [01:59<00:00,  4.60s/it, D_loss=1.3177, G_loss=8.0565, gpu_mem=12.41 GB]\n"
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 124\n"
          ]
        },
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 125\n"
          ]
        },
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          "name": "stderr",
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 126\n"
          ]
        },
        {
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          "name": "stderr",
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 127\n"
          ]
        },
        {
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          "name": "stderr",
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 128\n"
          ]
        },
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          "name": "stderr",
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 129\n"
          ]
        },
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          "name": "stderr",
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 130\n"
          ]
        },
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 131\n"
          ]
        },
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          "name": "stderr",
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 132\n"
          ]
        },
        {
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          "name": "stderr",
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        {
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 133\n"
          ]
        },
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 134\n"
          ]
        },
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          "name": "stderr",
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 135\n"
          ]
        },
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          "name": "stderr",
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 136\n"
          ]
        },
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 137\n"
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        },
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          "name": "stderr",
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 138\n"
          ]
        },
        {
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          "name": "stderr",
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          "name": "stdout",
          "text": [
            "----------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "Epoch 139\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            " 54%|█████▍    | 14/26 [01:06<00:56,  4.72s/it, D_loss=0.4457, G_loss=9.5101, gpu_mem=13.29 GB]\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-19-2da0ffaf5447>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\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[0;32m<ipython-input-18-4fe30c72c629>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(CFG, loader)\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Epoch {epoch + 1}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mtrain_one_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdisc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdataloader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mopt_disc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mopt_gen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mL1_LOSS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mBCE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mg_scaler\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0md_scaler\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     17\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-\"\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-17-9e548aceb791>\u001b[0m in \u001b[0;36mtrain_one_epoch\u001b[0;34m(gen, disc, loader, opt_disc, opt_gen, l1_loss, bce, g_scaler, d_scaler)\u001b[0m\n\u001b[1;32m      3\u001b[0m   \u001b[0mpBar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\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[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msketch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpBar\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      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0msketch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msketch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCFG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1194\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1195\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\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   1196\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1197\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    679\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\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    682\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    719\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[1;32m    720\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 721\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    722\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    723\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\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[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\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[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-11-3ab841692282>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m       \u001b[0mface_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mface_img\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'image'\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     20\u001b[0m       \u001b[0msketch_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msketch_img\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'image'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/albumentations/core/composition.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, force_apply, *args, **data)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransforms\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[0;32m--> 205\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mforce_apply\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_apply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdata\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    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcheck_each_transform\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/albumentations/core/transforms_interface.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, force_apply, *args, **kwargs)\u001b[0m\n\u001b[1;32m     96\u001b[0m                     )\n\u001b[1;32m     97\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_with_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/albumentations/core/transforms_interface.py\u001b[0m in \u001b[0;36mapply_with_params\u001b[0;34m(self, params, **kwargs)\u001b[0m\n\u001b[1;32m    109\u001b[0m                 \u001b[0mtarget_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_target_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m                 \u001b[0mtarget_dependencies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_dependence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\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[0;32m--> 111\u001b[0;31m                 \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtarget_dependencies\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    112\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m                 \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/albumentations/augmentations/transforms.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, image, **params)\u001b[0m\n\u001b[1;32m    678\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\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[0;32m--> 680\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_pixel_value\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    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_transform_init_args_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;32m/usr/local/lib/python3.7/dist-packages/albumentations/augmentations/functional.py\u001b[0m in \u001b[0;36mnormalize\u001b[0;34m(img, mean, std, max_pixel_value)\u001b[0m\n\u001b[1;32m    256\u001b[0m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m     \u001b[0mimg\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mdenominator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "joDPcm_dGtCB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}