GlassesRemovalNetwork.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 2,
  "metadata": {
    "colab": {
      "name": "GlassesRemovalNetwork.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNbSHsuB3OGOFqwOCcN8CBd",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"https://colab.research.google.com/github/agg-shambhavi/Glasses-Removal-Network/blob/main/GlassesRemovalNetwork.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ],
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cmaAqcpVEgg",
        "outputId": "3447306f-6625-4753-e510-9fd30f947dcc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Constants "
      ],
      "metadata": {
        "id": "gqdtrCTAXLCm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import torch\r\n",
        "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\n",
        "BATCH_SIZE = 1\r\n",
        "LEARNING_RATE = 2e-4\r\n",
        "NUM_EPOCHS = 2\r\n",
        "LAMBDA_IDENTITY = 0.0\r\n",
        "LAMBDA_CYCLE = 10\r\n",
        "# Korean paper trained for 200 epochs"
      ],
      "outputs": [],
      "metadata": {
        "id": "ZqOKjlG-XIE1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# File Paths"
      ],
      "metadata": {
        "id": "EUzv7cBk7sBx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import zipfile\r\n",
        "import os"
      ],
      "outputs": [],
      "metadata": {
        "id": "BbrXEkZD8a1D"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "def extract_zip_file(path):\r\n",
        "  # extract all contents of zip file\r\n",
        "  zip_train = zipfile.ZipFile(path, 'r')\r\n",
        "  zip_train.extractall(\"./\")\r\n",
        "  zip_train.close()"
      ],
      "outputs": [],
      "metadata": {
        "id": "pdY1ttvDiP_c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "trainGlassesZip  = \"/content/drive/MyDrive/Computer Vision /Cyclegan/Data/train glasses.zip\"\r\n",
        "trainNoglassesZip = \"/content/drive/MyDrive/Computer Vision /Cyclegan/Data/train non glasses.zip\"\r\n",
        "valGlassesZip  = \"/content/drive/MyDrive/Computer Vision /Cyclegan/Data/val glasses.zip\"\r\n",
        "valNoglassesZip = \"/content/drive/MyDrive/Computer Vision /Cyclegan/Data/val non glasses.zip\""
      ],
      "outputs": [],
      "metadata": {
        "id": "G7_zABZthBJY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "extract_zip_file(trainGlassesZip)\r\n",
        "extract_zip_file(trainNoglassesZip)\r\n",
        "extract_zip_file(valGlassesZip)\r\n",
        "extract_zip_file(valNoglassesZip)"
      ],
      "outputs": [],
      "metadata": {
        "id": "EJp5LmlhiUos"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "trainGlassesDir = \"/content/train glasses\"\r\n",
        "trainNoglassesDir = \"/content/train non glasses\"\r\n",
        "valGlassesDir = \"/content/val glasses\"\r\n",
        "valNoglassesDir = \"/content/val non glasses\""
      ],
      "outputs": [],
      "metadata": {
        "id": "qoaA5HgS8krj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "resultsNoGlasses = \"/content/drive/MyDrive/Computer Vision /Cyclegan/Results - G -> N\"\r\n",
        "resultGlasses = \"/content/drive/MyDrive/Computer Vision /Cyclegan/Results N -> G \""
      ],
      "outputs": [],
      "metadata": {
        "id": "fCUImld1Fvym"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
        "id": "94uxbuA5XBTV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "!pip install -U git+https://github.com/albu/albumentations > /dev/null "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Running command git clone -q https://github.com/albu/albumentations /tmp/pip-req-build-pajr_r11\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kREi2vocg3B1",
        "outputId": "d7dd50cf-89b5-4313-bab6-8c4ace42b535"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import torch.nn as nn\r\n",
        "from torch.utils.data import DataLoader\r\n",
        "import torch.optim as optim\r\n",
        "from tqdm import tqdm\r\n",
        "from PIL import Image, ImageOps\r\n",
        "import numpy as np\r\n",
        "from torch.utils.data import Dataset\r\n",
        "from torchvision.utils import save_image\r\n",
        "import torchvision.models.vgg as models\r\n",
        "import albumentations as A\r\n",
        "from albumentations.pytorch import ToTensorV2\r\n",
        "import time"
      ],
      "outputs": [],
      "metadata": {
        "id": "mofkH1iNWGTl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset Class & Transforms"
      ],
      "metadata": {
        "id": "ELkrRs0Xd2QR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "class GlassesDataset(Dataset):\r\n",
        "    def __init__(self, root_dir_glass, root_dir_noglass, transform=None):\r\n",
        "        super().__init__()\r\n",
        "        self.root_dir_glasses = root_dir_glass\r\n",
        "        self.root_dir_noglasses = root_dir_noglass\r\n",
        "        self.list_glasses = os.listdir(self.root_dir_glasses)\r\n",
        "        self.list_noglasses = os.listdir(self.root_dir_noglasses)\r\n",
        "        self.transform = transform\r\n",
        "        self.len_glasses = len(self.list_glasses)\r\n",
        "        self.len_noglasses = len(self.list_noglasses)\r\n",
        "        self.dataset_length = max(self.len_glasses, self.len_noglasses)\r\n",
        "        \r\n",
        "    def __len__(self):\r\n",
        "        return self.dataset_length\r\n",
        "    \r\n",
        "    def __getitem__(self, index):\r\n",
        "        start_time = time.time()\r\n",
        "        glasses_file = self.list_glasses[index % self.len_glasses]\r\n",
        "        no_glasses_file = self.list_noglasses[index % self.len_noglasses]\r\n",
        "        glasses_path = os.path.join(self.root_dir_glasses, glasses_file)\r\n",
        "        no_glasses_path = os.path.join(self.root_dir_noglasses, no_glasses_file)\r\n",
        "        img_glasses = np.array(ImageOps.grayscale(Image.open(glasses_path).convert('RGB')))\r\n",
        "        img_noglasses = np.array(ImageOps.grayscale(Image.open(no_glasses_path).convert('RGB')))\r\n",
        "        \r\n",
        "        if self.transform:\r\n",
        "            augmentation = self.transform(image=img_glasses, image0=img_noglasses)\r\n",
        "            img_glasses = augmentation[\"image\"]\r\n",
        "            img_noglasses = augmentation[\"image0\"]\r\n",
        "\r\n",
        "        end_time = time.time()\r\n",
        "        print(f\"{end_time - start_time} data loading time\")\r\n",
        "            \r\n",
        "        return img_glasses, img_noglasses"
      ],
      "outputs": [],
      "metadata": {
        "id": "Qq6HKnqyd13e"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "transforms_train = A.Compose(\r\n",
        "    [\r\n",
        "        A.Resize(width=256, height=256),\r\n",
        "        A.HorizontalFlip(p=0.5),\r\n",
        "        A.Normalize(mean=[0.5], std=[0.5], max_pixel_value=255),\r\n",
        "        ToTensorV2(),\r\n",
        "     ],\r\n",
        "    additional_targets={\"image0\": \"image\"},\r\n",
        ")"
      ],
      "outputs": [],
      "metadata": {
        "id": "3Kblie8fiUWe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "transforms_val = A.Compose(\r\n",
        "    [\r\n",
        "        A.Resize(width=256, height=256),\r\n",
        "        A.Normalize(mean=[0.5], std=[0.5], max_pixel_value=255),\r\n",
        "        ToTensorV2(),\r\n",
        "     ],\r\n",
        "    additional_targets={\"image0\": \"image\"},\r\n",
        ")"
      ],
      "outputs": [],
      "metadata": {
        "id": "ATkRsGOB6CAF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Architecture \r\n",
        "## Generator"
      ],
      "metadata": {
        "id": "Ayn6B4I7eWD7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "class SelfAttentionBlock(nn.Module):\r\n",
        "    \"\"\"Self Attention Layer/Block\"\"\"\r\n",
        "\r\n",
        "    def __init__(self, in_dim):\r\n",
        "        super().__init__()\r\n",
        "        self.in_channel = in_dim\r\n",
        "        self.query_conv = nn.Conv2d(\r\n",
        "            in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1\r\n",
        "        )\r\n",
        "        self.key_conv = nn.Conv2d(\r\n",
        "            in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1\r\n",
        "        )\r\n",
        "        self.value_conv = nn.Conv2d(\r\n",
        "            in_channels=in_dim, out_channels=in_dim, kernel_size=1\r\n",
        "        )\r\n",
        "        self.gamma = nn.Parameter(torch.zeros(1))\r\n",
        "        self.softmax = nn.Softmax(dim=-1)\r\n",
        "\r\n",
        "    def forward(self, x):\r\n",
        "        \"\"\"\r\n",
        "        inputs:\r\n",
        "            x : input feature maps (B X C X W X H)\r\n",
        "        returns:\r\n",
        "            out : self attention value + input features\r\n",
        "            attention: B x N X N (N is Width*Height)\r\n",
        "        \"\"\"\r\n",
        "\r\n",
        "        batch_size, c, h, w = x.shape\r\n",
        "        proj_query = self.query_conv(x).view(batch_size, -1, w * h).permute(0, 2, 1)\r\n",
        "        # proj_query with view is of shape (batch_size, c, w*h)\r\n",
        "        # proj_query with permute becomes batch_size, w*h, c), ie transpose of proj_query for matrix multiplication\r\n",
        "        proj_key = self.key_conv(x).view(batch_size, -1, w * h)  # (batch_size, c, w*h)\r\n",
        "\r\n",
        "        energy = torch.bmm(proj_query, proj_key)  # (batch_size, w*h, w*h)\r\n",
        "        attention = self.softmax(energy)  # (batch_size, w*h, w*h)\r\n",
        "\r\n",
        "        proj_value = self.value_conv(x).view(\r\n",
        "            batch_size, -1, w * h\r\n",
        "        )  # (batch_size, c*8, w*h) c*8 = C\r\n",
        "\r\n",
        "        out = torch.bmm(\r\n",
        "            proj_value, attention.permute(0, 2, 1)\r\n",
        "        )  # (batch_size, c*8, w*h)\r\n",
        "        # attention is transposed for matrix mul\r\n",
        "        out = out.view(batch_size, c, w, h)\r\n",
        "\r\n",
        "        out = self.gamma * out + x\r\n",
        "        return out, attention\r\n",
        "\r\n",
        "\r\n",
        "class ResidualBlock(nn.Module):\r\n",
        "    def __init__(self, channels):\r\n",
        "        super().__init__()\r\n",
        "        self.block = nn.Sequential(\r\n",
        "            # I am not sure which res block to take\r\n",
        "            nn.Conv2d(\r\n",
        "                in_channels=channels,\r\n",
        "                out_channels=channels,\r\n",
        "                kernel_size=1,\r\n",
        "                stride=1,\r\n",
        "                padding=0,\r\n",
        "            )\r\n",
        "            # nn.Conv2d(\r\n",
        "            #     in_channels=channels,\r\n",
        "            #     out_channels=channels,\r\n",
        "            #     kernel_size=7,\r\n",
        "            #     stride=1,\r\n",
        "            #     padding=3,\r\n",
        "            # )\r\n",
        "        )\r\n",
        "\r\n",
        "    def forward(self, x):\r\n",
        "        return x + self.block(x)\r\n",
        "\r\n",
        "\r\n",
        "class Generator(nn.Module):\r\n",
        "    def __init__(self, img_channels):\r\n",
        "        super().__init__()\r\n",
        "\r\n",
        "        self.downConvBlock = nn.Sequential(\r\n",
        "            nn.Conv2d(\r\n",
        "                in_channels=img_channels,\r\n",
        "                out_channels=1,\r\n",
        "                kernel_size=7,\r\n",
        "                stride=1,\r\n",
        "                padding=3,\r\n",
        "            ),\r\n",
        "            nn.Conv2d(\r\n",
        "                in_channels=1,\r\n",
        "                out_channels=64,\r\n",
        "                kernel_size=6,\r\n",
        "                stride=2,\r\n",
        "                padding=2,\r\n",
        "                bias=False,\r\n",
        "            ),\r\n",
        "            nn.InstanceNorm2d(64),\r\n",
        "            nn.ReLU(),\r\n",
        "            nn.Conv2d(\r\n",
        "                in_channels=64,\r\n",
        "                out_channels=128,\r\n",
        "                kernel_size=3,\r\n",
        "                stride=2,\r\n",
        "                padding=1,\r\n",
        "                bias=False,\r\n",
        "            ),\r\n",
        "            nn.InstanceNorm2d(128),\r\n",
        "            nn.ReLU(),\r\n",
        "            nn.Conv2d(\r\n",
        "                in_channels=128,\r\n",
        "                out_channels=256,\r\n",
        "                kernel_size=3,\r\n",
        "                stride=2,\r\n",
        "                padding=1,\r\n",
        "                bias=False,\r\n",
        "            ),\r\n",
        "            nn.InstanceNorm2d(256),\r\n",
        "            nn.ReLU(),\r\n",
        "        )\r\n",
        "\r\n",
        "        self.convtrans1 = nn.ConvTranspose2d(\r\n",
        "            in_channels=256,\r\n",
        "            out_channels=128,\r\n",
        "            kernel_size=3,\r\n",
        "            stride=2,\r\n",
        "            padding=1,\r\n",
        "            bias=False,\r\n",
        "        )\r\n",
        "\r\n",
        "        self.active1 = nn.Sequential(nn.InstanceNorm2d(128), nn.ReLU())\r\n",
        "        self.convtrans2 = nn.ConvTranspose2d(\r\n",
        "            in_channels=128, out_channels=64, kernel_size=3, stride=2, padding=1\r\n",
        "        )\r\n",
        "        self.convtrans3 = nn.ConvTranspose2d(\r\n",
        "            in_channels=64, out_channels=1, kernel_size=6, stride=2, padding=2\r\n",
        "        )\r\n",
        "        self.selfAttentionlayer = SelfAttentionBlock(in_dim=256)\r\n",
        "        self.resBlock1 = ResidualBlock(channels=256)\r\n",
        "        self.resBlock2 = ResidualBlock(channels=256)\r\n",
        "\r\n",
        "    def forward(self, x):\r\n",
        "        batch_size, ch, h, w = x.shape\r\n",
        "        out = self.downConvBlock(x)\r\n",
        "        out = self.resBlock1(out)\r\n",
        "        out, _ = self.selfAttentionlayer(out)\r\n",
        "        out = self.resBlock2(out)\r\n",
        "        out = self.convtrans1(out, output_size=(batch_size, 128, 32, 32))\r\n",
        "        out = self.active1(out)\r\n",
        "        out = self.convtrans2(out, output_size=(batch_size, 64, 64, 64))\r\n",
        "        out = self.convtrans3(out, output_size=(batch_size, 1, 128, 128))\r\n",
        "        out = self.lastlayer(out)\r\n",
        "        return out"
      ],
      "outputs": [],
      "metadata": {
        "id": "wVHW9DZMeU2J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discriminator"
      ],
      "metadata": {
        "id": "4DAeulBUeumi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "class Block(nn.Module):\r\n",
        "    def __init__(self, in_channels, out_channels, stride):\r\n",
        "        super().__init__()\r\n",
        "        self.conv = nn.Sequential(\r\n",
        "            nn.Conv2d(\r\n",
        "                in_channels,\r\n",
        "                out_channels,\r\n",
        "                kernel_size=4,\r\n",
        "                stride=stride,\r\n",
        "                padding=1,\r\n",
        "                bias=True,\r\n",
        "                padding_mode=\"reflect\",\r\n",
        "            ),\r\n",
        "            nn.InstanceNorm2d(out_channels),\r\n",
        "            nn.LeakyReLU(0.2),\r\n",
        "        )\r\n",
        "\r\n",
        "    def forward(self, x):\r\n",
        "        return self.conv(x)\r\n",
        "\r\n",
        "\r\n",
        "class Discriminator(nn.Module):\r\n",
        "    def __init__(self, in_channels, features=[64, 128, 256, 512]):\r\n",
        "        super().__init__()\r\n",
        "        self.initial = nn.Sequential(\r\n",
        "            nn.Conv2d(\r\n",
        "                in_channels,\r\n",
        "                features[0],\r\n",
        "                kernel_size=4,\r\n",
        "                stride=2,\r\n",
        "                padding=1,\r\n",
        "                padding_mode=\"reflect\",\r\n",
        "            ),\r\n",
        "            nn.LeakyReLU(0.2),\r\n",
        "        )\r\n",
        "\r\n",
        "        layers = []\r\n",
        "\r\n",
        "        in_channels = features[0]\r\n",
        "\r\n",
        "        for feature in features[1:]:\r\n",
        "            layers.append(\r\n",
        "                Block(in_channels, feature, stride=1 if feature == features[-1] else 2)\r\n",
        "            )\r\n",
        "            in_channels = feature\r\n",
        "\r\n",
        "        layers.append(\r\n",
        "            nn.Conv2d(\r\n",
        "                in_channels,\r\n",
        "                1,\r\n",
        "                kernel_size=4,\r\n",
        "                stride=1,\r\n",
        "                padding=1,\r\n",
        "                padding_mode=\"reflect\",\r\n",
        "            )\r\n",
        "        )\r\n",
        "\r\n",
        "        self.model = nn.Sequential(*layers)\r\n",
        "\r\n",
        "    def forward(self, x):\r\n",
        "        x = self.initial(x)\r\n",
        "        return torch.sigmoid(self.model(x))"
      ],
      "outputs": [],
      "metadata": {
        "id": "x2QQ9_w6exxu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Utility Functions"
      ],
      "metadata": {
        "id": "yGCQy8VEfIM8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "def save_some_examples_g(gen, val_loader, epoch, idx, folder):\r\n",
        "    img_glasses, img_noglasses = next(iter(val_loader))\r\n",
        "    img_glasses, img_noglasses = img_glasses.to(DEVICE), img_noglasses.to(DEVICE)\r\n",
        "    gen.eval()\r\n",
        "    with torch.no_grad():\r\n",
        "        y_fake_glasses = gen(img_noglasses)\r\n",
        "        y_fake_glasses = y_fake_glasses * 0.5 + 0.5\r\n",
        "        save_image(y_fake_glasses, os.path.join(folder, f\"{epoch}_{idx}_fake_glasses.png\"))\r\n",
        "        # save_image(img_noglasses, os.path.join(folder, f\"{epoch}_no_glasses.png\"))\r\n",
        "        # save_image(img_glasses, os.path.join(folder, f\"{epoch}_glasses.png\"))\r\n",
        "    gen.train()\r\n",
        "\r\n",
        "def save_some_examples_n(gen, val_loader, epoch, idx, folder):\r\n",
        "    img_glasses, img_noglasses = next(iter(val_loader))\r\n",
        "    img_glasses, img_noglasses = img_glasses.to(DEVICE), img_noglasses.to(DEVICE)\r\n",
        "    gen.eval()\r\n",
        "    with torch.no_grad():\r\n",
        "        y_fake_no_glasses = gen(img_glasses)\r\n",
        "        y_fake_no_glasses = y_fake_no_glasses * 0.5 + 0.5\r\n",
        "        save_image(y_fake_no_glasses, os.path.join(folder, f\"{epoch}_{idx}_fake_no_glasses.png\"))\r\n",
        "        # save_image(img_glasses, os.path.join(folder, f\"{epoch}_glasses.png\"))\r\n",
        "        # save_image(img_noglasses, os.path.join(folder, f\"{epoch}_no_glasses.png\"))\r\n",
        "    gen.train()\r\n",
        "\r\n",
        "\r\n",
        "def save_checkpoint(model, optimizer, epoch, filename):\r\n",
        "    filename = str(epoch) + filename + \"_cpt.pth.tar\"\r\n",
        "    print(\"=> Saving Checkpoint\")\r\n",
        "    checkpoint = {\"state_dict\": model.state_dict(), \"optimizer\": optimizer.state_dict()}\r\n",
        "    torch.save(checkpoint, filename)\r\n",
        "\r\n",
        "\r\n",
        "def load_checkpoint(checkpoint_file, model, optimizer, lr):\r\n",
        "    print(\"=> Loading Checkpoint\")\r\n",
        "    checkpoint = torch.load(checkpoint_file, map_location=DEVICE)\r\n",
        "    model.load_state(checkpoint[\"state_dict\"])\r\n",
        "    optimizer.load_state(checkpoint[\"state_dict\"])\r\n",
        "    for param_group in optimizer.param_groups:\r\n",
        "        param_group[\"lr\"] = lr\r\n"
      ],
      "outputs": [],
      "metadata": {
        "id": "cmYVmvNgfLML"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Function"
      ],
      "metadata": {
        "id": "BVuOobzE2hdL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "def getVggFeatures(input, i):\r\n",
        "    # function to get vgg features\r\n",
        "    # phi according to research paper\r\n",
        "    # i means the same as that of the paper\r\n",
        "    layer_dict = {1: 5, 2: 10, 3: 17, 4: 24, 5: 30}\r\n",
        "    vgg16 = models.vgg16(pretrained=True)\r\n",
        "    output = vgg16.features[: layer_dict[i]](input)\r\n",
        "    return output\r\n",
        "\r\n",
        "\r\n",
        "def pcclT1(cycle_specs, img_glasses, l1):\r\n",
        "    # need to convert b/w into 3 channels\r\n",
        "    batch_size, c, w, h = cycle_specs.shape\r\n",
        "    cycle_specs = cycle_specs.repeat(batch_size, 3, w, h)\r\n",
        "    img_glasses = img_glasses.repeat(batch_size, 3, w, h)\r\n",
        "    out = 0\r\n",
        "    for i in range(1, 6):\r\n",
        "        phi_gened = getVggFeatures(cycle_specs, i)\r\n",
        "        phi_og = getVggFeatures(img_glasses, i)\r\n",
        "        out += l1(phi_gened - phi_og)\r\n",
        "    # i dont know if out has to be divided by 5 or not\r\n",
        "    return out\r\n",
        "\r\n",
        "\r\n",
        "def pcclT2(cycle_nospecs, img_noglasses, l1):\r\n",
        "    # need to convert b/w into 3 channels\r\n",
        "    batch_size, c, w, h = cycle_nospecs.shape\r\n",
        "    out = 0\r\n",
        "    cycle_nospecs = cycle_nospecs.repeat(batch_size, 3, w, h)\r\n",
        "    img_noglasses = img_noglasses.repeat(batch_size, 3, w, h)\r\n",
        "    out = 0\r\n",
        "    for i in range(1, 6):\r\n",
        "        phi_gened = getVggFeatures(cycle_nospecs, i)\r\n",
        "        phi_og = getVggFeatures(img_noglasses, i)\r\n",
        "        out += l1(phi_gened - phi_og)\r\n",
        "    # i dont know if out has to be divided by 5 or not\r\n",
        "    return out\r\n",
        "\r\n",
        "\r\n",
        "def trainFn(\r\n",
        "    disc_G, disc_N, gen_G, gen_N, loader, optim_g, optim_d, l1, mse, val_loader, epoch\r\n",
        "):\r\n",
        "    loop = tqdm(loader, leave=True, position=0)\r\n",
        "\r\n",
        "    for idx, (img_glasses, img_noglasses) in enumerate(loop):\r\n",
        "        img_glasses = img_glasses.to(DEVICE)\r\n",
        "        img_noglasses = img_noglasses.to(DEVICE)\r\n",
        "\r\n",
        "        # Train Discriminators\r\n",
        "        start_time = time.time()\r\n",
        "\r\n",
        "        fake_nospecs = gen_N(img_glasses)\r\n",
        "        disc_ns_real = disc_N(img_noglasses)\r\n",
        "        disc_ns_fake = disc_N(fake_nospecs.detach())\r\n",
        "        disc_ns_real_loss = mse(disc_ns_real, torch.ones_like(disc_ns_real))\r\n",
        "        disc_ns_fake_loss = mse(disc_ns_fake, torch.zeros_like(disc_ns_fake))\r\n",
        "        disc_ns_loss = disc_ns_real_loss + disc_ns_fake_loss\r\n",
        "\r\n",
        "        fake_specs = gen_G(img_noglasses)\r\n",
        "        disc_s_real = disc_G(img_glasses)\r\n",
        "        disc_s_fake = disc_G(fake_specs.detach())\r\n",
        "        disc_s_real_loss = mse(disc_s_real, torch.ones_like(disc_s_real))\r\n",
        "        disc_s_fake_loss = mse(disc_s_fake, torch.zeros_like(disc_s_fake))\r\n",
        "        disc_s_loss = disc_s_real_loss + disc_s_fake_loss\r\n",
        "\r\n",
        "        end_time = time.time()\r\n",
        "        print(f\"{end_time - start_time} Discriminators training\")\r\n",
        "\r\n",
        "        # grand total\r\n",
        "        D_loss = (disc_ns_loss + disc_s_loss) / 2\r\n",
        "\r\n",
        "        # backprop and update weights of discriminator\r\n",
        "\r\n",
        "        start_time = time.time()\r\n",
        "\r\n",
        "        optim_d.zero_grad()\r\n",
        "        D_loss.backward()\r\n",
        "        optim_d.step()\r\n",
        "\r\n",
        "        end_time = time.time()\r\n",
        "        print(f\"{end_time - start_time} backprop discriminator\")\r\n",
        "\r\n",
        "        # Train Generators\r\n",
        "        # adversarial loss\r\n",
        "\r\n",
        "        start_time = time.time()\r\n",
        "\r\n",
        "        disc_ns_fake = disc_N(fake_nospecs)\r\n",
        "        disc_s_fake = disc_G(fake_specs)\r\n",
        "        loss_g_s = mse(disc_s_fake, torch.ones_like(disc_s_fake))\r\n",
        "        loss_g_ns = mse(disc_ns_fake, torch.ones_like(disc_ns_fake))\r\n",
        "\r\n",
        "        end_time = time.time()\r\n",
        "        print(f\"{end_time - start_time} adversarial loss\")\r\n",
        "\r\n",
        "        # cycle loss\r\n",
        "\r\n",
        "        start_time = time.time()\r\n",
        "\r\n",
        "        cycle_specs = gen_G(fake_nospecs)\r\n",
        "        cycle_nospecs = gen_N(fake_specs)\r\n",
        "        specs_cycle_loss = l1(img_glasses, cycle_specs)\r\n",
        "        nospecs_cycle_loss = l1(img_noglasses, cycle_nospecs)\r\n",
        "\r\n",
        "        end_time = time.time()\r\n",
        "        print(f\"{end_time - start_time} cycle loss\")\r\n",
        "\r\n",
        "        ## Perceptual Loss\r\n",
        "        start_time = time.time()\r\n",
        "        loss_pccl = pcclT1(cycle_specs, img_glasses, l1) + pcclT2(\r\n",
        "            cycle_nospecs, img_noglasses, l1\r\n",
        "        )\r\n",
        "        end_time = time.time()\r\n",
        "        print(f\"{end_time - start_time} Perceptual loss\")\r\n",
        "\r\n",
        "        # grand total\r\n",
        "        G_loss = (\r\n",
        "            loss_g_s\r\n",
        "            + loss_g_ns\r\n",
        "            + (specs_cycle_loss * LAMBDA_CYCLE)\r\n",
        "            + (nospecs_cycle_loss * LAMBDA_CYCLE)\r\n",
        "            + loss_pccl\r\n",
        "        )\r\n",
        "\r\n",
        "        # backprop and update weights of discriminator\r\n",
        "\r\n",
        "        start_time = time.time()\r\n",
        "\r\n",
        "        optim_g.zero_grad()\r\n",
        "        G_loss.backward()\r\n",
        "        optim_g.step()\r\n",
        "\r\n",
        "        end_time = time.time()\r\n",
        "        print(f\"{end_time - start_time} generator backprop\")\r\n",
        "\r\n",
        "        # if idx == 2000:\r\n",
        "        #   print(f\"Generator Loss: {G_loss} Discriminator Loss: {D_loss}\")\r\n",
        "\r\n",
        "        # if idx % 2 == 0:\r\n",
        "        print(f\"Generator Loss: {G_loss} Discriminator Loss: {D_loss}\")\r\n",
        "\r\n",
        "        save_some_examples_g(gen_G, val_loader, epoch, idx, folder=resultGlasses)\r\n",
        "        save_some_examples_n(gen_N, val_loader, epoch, idx, folder=resultsNoGlasses)\r\n"
      ],
      "outputs": [],
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "RToE1PfO2klY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Main Function"
      ],
      "metadata": {
        "id": "faIMfVZ72ljk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "def main():\r\n",
        "  # Instantiate Generator and Discriminators \r\n",
        "  disc_G = Discriminator(in_channels=1).to(DEVICE)\r\n",
        "  disc_N = Discriminator(in_channels=1).to(DEVICE)\r\n",
        "  gen_G = Generator(img_channels=1, num_residuals=4).to(DEVICE)\r\n",
        "  gen_N = Generator(img_channels=1, num_residuals=4).to(DEVICE)\r\n",
        "\r\n",
        "  # Initialize Optimizers\r\n",
        "  opt_disc = optim.Adam(\r\n",
        "      list(disc_G.parameters()) + list(disc_N.parameters()),\r\n",
        "      lr = LEARNING_RATE,\r\n",
        "      betas = (0.5, 0.999)\r\n",
        "  )\r\n",
        "\r\n",
        "  opt_gen = optim.Adam(\r\n",
        "      list(gen_G.parameters()) + list(gen_N.parameters()),\r\n",
        "      lr = LEARNING_RATE,\r\n",
        "      betas = (0.5, 0.999)\r\n",
        "  )\r\n",
        "\r\n",
        "  # Loss Function\r\n",
        "  L1 = nn.L1Loss()\r\n",
        "  mse = nn.MSELoss()\r\n",
        "\r\n",
        "  # Instantiate Datasets\r\n",
        "  trainData = GlassesDataset(root_dir_glass=trainGlassesDir, root_dir_noglass=trainNoglassesDir, transform=transforms_train)\r\n",
        "  valData = GlassesDataset(root_dir_glass=valGlassesDir, root_dir_noglass=valNoglassesDir, transform=transforms_val)\r\n",
        "\r\n",
        "  # Dataloader\r\n",
        "  train_loader = DataLoader(trainData, BATCH_SIZE, shuffle=True)\r\n",
        "  val_loader = DataLoader(valData, BATCH_SIZE, shuffle=False)\r\n",
        "\r\n",
        "  # TRAINING LOOP\r\n",
        "  for epoch in range(NUM_EPOCHS):\r\n",
        "    print(f\"Epoch {epoch}\")\r\n",
        "    trainFn(disc_G, disc_N, gen_G, gen_N, train_loader, opt_gen, opt_disc, L1, mse, val_loader, epoch)\r\n",
        "\r\n",
        "    # if epoch % 3 == 0:\r\n",
        "    save_checkpoint(disc_G, opt_disc, epoch, \"disc_G\")\r\n",
        "    save_checkpoint(disc_N, opt_disc, epoch, \"disc_N\")\r\n",
        "    save_checkpoint(gen_G, opt_gen, epoch, \"gen_G\")\r\n",
        "    save_checkpoint(gen_N, opt_gen, epoch, \"gen_N\")\r\n",
        "\r\n",
        "    # save_some_examples_g(gen_G, val_loader, epoch, folder = resultGlasses)\r\n",
        "    # save_some_examples_n(gen_N, val_loader, epoch, folder = resultsNoGlasses)"
      ],
      "outputs": [],
      "metadata": {
        "id": "5GO-M2vc2oSV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "main()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/2035 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "0.19081783294677734 data loading time\n",
            "3.1997427940368652 Discriminators training\n",
            "1.1547625064849854 backprop discriminator\n",
            "0.24422144889831543 adversarial loss\n",
            "2.3628146648406982 cycle loss\n",
            "9.593616962432861 generator backprop\n",
            "Generator Loss: 9.346444129943848 Discriminator Loss: 0.5261926651000977\n",
            "0.11168742179870605 data loading time\n",
            "0.11684632301330566 data loading time\n"
          ]
        },
        {
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        {
          "output_type": "stream",
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            "0.11888289451599121 data loading time\n",
            "2.5681607723236084 Discriminators training\n",
            "1.098221778869629 backprop discriminator\n",
            "0.2455005645751953 adversarial loss\n",
            "2.1605730056762695 cycle loss\n",
            "9.403878211975098 generator backprop\n",
            "Generator Loss: 10.514144897460938 Discriminator Loss: 0.5621699690818787\n",
            "0.11118292808532715 data loading time\n",
            "0.11231112480163574 data loading time\n"
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        {
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            "0.11957621574401855 data loading time\n",
            "2.574983835220337 Discriminators training\n",
            "1.1013920307159424 backprop discriminator\n",
            "0.2380380630493164 adversarial loss\n",
            "2.149538278579712 cycle loss\n",
            "9.355863094329834 generator backprop\n",
            "Generator Loss: 9.254664421081543 Discriminator Loss: 0.591654360294342\n",
            "0.11113762855529785 data loading time\n",
            "0.11329293251037598 data loading time\n"
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            "0.11903023719787598 data loading time\n",
            "2.569998264312744 Discriminators training\n",
            "1.0963680744171143 backprop discriminator\n",
            "0.23411822319030762 adversarial loss\n",
            "2.1075704097747803 cycle loss\n",
            "9.346097230911255 generator backprop\n",
            "Generator Loss: 7.257009029388428 Discriminator Loss: 0.5804786086082458\n",
            "0.11203408241271973 data loading time\n",
            "0.11315393447875977 data loading time\n"
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            "0.12311840057373047 data loading time\n",
            "2.561337471008301 Discriminators training\n",
            "1.1127817630767822 backprop discriminator\n",
            "0.24317717552185059 adversarial loss\n",
            "2.1143510341644287 cycle loss\n",
            "9.30529236793518 generator backprop\n",
            "Generator Loss: 6.145906448364258 Discriminator Loss: 0.5474666357040405\n",
            "0.11180257797241211 data loading time\n",
            "0.11356997489929199 data loading time\n"
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            "2.5580201148986816 Discriminators training\n",
            "1.0749008655548096 backprop discriminator\n",
            "0.24089360237121582 adversarial loss\n",
            "2.0877270698547363 cycle loss\n",
            "9.210145235061646 generator backprop\n",
            "Generator Loss: 5.683241844177246 Discriminator Loss: 0.6042537689208984\n",
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            "2.542994260787964 Discriminators training\n",
            "1.077178955078125 backprop discriminator\n",
            "0.24052095413208008 adversarial loss\n",
            "2.081648111343384 cycle loss\n",
            "9.212797164916992 generator backprop\n",
            "Generator Loss: 4.750693321228027 Discriminator Loss: 0.6056523323059082\n",
            "0.11120009422302246 data loading time\n",
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            "2.533768892288208 Discriminators training\n",
            "1.0812299251556396 backprop discriminator\n",
            "0.2398386001586914 adversarial loss\n",
            "2.07686710357666 cycle loss\n",
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            "Generator Loss: 6.759496688842773 Discriminator Loss: 0.5147786140441895\n",
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            "2.5471997261047363 Discriminators training\n",
            "1.0634303092956543 backprop discriminator\n",
            "0.23538899421691895 adversarial loss\n",
            "2.0752482414245605 cycle loss\n",
            "9.168410062789917 generator backprop\n",
            "Generator Loss: 4.966924667358398 Discriminator Loss: 0.5409870147705078\n",
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            "2.5343098640441895 Discriminators training\n",
            "1.0661633014678955 backprop discriminator\n",
            "0.24545550346374512 adversarial loss\n",
            "2.071425676345825 cycle loss\n",
            "9.166049242019653 generator backprop\n",
            "Generator Loss: 4.554622650146484 Discriminator Loss: 0.5389804840087891\n",
            "0.11097335815429688 data loading time\n",
            "0.11112761497497559 data loading time\n"
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            "2.5678703784942627 Discriminators training\n",
            "1.065720558166504 backprop discriminator\n",
            "0.23373079299926758 adversarial loss\n",
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            "9.222495079040527 generator backprop\n",
            "Generator Loss: 4.149910926818848 Discriminator Loss: 0.5325177907943726\n",
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            "0.1133575439453125 data loading time\n"
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            "2.5716776847839355 Discriminators training\n",
            "1.0831151008605957 backprop discriminator\n",
            "0.24401545524597168 adversarial loss\n",
            "2.105356216430664 cycle loss\n",
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