enhancing-anime-faces-with-srgan.ipynb

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   },
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "twenty-pressure",
   "metadata": {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torchvision.datasets import ImageFolder\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "from torchvision.models import vgg19\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torchvision.utils import save_image, make_grid\n",
    "from PIL import Image\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "random.seed(42)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "constant-metallic",
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     "start_time": "2021-06-25T06:06:48.167479",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "## Import Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "biblical-drinking",
   "metadata": {
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   "source": [
    "class ImageDl(Dataset):\n",
    "    def __init__(self,files):\n",
    "        self.files = files\n",
    "        self.lr_transform = transforms.Compose([\n",
    "            transforms.Resize((64,64),Image.BICUBIC),\n",
    "            transforms.CenterCrop(64),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(*stats)   \n",
    "        ])\n",
    "        self.hr_transform = transforms.Compose([\n",
    "            transforms.Resize((256,256),Image.BICUBIC),\n",
    "            transforms.CenterCrop(256),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(*stats)   \n",
    "        ])\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.files[index % len(self.files)][0]\n",
    "        img_lr = self.lr_transform(img)\n",
    "        img_hr = self.hr_transform(img)\n",
    "        return {\"lr\": img_lr, \"hr\": img_hr}\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "particular-render",
   "metadata": {
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total batches: 3179\n"
     ]
    }
   ],
   "source": [
    "os.makedirs(\"generated-images\", exist_ok=True)\n",
    "dataset = ImageFolder('../input/animefacedataset/')\n",
    "batch_size = 20\n",
    "stats = (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)\n",
    "train_dataloader = DataLoader(ImageDl(dataset), batch_size, shuffle=True, num_workers=3, pin_memory=True)\n",
    "# pin_memory: Host to GPU copies are much faster when they originate from pinned (page-locked) memory\n",
    "print('Total batches:',len(train_dataloader))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "express-pride",
   "metadata": {
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     "exception": false,
     "start_time": "2021-06-25T06:07:25.502997",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "determined-alexander",
   "metadata": {
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   "outputs": [],
   "source": [
    "class ResidualBlock(nn.Module):\n",
    "    \n",
    "    def __init__(self,in_channels):\n",
    "        super(ResidualBlock,self).__init__()\n",
    "        self.res_block = nn.Sequential(\n",
    "            nn.Conv2d(in_channels,in_channels,kernel_size=3,stride=1,padding=1),\n",
    "            nn.BatchNorm2d(in_channels,0.8),\n",
    "            nn.PReLU(),\n",
    "            nn.Conv2d(in_channels,in_channels,kernel_size=3,stride=1,padding=1),\n",
    "            nn.BatchNorm2d(in_channels,0.8)\n",
    "        )\n",
    "        \n",
    "    def forward(self,x):\n",
    "        return x + self.res_block(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "removed-trigger",
   "metadata": {
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     "status": "completed"
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   },
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    \n",
    "    def __init__(self,in_channels=3,out_channels=3,res_block_size=16):\n",
    "        super(Generator,self).__init__()\n",
    "        # first conv block\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels,64,kernel_size=9,stride=1,padding=4),      \n",
    "            nn.PReLU()\n",
    "        )\n",
    "        # residual blocks\n",
    "        res_blocks = []\n",
    "        for _ in range(res_block_size):\n",
    "            res_blocks.append(ResidualBlock(64))\n",
    "        \n",
    "        self.res_blocks = nn.Sequential(*res_blocks)\n",
    "        # second conv layer\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(64,64,kernel_size=3,stride=1,padding=1),\n",
    "            nn.BatchNorm2d(64,0.8),            \n",
    "        )\n",
    "        # upsampling layers\n",
    "        upsample_layers = []\n",
    "        for out_features in range(2):\n",
    "            upsample_layers += [\n",
    "                nn.Conv2d(64,256,kernel_size=3,stride=1,padding=1),       \n",
    "                nn.BatchNorm2d(256),\n",
    "                nn.PixelShuffle(upscale_factor=2),\n",
    "                nn.PReLU(),       \n",
    "            ]\n",
    "        self.upsample_layers = nn.Sequential(*upsample_layers)\n",
    "        # third conv layer\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(64,out_channels,kernel_size=9,stride=1,padding=4),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "        \n",
    "    def forward(self,x):\n",
    "        out1 = self.conv1(x)\n",
    "        out = self.res_blocks(out1)\n",
    "        out2 = self.conv2(out)\n",
    "        out_final = torch.add(out1,out2)\n",
    "        out_final = self.upsample_layers(out_final)\n",
    "        out_final = self.conv3(out_final)\n",
    "        return out_final\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graduate-reservoir",
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    "tags": []
   },
   "source": [
    "## Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "improving-campaign",
   "metadata": {
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   "source": [
    "class Discriminator(nn.Module):\n",
    "    def __init__(self,input_shape):\n",
    "        super(Discriminator,self).__init__()\n",
    "        self.input_shape = input_shape\n",
    "        in_channels,in_height,in_width = self.input_shape\n",
    "        \n",
    "        def disc_block(in_filters, out_filters,first_block=False):\n",
    "            layers = []\n",
    "            layers.append(nn.Conv2d(in_filters,out_filters,kernel_size=3,stride=1,padding=1))\n",
    "            if not first_block :\n",
    "                layers.append(nn.BatchNorm2d(out_filters))\n",
    "            \n",
    "            layers.append(nn.LeakyReLU(0.2,inplace=True))\n",
    "            layers.append(nn.Conv2d(out_filters,out_filters,kernel_size=3,stride=2,padding=1))\n",
    "            layers.append(nn.BatchNorm2d(out_filters))\n",
    "            layers.append(nn.LeakyReLU(0.2,inplace=True))\n",
    "            return layers\n",
    "        \n",
    "        layers = []\n",
    "        in_filters = in_channels\n",
    "        for i, out_filters in enumerate([64,128,256,512]):\n",
    "            layers.extend(disc_block(in_filters,out_filters,first_block=(i == 0)))\n",
    "            in_filters = out_filters\n",
    "            \n",
    "        layers.append(nn.Conv2d(out_filters,1,kernel_size=3,stride=1,padding=1))\n",
    "        self.disc = nn.Sequential(*layers)\n",
    "        \n",
    "    def forward(self,img):\n",
    "        return self.disc(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "single-peter",
   "metadata": {
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     "start_time": "2021-06-25T06:07:25.653373",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "honey-bunch",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n"
     ]
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       "  0%|          | 0.00/548M [00:00<?, ?B/s]"
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   "source": [
    "generator = Generator()\n",
    "discriminator = Discriminator(input_shape=(3,256,256))\n",
    "vgg19_model = vgg19(pretrained=True)\n",
    "# Extract features from vgg pretrained vgg model\n",
    "feature_extractor = nn.Sequential(*list(vgg19_model.features.children())[:18])       \n",
    "feature_extractor.eval()\n",
    "adversarial_crit = torch.nn.MSELoss()\n",
    "content_crit = torch.nn.L1Loss()\n",
    "\n",
    "cuda = torch.cuda.is_available()\n",
    "if cuda:\n",
    "    Tensor = torch.cuda.FloatTensor    \n",
    "    generator = generator.cuda()\n",
    "    discriminator = discriminator.cuda()\n",
    "    feature_extractor = feature_extractor.cuda()\n",
    "    adversarial_crit = adversarial_crit.cuda()\n",
    "    content_crit = content_crit.cuda()\n",
    "    \n",
    "else :\n",
    "    Tensor = torch.Tensor    \n",
    "\n",
    "lr = 0.00008\n",
    "EPOCHS = 4\n",
    "gen_opt = torch.optim.Adam(generator.parameters(), lr=lr, betas=(0.5, 0.999))\n",
    "disc_opt = torch.optim.Adam(discriminator.parameters(), lr=lr, betas=(0.5, 0.999))"
   ]
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   "source": [
    "for epoch in range(EPOCHS):\n",
    "    tqdm_bar = tqdm(train_dataloader)\n",
    "    for idx, imgs in enumerate(tqdm_bar):\n",
    "        generator.train(); discriminator.train()\n",
    "        imgs_lr = Variable(imgs[\"lr\"].type(Tensor))\n",
    "        imgs_hr = Variable(imgs[\"hr\"].type(Tensor))\n",
    "        valid = Variable(Tensor(np.ones((imgs_lr.size(0),1,16,16))), requires_grad=False)\n",
    "        fake = Variable(Tensor(np.zeros((imgs_lr.size(0),1,16,16))), requires_grad=False)\n",
    "        gen_opt.zero_grad()\n",
    "        gen_hr = generator(imgs_lr)\n",
    "        loss_adv = adversarial_crit(discriminator(gen_hr), valid)\n",
    "        gen_features = feature_extractor(gen_hr)\n",
    "        real_features = feature_extractor(imgs_hr)\n",
    "        loss_content = content_crit(gen_features, real_features.detach())\n",
    "        gen_loss = loss_content + 1e-3 * loss_adv\n",
    "        gen_loss.backward()\n",
    "        gen_opt.step()\n",
    "        disc_opt.zero_grad()\n",
    "        real_loss = adversarial_crit(discriminator(imgs_hr), valid)\n",
    "        fake_loss = adversarial_crit(discriminator(gen_hr.detach()), fake)\n",
    "        disc_loss = (real_loss + fake_loss) / 2\n",
    "        disc_loss.backward()\n",
    "        disc_opt.step()\n",
    "\n",
    "        if idx > 3174:\n",
    "            imgs_lr = nn.functional.interpolate(imgs_lr, scale_factor=4)\n",
    "            gen_hr = make_grid(gen_hr, nrow=1, normalize=True)\n",
    "            imgs_lr = make_grid(imgs_lr, nrow=1, normalize=True)\n",
    "            img_grid = torch.cat((imgs_lr, gen_hr), -1)\n",
    "            save_image(img_grid, f\"generated-images/{idx}.png\", normalize=False)\n",
    "\n"
   ]
  }
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