SRGAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "#%matplotlib inline\n",
    "import argparse\n",
    "import os\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.optim as optim\n",
    "import torch.utils.data\n",
    "import torchvision.datasets as dset\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.utils as vutils\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "from IPython.display import HTML\n",
    "from torch.autograd import Variable\n",
    "from torchvision.utils import save_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Root directory for dataset\n",
    "dataroot = \"data\"\n",
    "\n",
    "# Number of workers for dataloader\n",
    "workers = 2\n",
    "\n",
    "# Batch size during training\n",
    "batch_size = 10\n",
    "\n",
    "# Spatial size of training images. All images will be resized to this\n",
    "#   size using a transformer.\n",
    "image_size_HR = 64\n",
    "image_size_LR = 32\n",
    "# Number of channels in the training images. For color images this is 3\n",
    "nc = 3\n",
    "\n",
    "# Size of z latent vector (i.e. size of generator input)\n",
    "nz = 100\n",
    "\n",
    "# Size of feature maps in generator\n",
    "ngf = 32\n",
    "\n",
    "# Size of feature maps in discriminator\n",
    "ndf = 32\n",
    "\n",
    "# Number of training epochs\n",
    "num_epochs = 3\n",
    "\n",
    "# Learning rate for optimizers\n",
    "lr = 0.0002\n",
    "\n",
    "# Beta1 hyperparam for Adam optimizers\n",
    "beta1 = 0.5\n",
    "\n",
    "# Number of GPUs available. Use 0 for CPU mode.\n",
    "ngpu = 1\n",
    "\n",
    "\n",
    "# The interval between image sampling.\n",
    "sample_interval = 400 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d98379a188>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can use an image folder dataset the way we have it setup.\n",
    "# Create the dataset\n",
    "dataset_HR = dset.ImageFolder(root=dataroot,\n",
    "                           transform=transforms.Compose([\n",
    "                               transforms.Resize(image_size_HR),\n",
    "                               transforms.CenterCrop(image_size_HR),\n",
    "                               transforms.ToTensor(),\n",
    "                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "                           ]))\n",
    "\n",
    "dataset_LR = dset.ImageFolder(root=dataroot,\n",
    "                           transform=transforms.Compose([\n",
    "                               transforms.Resize(image_size_LR),\n",
    "                               transforms.CenterCrop(image_size_LR),\n",
    "                               transforms.ToTensor(),\n",
    "                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "                           ]))\n",
    "\n",
    "# Create the dataloader\n",
    "dataloader_HR = torch.utils.data.DataLoader(dataset_HR, batch_size=batch_size,\n",
    "                                         shuffle=False, num_workers=workers)\n",
    "\n",
    "dataloader_LR = torch.utils.data.DataLoader(dataset_LR, batch_size=batch_size,\n",
    "                                         shuffle=False, num_workers=workers)\n",
    "\n",
    "# Decide which device we want to run on\n",
    "device = torch.device(\"cuda:0\" if (torch.cuda.is_available() and ngpu > 0) else \"cpu\")\n",
    "\n",
    "# Plot some training images\n",
    "real_batch = next(iter(dataloader_HR))\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"HR Images\")\n",
    "plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))\n",
    "\n",
    "\n",
    "# Plot some training images\n",
    "real_batch = next(iter(dataloader_LR))\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"LR Images\")\n",
    "plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom weights initialization called on netG and netD\n",
    "def weights_init(m):\n",
    "    classname = m.__class__.__name__\n",
    "    if classname.find('Conv') != -1:\n",
    "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
    "    elif classname.find('BatchNorm') != -1:\n",
    "        nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
    "        nn.init.constant_(m.bias.data, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generator Code\n",
    "\n",
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Generator, self).__init__()\n",
    "        self.main = nn.Sequential(\n",
    "            # input is Z, going into a convolution\n",
    "            nn.ConvTranspose2d( 3,32 * 8, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(32 * 8),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf*8) x 4 x 4\n",
    "            nn.ConvTranspose2d(32 * 8, 32 * 4, 3, 1, 1, bias=False),\n",
    "            nn.BatchNorm2d(32 * 4),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf*4) x 8 x 8\n",
    "            nn.ConvTranspose2d( 32 * 4, 32 * 2, 3, 1, 1, bias=False),\n",
    "            nn.BatchNorm2d(32 * 2),\n",
    "            nn.ReLU(True),\n",
    "#             # state size. (ngf*2) x 16 x 16\n",
    "            nn.ConvTranspose2d( 32 * 2, 32, 3, 1, 1, bias=False),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf) x 32 x 32\n",
    "            nn.ConvTranspose2d( 32, nc, 3, 1, 1, bias=False),\n",
    "            nn.Tanh()\n",
    "            # state size. (nc) x 64 x 64\n",
    "        )\n",
    "\n",
    "    def forward(self, input):\n",
    "        return self.main(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.main = nn.Sequential(\n",
    "            # input is (nc) x 64 x 64\n",
    "            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf) x 32 x 32\n",
    "            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 2),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf*2) x 16 x 16\n",
    "            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 4),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf*4) x 8 x 8\n",
    "            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 8),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf*8) x 4 x 4\n",
    "            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, input):\n",
    "        return self.main(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generator(\n",
      "  (main): Sequential(\n",
      "    (0): ConvTranspose2d(3, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    (10): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): ConvTranspose2d(32, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    (13): Tanh()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Create the generators\n",
    "netG = Generator().to(device)\n",
    "\n",
    "# Initialize weights\n",
    "netG.apply(weights_init)\n",
    "\n",
    "# Print the model\n",
    "print(netG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discriminator(\n",
      "  (main): Sequential(\n",
      "    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      "    (2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (4): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      "    (5): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (7): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      "    (8): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (9): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (10): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      "    (11): Conv2d(256, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
      "    (12): Sigmoid()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Create the discriminators\n",
    "netD = Discriminator().to(device)\n",
    "\n",
    "# Initialize weights\n",
    "netD.apply(weights_init)\n",
    "\n",
    "# Print the model\n",
    "print(netD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize MSELoss function\n",
    "loss = nn.MSELoss()\n",
    "# loss = nn.BCELoss()\n",
    "\n",
    "# Create batch of latent vectors that we will use to visualize\n",
    "#  the progression of the generator\n",
    "#fixed_noise = torch.randn(64, nz, 1, 1, device=device)\n",
    "\n",
    "# Establish convention for real and fake labels during training\n",
    "real_label = 1\n",
    "fake_label = 0\n",
    "\n",
    "# Setup Adam optimizers for both G and D\n",
    "optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))\n",
    "optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abdelsalam\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\loss.py:431: UserWarning: Using a target size (torch.Size([10, 1])) that is different to the input size (torch.Size([10, 1, 1, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0/3] [Batch 0/20260] [D loss: 0.290533] [G loss: 0.398791]\n",
      "[Epoch 0/3] [Batch 400/20260] [D loss: 0.015379] [G loss: 0.017934]\n",
      "[Epoch 0/3] [Batch 800/20260] [D loss: 0.002933] [G loss: 0.008042]\n",
      "[Epoch 0/3] [Batch 1200/20260] [D loss: 0.000005] [G loss: 0.006868]\n",
      "[Epoch 0/3] [Batch 1600/20260] [D loss: 0.000001] [G loss: 0.010089]\n",
      "[Epoch 0/3] [Batch 2000/20260] [D loss: 0.000000] [G loss: 0.004102]\n",
      "[Epoch 0/3] [Batch 2400/20260] [D loss: 0.000047] [G loss: 0.004319]\n",
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      "[Epoch 0/3] [Batch 3200/20260] [D loss: 0.000000] [G loss: 0.004319]\n",
      "[Epoch 0/3] [Batch 3600/20260] [D loss: 0.000032] [G loss: 0.004495]\n",
      "[Epoch 0/3] [Batch 4000/20260] [D loss: 0.000001] [G loss: 0.004812]\n",
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      "[Epoch 0/3] [Batch 14800/20260] [D loss: 0.000000] [G loss: 0.003034]\n",
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      "[Epoch 0/3] [Batch 19600/20260] [D loss: 0.000000] [G loss: 0.002885]\n",
      "[Epoch 0/3] [Batch 20000/20260] [D loss: 0.000000] [G loss: 0.002559]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abdelsalam\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\loss.py:431: UserWarning: Using a target size (torch.Size([9, 1])) that is different to the input size (torch.Size([9, 1, 1, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 1/3] [Batch 140/20260] [D loss: 0.000000] [G loss: 0.002738]\n",
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      "[Epoch 2/3] [Batch 17480/20260] [D loss: 0.000000] [G loss: 0.002829]\n",
      "[Epoch 2/3] [Batch 17880/20260] [D loss: 0.000000] [G loss: 0.001915]\n",
      "[Epoch 2/3] [Batch 18280/20260] [D loss: 0.000000] [G loss: 0.002423]\n",
      "[Epoch 2/3] [Batch 18680/20260] [D loss: 0.000000] [G loss: 0.002579]\n",
      "[Epoch 2/3] [Batch 19080/20260] [D loss: 0.000000] [G loss: 0.002251]\n",
      "[Epoch 2/3] [Batch 19480/20260] [D loss: 0.000000] [G loss: 0.002164]\n",
      "[Epoch 2/3] [Batch 19880/20260] [D loss: 0.000000] [G loss: 0.003321]\n"
     ]
    }
   ],
   "source": [
    "Tensor = torch.cuda.FloatTensor\n",
    "for epoch in range(num_epochs):\n",
    "    for i, ((imgs_HR, _), (imgs_LR, _)) in enumerate(zip(dataloader_HR, dataloader_LR)):\n",
    "\n",
    "        batch_size_HR = imgs_HR.shape[0]\n",
    "        batch_size_LR = imgs_LR.shape[0]\n",
    "        # Adversarial ground truths\n",
    "        valid = Variable(Tensor(batch_size_HR, 1).fill_(1.0), requires_grad=False)\n",
    "        fake = Variable(Tensor(batch_size_LR, 1).fill_(0.0), requires_grad=False)\n",
    "\n",
    "        # Configure input\n",
    "        imgs_HR = Variable(imgs_HR.type(Tensor).expand(imgs_HR.size(0), 3, image_size_HR, image_size_HR))\n",
    "        imgs_LR = Variable(imgs_LR.type(Tensor).expand(imgs_LR.size(0), 3, image_size_LR, image_size_LR))\n",
    "\n",
    "        # ------------------\n",
    "        #  Train Generators\n",
    "        # ------------------\n",
    "\n",
    "        optimizerG.zero_grad()\n",
    "\n",
    "       \n",
    "        # Generate a batch of images\n",
    "        gen_imgs = netG(imgs_LR)\n",
    "        # Determine validity of generated images\n",
    "        validity = netD(gen_imgs)\n",
    "        \n",
    "        g_loss = (loss(gen_imgs, imgs_HR))\n",
    "\n",
    "        g_loss.backward()\n",
    "        optimizerG.step()\n",
    "\n",
    "        # ----------------------\n",
    "        #  Train Discriminators\n",
    "        # ----------------------\n",
    "\n",
    "        optimizerD.zero_grad()\n",
    "\n",
    "        # Determine validity of real and generated images\n",
    "        validity_real = netD(imgs_HR)\n",
    "        validity_fake = netD(gen_imgs.detach())\n",
    "\n",
    "        d_loss = (\n",
    "            loss(validity_real, valid)\n",
    "            + loss(validity_fake, fake)\n",
    "        ) / 2\n",
    "\n",
    "  \n",
    "        d_loss.backward()\n",
    "\n",
    "        optimizerD.step()\n",
    "\n",
    "\n",
    "        batches_done = epoch * len(dataloader_HR) + i\n",
    "        if batches_done % sample_interval == 0:\n",
    "            gen_imgs = torch.cat((imgs_HR.data, gen_imgs.data), 0)\n",
    "            save_image(gen_imgs, \"images/%d.png\" % batches_done, nrow=8, normalize=True)\n",
    "            print(\n",
    "            \"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]\"\n",
    "            % (epoch, num_epochs, i, len(dataloader_HR), d_loss.item(), g_loss.item())\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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