Complete.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Image Compression and Generation using Variational Autoencoders in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Prerequisites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "- Intermediate-level knowledge of Python 3 (dealing with images and vector maths is useful)\n",
    "- Exposure to PyTorch usage\n",
    "- Basic understanding of Autoencoders and Generative Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Project Outline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "**Task 1**: Introduction (this section)\n",
    "\n",
    "**Task 2**: Exploratory Data Analysis and Preprocessing\n",
    "\n",
    "**Task 3**: Training/Validation Split\n",
    "\n",
    "**Task 4**: Creating Data Loaders\n",
    "\n",
    "**Task 5**: VAE Architecture and Model Creation\n",
    "\n",
    "**Task 6**: Training Loop\n",
    "\n",
    "**Task 7**: Results and Other Uses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Task 1: Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "I will explain the VAE briefly, but for additional information, feel free to check out the following resources:\n",
    "\n",
    "- [Resource 1: Blog Post](https://www.jeremyjordan.me/variational-autoencoders/)\n",
    "- [Resource 2: Theory Post](https://ermongroup.github.io/cs228-notes/inference/variational/)\n",
    "- [Resource 3: Lecture Video](https://www.youtube.com/watch?v=P78QYjWh5sM&feature=youtu.be)\n",
    "\n",
    "In addition, our VAE model in PyTorch has been adapted from the official PyTorch implementation you can find in [this repo](https://github.com/pytorch/examples/tree/master/vae). Enjoy the project!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "<img src=\"Images/vae.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.utils.data\n",
    "from torch import nn, optim\n",
    "from torch.nn import functional as F\n",
    "from torchvision import datasets, transforms\n",
    "from torchvision.utils import save_image\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "import random\n",
    "random.seed(5)\n",
    "\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cpu\n"
     ]
    }
   ],
   "source": [
    "epochs = 1000\n",
    "batch_size = 64\n",
    "torch.manual_seed(17)\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f'Device: {device}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Task 2: Exploratory Data Analysis and Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "We use the computer-generated fonts of the [Character Font Images Data Set](http://archive.ics.uci.edu/ml/datasets/Character+Font+Images#)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def show_img(img):\n",
    "    img = img.permute(1, 2, 0)\n",
    "    if img.shape[2]==1:\n",
    "        img = img.view(img.shape[0], img.shape[1])\n",
    "    plt.title(f'Image has size {img.cpu().numpy().shape}')\n",
    "    plt.imshow(img, cmap='gray')\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "transforms_all = transforms.Compose([\n",
    "    transforms.Grayscale(num_output_channels=1),\n",
    "    transforms.Resize(size=50),\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "dummy_batch = torch.utils.data.DataLoader(\n",
    "    datasets.ImageFolder('Font/all/', transform=transforms.ToTensor()),\n",
    "    batch_size=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "for batch in dummy_batch:\n",
    "    original_image = batch[0][0]\n",
    "    show_img(original_image)\n",
    "    show_img(transforms_all(transforms.ToPILImage()(original_image)))\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Task 3: Training/Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[Errno 17] File exists: 'Font/train'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-313-8e62dfd63e4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfolders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfolder\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfolder\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfolders\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfolder\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Font/train'\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      7\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Font/val'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: 'Font/train'"
     ]
    }
   ],
   "source": [
    "folders = os.listdir('Font/all')\n",
    "\n",
    "# ignore hidden files\n",
    "folders = [folder for folder in folders if folder[0]!='.']\n",
    "\n",
    "os.mkdir('Font/train')\n",
    "os.mkdir('Font/val')\n",
    "\n",
    "for folder in tqdm(folders):\n",
    "    os.mkdir(f'Font/val/{folder}')\n",
    "    os.mkdir(f'Font/train/{folder}')\n",
    "    \n",
    "    images = os.listdir(f'Font/all/{folder}')\n",
    "    \n",
    "    # ignore hidden files\n",
    "    images = [image for image in images if image[0]!='.']\n",
    "    \n",
    "    random.shuffle(images)\n",
    "    for image in images[:20]:\n",
    "        shutil.copy(f'Font/all/{folder}/{image}', f'Font/val/{folder}/{image}')\n",
    "        \n",
    "    for image in images[20:]:\n",
    "        shutil.copy(f'Font/all/{folder}/{image}', f'Font/train/{folder}/{image}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Task 4: Creating Data Loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "transforms_set = transforms.Compose([\n",
    "    transforms.Grayscale(num_output_channels=1),\n",
    "    transforms.Resize(size=50),\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.ImageFolder('Font/train', transform=transforms_set),\n",
    "    batch_size=batch_size, shuffle=True)\n",
    "val_loader = torch.utils.data.DataLoader(\n",
    "    datasets.ImageFolder('Font/val', transform=transforms_set),\n",
    "    batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Task 5: VAE Architecture and Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "class VAE(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(VAE, self).__init__()\n",
    "    \n",
    "        z = 32\n",
    "        \n",
    "        self.fc1 = nn.Linear(50*50, 1000)\n",
    "        self.fc21 = nn.Linear(1000, z)\n",
    "        self.fc22 = nn.Linear(1000, z)\n",
    "        self.fc3 = nn.Linear(z, 1000)\n",
    "        self.fc4 = nn.Linear(1000, 50*50)\n",
    "\n",
    "    def encode(self, x):\n",
    "        h1 = F.relu(self.fc1(x))\n",
    "        return self.fc21(h1), self.fc22(h1)\n",
    "\n",
    "    def reparameterize(self, mu, logvar):\n",
    "        std = torch.exp(0.5*logvar)\n",
    "        eps = torch.randn_like(std)\n",
    "        return mu + eps*std\n",
    "\n",
    "    def decode(self, z):\n",
    "        h3 = F.relu(self.fc3(z))\n",
    "        return torch.sigmoid(self.fc4(h3))\n",
    "\n",
    "    def forward(self, x):\n",
    "        mu, logvar = self.encode(x.view(-1, 50*50))\n",
    "        z = self.reparameterize(mu, logvar)\n",
    "        return self.decode(z), mu, logvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def loss_function(recon_x, x, mu, logvar):\n",
    "    \n",
    "    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 50*50), reduction='sum')\n",
    "\n",
    "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
    "\n",
    "    return BCE + KLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "model = VAE().to(device)\n",
    "\n",
    "optimizer = optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Task 6: Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def evaluate(evaluate_data=val_loader):\n",
    "    model.eval()\n",
    "    val_loss = 0\n",
    "    with torch.no_grad():\n",
    "        for i, (data, _) in enumerate(evaluate_data):\n",
    "            \n",
    "            data = data.to(device)\n",
    "            \n",
    "            recon_batch, mu, logvar = model(data)\n",
    "            \n",
    "            val_loss += loss_function(recon_batch, data, mu, logvar).item()\n",
    "            \n",
    "            if i == 0:\n",
    "                n = min(data.size(0), 16)\n",
    "                comparison = torch.cat([data[:n],\n",
    "                                      recon_batch.view(batch_size, 1, 50, 50)[:n]])\n",
    "                save_image(comparison.cpu(),\n",
    "                         'Results/reconstruction_' + str(epoch) + '.png', nrow=n)\n",
    "\n",
    "    val_loss /= len(evaluate_data.dataset)\n",
    "    return val_loss\n",
    "\n",
    "\n",
    "def sample_latent_space(epoch):\n",
    "    with torch.no_grad():\n",
    "        sample = torch.randn(64, 32).to(device)\n",
    "        sample = model.decode(sample).cpu()\n",
    "        save_image(sample.view(64, 1, 50, 50),\n",
    "                   'Results/sample_' + str(epoch) + '.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def train(epoch):\n",
    "\n",
    "    model.train()\n",
    "    train_loss = 0\n",
    "    \n",
    "    progress_bar = tqdm(train_loader, desc='Epoch {:03d}'.format(epoch), leave=False, disable=False)\n",
    "    for data, _ in progress_bar:\n",
    "        \n",
    "        data = data.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        recon_batch, mu, logvar = model(data)\n",
    "        \n",
    "        loss = loss_function(recon_batch, data, mu, logvar)\n",
    "        loss.backward()\n",
    "        train_loss += loss.item()\n",
    "        \n",
    "        optimizer.step()\n",
    "\n",
    "        progress_bar.set_postfix({'training_loss': '{:.3f}'.format(loss.item() / len(data))})\n",
    "\n",
    "    average_train_loss = train_loss / len(train_loader.dataset)\n",
    "    tqdm.write('Training set loss (average, epoch {:03d}): {:.3f}'.format(epoch, average_train_loss))\n",
    "    val_loss = evaluate(val_loader)\n",
    "    tqdm.write('\\t\\t\\t\\t====> Validation set loss: {:.3f}'.format(val_loss))\n",
    "\n",
    "    train_losses.append(average_train_loss)\n",
    "    val_losses.append(val_loss)\n",
    "    \n",
    "    if epoch%50==0:\n",
    "        torch.save(model.state_dict(), f'Models/epoch_{epoch}.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "epochs=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch 001', max=965.0, style=ProgressStyle(description_wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set loss (average, epoch 001): 266.831\n",
      "\t\t\t\t====> Validation set loss: 266.324\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch 002', max=965.0, style=ProgressStyle(description_wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set loss (average, epoch 002): 265.057\n",
      "\t\t\t\t====> Validation set loss: 263.595\n"
     ]
    }
   ],
   "source": [
    "train_losses = []\n",
    "val_losses = []\n",
    "\n",
    "for epoch in range(1, epochs+1):\n",
    "    train(epoch)\n",
    "    sample_latent_space(epoch)\n",
    "    \n",
    "np.savetxt('Models/training_losses.txt', np.array(train_losses), delimiter='\\n')\n",
    "np.savetxt('Models/validation_losses.txt', np.array(val_losses), delimiter='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('Presaved_Models/epoch_950.model', map_location=torch.device('cpu')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "for i in range(100):\n",
    "    sample_latent_space(f'-{i}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Task 7: Results and Other Uses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "train_losses = np.loadtxt('Presaved_Models/training_losses.txt')\n",
    "val_losses = np.loadtxt('Presaved_Models/validation_losses.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
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    "plt.plot(\n",
    "    range(1, len(train_losses)+1), \n",
    "    train_losses, \n",
    "    label='Training Loss',\n",
    "    linewidth=2, \n",
    "    alpha=0.7\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    range(1, len(val_losses)+1),\n",
    "    val_losses,\n",
    "    label='Validation Loss',\n",
    "    linewidth=2,\n",
    "    alpha=0.7\n",
    ")\n",
    "\n",
    "plt.title('VAE Font Training')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "After 950 epochs: \n",
    "<img src=\"Presaved_Results/reconstruction_950.png\">\n",
    "\n",
    "After 950 epochs: \n",
    "<img src=\"Presaved_Results/sample_950.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Alternative Font Reconstruction (2-D Latent Space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "After 1000 epochs: <img src=\"Presaved_Results/reconstruction_1000_2dim.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### CelebA Reconstructions with 500-D Latent Space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "[CelebA Dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)\n",
    "\n",
    "After 100 epochs:\n",
    "<img src=\"Celebs/reconstruction_100.png\">\n",
    "\n",
    "After 200 epochs:\n",
    "<img src=\"Celebs/reconstruction_200.png\">\n",
    "\n",
    "After 300 epochs:\n",
    "<img src=\"Celebs/reconstruction_300.png\">\n",
    "\n",
    "After 400 epochs:\n",
    "<img src=\"Celebs/reconstruction_400.png\">\n",
    "\n",
    "After 500 epochs:\n",
    "<img src=\"Celebs/reconstruction_500.png\">\n",
    "\n",
    "After 600 epochs:\n",
    "<img src=\"Celebs/reconstruction_600.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  }
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