pytorch_ipynb/gan/wgan-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.7.3\n",
      "IPython 7.9.0\n",
      "\n",
      "torch 1.7.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Runs on CPU or GPU (if available)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Zoo -- Wasserstein Generative Adversarial Networks (GAN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of a very simple/rudimentary Wasserstein GAN using just fully connected layers.\n",
    "\n",
    "The Wasserstein GAN is based on the paper\n",
    "\n",
    "- Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. arXiv preprint arXiv:1701.07875. (https://arxiv.org/abs/1701.07875)\n",
    "\n",
    "The main differences to a regular GAN are annotated in the code. In short, the main differences are \n",
    "\n",
    "1. Not using a sigmoid activation function and just using a linear output layer for the critic (i.e., discriminator).\n",
    "2. Using label -1 instead of 1 for the real images; using label 1 instead of 0 for fake images.\n",
    "3. Using Wasserstein distance (loss) for training both the critic and the generator.\n",
    "4. After each weight update, clip the weights to be in range [-0.1, 0.1].\n",
    "5. Train the critic 5 times for each generator training update.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image batch dimensions: torch.Size([128, 1, 28, 28])\n",
      "Image label dimensions: torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Device\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Hyperparameters\n",
    "random_seed = 0\n",
    "generator_learning_rate = 0.0005\n",
    "discriminator_learning_rate = 0.0005\n",
    "NUM_EPOCHS = 100\n",
    "BATCH_SIZE = 128\n",
    "LATENT_DIM = 50\n",
    "IMG_SHAPE = (1, 28, 28)\n",
    "IMG_SIZE = 1\n",
    "for x in IMG_SHAPE:\n",
    "    IMG_SIZE *= x\n",
    "\n",
    "## WGAN-specific settings\n",
    "num_iter_critic = 5\n",
    "weight_clip_value = 0.01\n",
    "\n",
    "\n",
    "\n",
    "##########################\n",
    "### MNIST DATASET\n",
    "##########################\n",
    "\n",
    "# Note transforms.ToTensor() scales input images\n",
    "# to 0-1 range\n",
    "train_dataset = datasets.MNIST(root='data', \n",
    "                               train=True, \n",
    "                               transform=transforms.ToTensor(),\n",
    "                               download=True)\n",
    "\n",
    "test_dataset = datasets.MNIST(root='data', \n",
    "                              train=False, \n",
    "                              transform=transforms.ToTensor())\n",
    "\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset, \n",
    "                          batch_size=BATCH_SIZE, \n",
    "                          shuffle=True)\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset, \n",
    "                         batch_size=BATCH_SIZE, \n",
    "                         shuffle=False)\n",
    "\n",
    "# Checking the dataset\n",
    "for images, labels in train_loader:  \n",
    "    print('Image batch dimensions:', images.shape)\n",
    "    print('Image label dimensions:', labels.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################\n",
    "### MODEL\n",
    "##########################\n",
    "\n",
    "\n",
    "def wasserstein_loss(y_true, y_pred):\n",
    "    return torch.mean(y_true * y_pred)\n",
    "\n",
    "\n",
    "class GAN(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(GAN, self).__init__()\n",
    "        \n",
    "        \n",
    "        self.generator = nn.Sequential(\n",
    "            nn.Linear(LATENT_DIM, 128),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            #nn.Dropout(p=0.5),\n",
    "            nn.Linear(128, IMG_SIZE),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "        \n",
    "        self.discriminator = nn.Sequential(\n",
    "            nn.Linear(IMG_SIZE, 128),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            #nn.Dropout(p=0.5),\n",
    "            nn.Linear(128, 1),\n",
    "            #nn.Sigmoid() # WGAN should have linear activation\n",
    "        )\n",
    "\n",
    "            \n",
    "    def generator_forward(self, z):\n",
    "        img = self.generator(z)\n",
    "        return img\n",
    "    \n",
    "    def discriminator_forward(self, img):\n",
    "        pred = model.discriminator(img)\n",
    "        return pred.view(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(random_seed)\n",
    "\n",
    "model = GAN()\n",
    "model = model.to(device)\n",
    "\n",
    "optim_gener = torch.optim.Adam(model.generator.parameters(), lr=generator_learning_rate)\n",
    "optim_discr = torch.optim.Adam(model.discriminator.parameters(), lr=discriminator_learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Time elapsed: 21.64 min\n",
      "Total Training Time: 21.64 min\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()    \n",
    "\n",
    "discr_costs = []\n",
    "gener_costs = []\n",
    "for epoch in range(NUM_EPOCHS):\n",
    "    model = model.train()\n",
    "    for batch_idx, (features, targets) in enumerate(train_loader):\n",
    "\n",
    "        \n",
    "        \n",
    "        features = (features - 0.5)*2.\n",
    "        features = features.view(-1, IMG_SIZE).to(device) \n",
    "        targets = targets.to(device)\n",
    "\n",
    "        # Regular GAN:\n",
    "        # valid = torch.ones(targets.size(0)).float().to(device)\n",
    "        # fake = torch.zeros(targets.size(0)).float().to(device)\n",
    "        \n",
    "        # WGAN:\n",
    "        valid = -(torch.ones(targets.size(0)).float()).to(device)\n",
    "        fake = torch.ones(targets.size(0)).float().to(device)\n",
    "        \n",
    "\n",
    "        ### FORWARD AND BACK PROP\n",
    "        \n",
    "        \n",
    "        # --------------------------\n",
    "        # Train Generator\n",
    "        # --------------------------\n",
    "        \n",
    "        # Make new images\n",
    "        z = torch.zeros((targets.size(0), LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
    "        generated_features = model.generator_forward(z)\n",
    "        \n",
    "        # Loss for fooling the discriminator\n",
    "        discr_pred = model.discriminator_forward(generated_features)\n",
    "        \n",
    "        \n",
    "        # Regular GAN:\n",
    "        # gener_loss = F.binary_cross_entropy_with_logits(discr_pred, valid)\n",
    "        \n",
    "        # WGAN:\n",
    "        gener_loss = wasserstein_loss(valid, discr_pred)\n",
    "        \n",
    "        optim_gener.zero_grad()\n",
    "        gener_loss.backward()\n",
    "        optim_gener.step()\n",
    "        \n",
    "        # --------------------------\n",
    "        # Train Discriminator\n",
    "        # --------------------------        \n",
    "\n",
    "        \n",
    "        # WGAN: 5 loops for discriminator\n",
    "        for _ in range(num_iter_critic):\n",
    "        \n",
    "            discr_pred_real = model.discriminator_forward(features.view(-1, IMG_SIZE))\n",
    "            # Regular GAN:\n",
    "            # real_loss = F.binary_cross_entropy_with_logits(discr_pred_real, valid)\n",
    "            # WGAN:\n",
    "            real_loss = wasserstein_loss(valid, discr_pred_real)\n",
    "\n",
    "            discr_pred_fake = model.discriminator_forward(generated_features.detach())\n",
    "\n",
    "            # Regular GAN:\n",
    "            # fake_loss = F.binary_cross_entropy_with_logits(discr_pred_fake, fake)\n",
    "            # WGAN:\n",
    "            fake_loss = wasserstein_loss(fake, discr_pred_fake)\n",
    "\n",
    "            # Regular GAN:\n",
    "            discr_loss = (real_loss + fake_loss)\n",
    "            # WGAN:\n",
    "            #discr_loss = -(real_loss - fake_loss)\n",
    "\n",
    "            optim_discr.zero_grad()\n",
    "            discr_loss.backward()\n",
    "            optim_discr.step()        \n",
    "\n",
    "            # WGAN:\n",
    "            for p in model.discriminator.parameters():\n",
    "                p.data.clamp_(-weight_clip_value, weight_clip_value)\n",
    "\n",
    "        \n",
    "        discr_costs.append(discr_loss.item())\n",
    "        gener_costs.append(gener_loss.item())\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        ### LOGGING\n",
    "        if not batch_idx % 100:\n",
    "            print ('Epoch: %03d/%03d | Batch %03d/%03d | Gen/Dis Loss: %.4f/%.4f' \n",
    "                   %(epoch+1, NUM_EPOCHS, batch_idx, \n",
    "                     len(train_loader), gener_loss, discr_loss))\n",
    "\n",
    "    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
    "    \n",
    "print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(1, 1, 1)\n",
    "ax1.plot(range(len(gener_costs)), gener_costs, label='Generator loss')\n",
    "ax1.plot(range(len(discr_costs)), discr_costs, label='Discriminator loss')\n",
    "ax1.set_xlabel('Iterations')\n",
    "ax1.set_ylabel('Loss')\n",
    "ax1.legend()\n",
    "\n",
    "###################\n",
    "# Set scond x-axis\n",
    "ax2 = ax1.twiny()\n",
    "newlabel = list(range(NUM_EPOCHS+1))\n",
    "iter_per_epoch = len(train_loader)\n",
    "newpos = [e*iter_per_epoch for e in newlabel]\n",
    "\n",
    "ax2.set_xticklabels(newlabel[::10])\n",
    "ax2.set_xticks(newpos[::10])\n",
    "\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_label_position('bottom')\n",
    "ax2.spines['bottom'].set_position(('outward', 45))\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax2.set_xlim(ax1.get_xlim())\n",
    "###################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x180 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##########################\n",
    "### VISUALIZATION\n",
    "##########################\n",
    "\n",
    "\n",
    "model.eval()\n",
    "# Make new images\n",
    "z = torch.zeros((5, LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
    "generated_features = model.generator_forward(z)\n",
    "imgs = generated_features.view(-1, 28, 28)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(20, 2.5))\n",
    "\n",
    "\n",
    "for i, ax in enumerate(axes):\n",
    "    axes[i].imshow(imgs[i].to(torch.device('cpu')).detach(), cmap='binary')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}