wasserstein-distance/Semi-Improved_Training_of_Wasserstein_GAN.ipynb

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    "# Semi-Improved Training of Wasserstein GANs\n",
    "\n",
    "by Thomas Viehmann, tv@lernapparat.de\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Wasserstein GANs have been introduced in [Arjovsky et al: Wasserstein GAN](https://arxiv.org/abs/1701.07875).\n",
    "There also is a [great summary of the article by Alex Irpan](http://www.alexirpan.com/2017/02/22/wasserstein-gan.html).\n",
    "\n",
    "Recently, [Gulrajani et al: Improved Training of Wasserstein GANs](https://www.arxiv.org/abs/1704.00028) added a relaxed constraint to the original Wasserstein GAN discriminator training objective. I wrote my take at what is up with that in [Geometric Intuition on Improved Wasserstein GANs](https://lernapparat.de/improved-wasserstein-gan/).\n",
    "\n",
    "Looking at the code accompanying the latter, I noticed that the penalty actually samples a single interpolated point per real-fake data pair and computed the gradient there. Having finished my writeup linked above, this - and the fact that [pytorch did not support second derivatives](https://github.com/martinarjovsky/WassersteinGAN/issues/36) - led me to think that maybe one could take a step back - namely stick with $|f(x)-f(y)| = |x-y|$ instead of testing $|\\nabla f| = 1$ (see the [*back of the envelope* section](https://lernapparat.de/improved-wasserstein-gan/) for details).\n",
    "\n",
    "## Semi-Improved Training of Wasserstein GANs\n",
    "As this cuts one step from *Improved Training of Wasserstein GANs* I call this method *Semi-Improved Training of Wasserstein GANs*.\n",
    "Below we implement two variants:\n",
    "- Variant 1 takes $x$ and $y$ as a pair of real and fake data and applies the same penalty as *Improved Training*: $p(x,y) = \\lambda (1-|f(x)-f(y)|/|x-y|)^2$.\n",
    "- Variant 2 takes $x$ and $y$, and a randomly chosen interpolation point $z=\\alpha x+(1-\\alpha) y$ (just like the point where *Improved Training* evaluates $|\\nabla f|$). It then uses $(p(x,z)+p(y,z))/2$ as the penalty term.\n",
    "\n",
    "As in improved training, these replace the parameter clipping of the original Wasserstein GAN.\n",
    "\n",
    "This being github and not arxiv, we apply this new method to the toy examples of *Improved Training* and argue that the *Semi-Improved Training* achieves comparable results.\n",
    "\n",
    "## Acknowledgement\n",
    "\n",
    "This notebook borrows heavily from the code accompanying the two papers, namely\n",
    "- https://github.com/martinarjovsky/WassersteinGAN (initialisation, loss calculation and training loop)\n",
    "- https://github.com/igul222/improved_wgan_training (toy problem data generation and network structure, visualization)\n",
    "\n",
    "Please see the licenses there and refer to the above when using their code.\n",
    "I applaud publishing the code with the papers. This was crucial in enabling this exercise.\n",
    "\n",
    "## Enough talk, let's see the code and pictures below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_id": "E891EBA623F548959E5D5309A902D3C6"
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   "outputs": [],
   "source": [
    "# import everything\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable\n",
    "import os\n",
    "import numpy\n",
    "import sklearn.datasets\n",
    "\n",
    "from matplotlib import pyplot\n",
    "%matplotlib inline\n",
    "import IPython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cell_id": "D87035FD9B864129B2DD8E76869FB2F1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Seed:  15042017\n"
     ]
    }
   ],
   "source": [
    "# set up parameters, a blend of Wasserstein GAN and Improved Training's code\n",
    "\n",
    "lipschitz_constraint = 1 # 0: original WGAN, 1: method 1 from above, 2: method 2 form above\n",
    "penalty_weight = 0.1     # penalty weight term lambda \n",
    "\n",
    "opt_dataset = '25gaussians' # 8gaussians | swissroll | 25gaussians\n",
    "opt_niter = 10000\n",
    "opt_batchSize=256\n",
    "opt_lrD = 0.00005 # learning rate for Critic, default=0.00005\n",
    "opt_lrG = 0.00005 # learning rate for Generator, default=0.00005\n",
    "opt_beta1=0.5 # beta1 for adam. default=0.5\n",
    "opt_cuda = True\n",
    "opt_clamp_lower = -0.01 #default -0.01\n",
    "opt_clamp_upper =  0.01 #default  0.01\n",
    "opt_Diters = 5 # number of D iters per each G iter\n",
    "opt_adam = True  # Whether to use adam (default False is rmsprop)\n",
    "opt_prefix = None # whether to write images (=prefix of type string) or show in notebook (=None)\n",
    "\n",
    "opt_manualSeed = 15042017\n",
    "print(\"Random Seed: \", opt_manualSeed)\n",
    "random.seed(opt_manualSeed)\n",
    "numpy.random.seed(opt_manualSeed)\n",
    "torch.manual_seed(opt_manualSeed)\n",
    "cudnn.benchmark = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cell_id": "5FF6184244104ADF9D0A3A8FADFD2290"
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   "outputs": [],
   "source": [
    "# set up variables (torch administrativa)\n",
    "\n",
    "input = torch.FloatTensor(opt_batchSize, 2)\n",
    "input2 = torch.FloatTensor(opt_batchSize, 2)\n",
    "interp_alpha = torch.FloatTensor(opt_batchSize, 1)\n",
    "noise = torch.FloatTensor(opt_batchSize, 2)\n",
    "fixed_noise = torch.FloatTensor(opt_batchSize, 2).normal_(0, 1)\n",
    "\n",
    "if opt_cuda:\n",
    "    input = input.cuda()\n",
    "    input2 = input2.cuda()\n",
    "    interp_alpha = interp_alpha.cuda()\n",
    "    noise, fixed_noise = noise.cuda(), fixed_noise.cuda()\n",
    "\n",
    "input = Variable(input)\n",
    "input2 = Variable(input2)\n",
    "noise = Variable(noise)\n",
    "interp_alpha = Variable(interp_alpha)\n",
    "fixed_noise = Variable(fixed_noise)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cell_id": "765ABDEF6838476D8DA9E408AEAB3805"
   },
   "outputs": [],
   "source": [
    "# Dataset generator largely form Improved Training of Wasserstein GAN code (see link above)\n",
    "def inf_train_gen(DATASET='8gaussians', BATCH_SIZE=opt_batchSize):\n",
    "    if DATASET == '25gaussians':\n",
    "        dataset = []\n",
    "        for i in range(100000//25):\n",
    "            for x in range(-2, 3):\n",
    "                for y in range(-2, 3):\n",
    "                    point = numpy.random.randn(2)*0.05\n",
    "                    point[0] += 2*x\n",
    "                    point[1] += 2*y\n",
    "                    dataset.append(point)\n",
    "        dataset = numpy.array(dataset, dtype='float32')\n",
    "        numpy.random.shuffle(dataset)\n",
    "        dataset /= 2.828 # stdev\n",
    "        while True:\n",
    "            for i in range(len(dataset)//BATCH_SIZE):\n",
    "                yield torch.from_numpy(dataset[i*BATCH_SIZE:(i+1)*BATCH_SIZE])\n",
    "\n",
    "    elif DATASET == 'swissroll':\n",
    "\n",
    "        while True:\n",
    "            data = sklearn.datasets.make_swiss_roll(\n",
    "                n_samples=BATCH_SIZE, \n",
    "                noise=0.25\n",
    "            )[0]\n",
    "            data = data.astype('float32')[:, [0, 2]]\n",
    "            data /= 7.5 # stdev plus a little\n",
    "            yield torch.from_numpy(data)\n",
    "\n",
    "    elif DATASET == '8gaussians':\n",
    "    \n",
    "        scale = 2.\n",
    "        centers = [\n",
    "            (1,0),\n",
    "            (-1,0),\n",
    "            (0,1),\n",
    "            (0,-1),\n",
    "            (1./numpy.sqrt(2), 1./numpy.sqrt(2)),\n",
    "            (1./numpy.sqrt(2), -1./numpy.sqrt(2)),\n",
    "            (-1./numpy.sqrt(2), 1./numpy.sqrt(2)),\n",
    "            (-1./numpy.sqrt(2), -1./numpy.sqrt(2))\n",
    "        ]\n",
    "        centers = [(scale*x,scale*y) for x,y in centers]\n",
    "        while True:\n",
    "            dataset = []\n",
    "            for i in range(BATCH_SIZE):\n",
    "                point = numpy.random.randn(2)*.02\n",
    "                center = random.choice(centers)\n",
    "                point[0] += center[0]\n",
    "                point[1] += center[1]\n",
    "                dataset.append(point)\n",
    "            dataset = numpy.array(dataset, dtype='float32')\n",
    "            dataset /= 1.414 # stdev\n",
    "            yield torch.from_numpy(dataset)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cell_id": "C048557935C34DA89C5177D169F18C7E"
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   "outputs": [],
   "source": [
    "# Generates and saves a plot of the true distribution, the generator, and the\n",
    "# critic.\n",
    "# largely form Improved Training of Wasserstein GAN code (see link above)\n",
    "class ImageGenerator:\n",
    "  def __init__(self, netG, netD, prefix='frame', noise_dim=2):\n",
    "    self.prefix = prefix\n",
    "    self.frame_index = 1\n",
    "    self.noise_dim = noise_dim\n",
    "    self.netG = netG\n",
    "    self.netD = netD\n",
    "    \n",
    "  def __call__(self, true_dist,losses):\n",
    "    N_POINTS = 128\n",
    "    RANGE = 3\n",
    "\n",
    "    points = numpy.zeros((N_POINTS, N_POINTS, 2), dtype='float32')\n",
    "    points[:,:,0] = numpy.linspace(-RANGE, RANGE, N_POINTS)[:,None]\n",
    "    points[:,:,1] = numpy.linspace(-RANGE, RANGE, N_POINTS)[None,:]\n",
    "    points = points.reshape((-1,2))\n",
    "    points = Variable(torch.from_numpy(points).cuda())\n",
    "    \n",
    "    noise = torch.FloatTensor().resize_(batch_size, 2)\n",
    "    noise.normal_(0, 1)\n",
    "    noise = Variable(noise.cuda())\n",
    "\n",
    "    fake = self.netG(noise)\n",
    "    samples = fake.data.cpu().numpy()\n",
    "\n",
    "    disc_points = self.netD(points)\n",
    "    \n",
    "    disc_map = disc_points.data.cpu().numpy()\n",
    "    pyplot.clf()\n",
    "    if self.prefix is None:\n",
    "        #pyplot.suplots(nrows=1, ncols=2)\n",
    "        pyplot.subplot(1,2,1)\n",
    "    x = y = numpy.linspace(-RANGE, RANGE, N_POINTS)\n",
    "    pyplot.contour(x,y,disc_map.reshape((len(x), len(y))))\n",
    "\n",
    "    true_dist = true_dist.cpu().numpy()\n",
    "    pyplot.scatter(true_dist[:, 0], true_dist[:, 1], c='orange',marker='+')\n",
    "    pyplot.scatter(samples[:, 0],   samples[:, 1],   c='green', marker='+')\n",
    "    if self.prefix is not None:\n",
    "      pyplot.savefig(self.prefix+'{:05d}'.format(self.frame_index)+'.jpg')\n",
    "    else:\n",
    "      pyplot.subplot(1,2,2)\n",
    "      pyplot.plot(losses)\n",
    "      IPython.display.clear_output(wait=True)\n",
    "      IPython.display.display(pyplot.gcf())\n",
    "    self.frame_index += 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "D1D69F416D1348FE8F88FD8307B124A3",
    "collapsed": true
   },
   "source": [
    "Define the Generator and Critic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cell_id": "BBD450F7B0F846F99242E21B23678366"
   },
   "outputs": [],
   "source": [
    "class ToyGAN_G(nn.Module):\n",
    "    def __init__(self, dim_hidden=512, dim_out=2, noise_dim=2):\n",
    "        super(ToyGAN_G, self).__init__()\n",
    "        self.dim_hidden, self.dim_out, self.noise_dim = dim_hidden, dim_out, noise_dim\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(noise_dim, dim_hidden),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(dim_hidden, dim_hidden),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(dim_hidden, dim_hidden),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(dim_hidden, dim_out)\n",
    "            )\n",
    "    def forward(self, x): #?\n",
    "        x = self.net(x)\n",
    "        return x\n",
    "\n",
    "class ToyGAN_D(nn.Module):\n",
    "    def __init__(self, dim_hidden=512, dim_gen_out=2):\n",
    "        super(ToyGAN_D, self).__init__()\n",
    "        self.dim_hidden, self.dim_gen_out = dim_hidden, dim_gen_out\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(dim_gen_out, dim_hidden),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(dim_hidden, dim_hidden),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(dim_hidden, dim_hidden),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(dim_hidden, 1)\n",
    "            )\n",
    "    def forward(self, x): #?\n",
    "        x = self.net(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "2DE3E3C9FDCF4B9287310F76605C3E99"
   },
   "source": [
    "Do the training (code largely taken from Wasserstein GAN code, see link above)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cell_id": "7D33EB6F126244DC8D7E01CF800349CE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9999/10000] Loss_D: -0.351382 Loss_G: 60.771648 Loss_D_real: 60.694508 Loss_D_fake 61.236496 Loss_lip 0.190606\n"
     ]
    },
    {
     "data": {
      "image/png": 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yucwR0WEBXLpgFOHBvjzw0no2bj/O0hmuyzdbJidnkQT1WrOBYNm20F2IorgR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O4pzFu8wcu3A/RNSaKgtJzSWl9jlaRQudjooJ/+vkk0IiJoUmAFDJqsqo4Mk9/naQ2m5ulBa42n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\no2EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f281e550d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_generator = inf_train_gen(opt_dataset)\n",
    "netD = ToyGAN_D()\n",
    "netG = ToyGAN_G()\n",
    "netD.cuda()\n",
    "netG.cuda()\n",
    "\n",
    "generate_image = ImageGenerator(netG, netD, prefix=opt_prefix)\n",
    "\n",
    "if opt_adam:\n",
    "    optimizerD = optim.Adam(netD.parameters(), lr=opt_lrD, betas=(opt_beta1, 0.999))\n",
    "    optimizerG = optim.Adam(netG.parameters(), lr=opt_lrG, betas=(opt_beta1, 0.999))\n",
    "else:\n",
    "    optimizerD = optim.RMSprop(netD.parameters(), lr = opt_lrD)\n",
    "    optimizerG = optim.RMSprop(netG.parameters(), lr = opt_lrG)\n",
    "\n",
    "losses = []\n",
    "for batches in range(opt_niter):\n",
    "    ############################\n",
    "    # (1) Update D network\n",
    "    ###########################\n",
    "    for p in netD.parameters(): # reset requires_grad\n",
    "        p.requires_grad = True # they are set to False below in netG update\n",
    "\n",
    "    # train the discriminator Diters times\n",
    "    if batches < 25 or batches % 500 == 0:\n",
    "        Diters = 100\n",
    "    else:\n",
    "        Diters = opt_Diters\n",
    "    for j in range(Diters):\n",
    "\n",
    "        if lipschitz_constraint == 0:\n",
    "            # clamp parameters to a cube\n",
    "            for p in netD.parameters():\n",
    "                p.data.clamp_(opt_clamp_lower, opt_clamp_upper)\n",
    "\n",
    "        data = next(data_generator)\n",
    "\n",
    "        # train with real\n",
    "        real_cpu = data\n",
    "        netD.zero_grad()\n",
    "        batch_size = real_cpu.size(0)\n",
    "        input.data.resize_(real_cpu.size()).copy_(real_cpu)\n",
    "\n",
    "        errD_real_vec = netD(input).view(-1)\n",
    "        errD_real = errD_real_vec.mean(0).view(1)\n",
    "\n",
    "        # train with fake\n",
    "        noise.data.resize_(batch_size, 2)\n",
    "        noise.data.normal_(0, 1)\n",
    "        fake = netG(noise)\n",
    "        input2.data.resize_(fake.data.size()).copy_(fake.data)\n",
    "        errD_fake_vec = netD(input2).view(-1)\n",
    "        errD_fake = errD_fake_vec.mean(0).view(1)\n",
    "        errD = errD_real - errD_fake\n",
    "        # This is the semi-improved training penalty term to soft-enforce the Lipschitz 1 bound)\n",
    "        # Note how we conditionally retained variables and disabled the weight clipping above\n",
    "        if lipschitz_constraint == 1:\n",
    "            dist = ((input-input2)**2).sum(1)**0.5\n",
    "            lip_est = (errD_real_vec-errD_fake_vec).abs()/(dist+1e-8)\n",
    "            lip_loss = penalty_weight*((1.0-lip_est)**2).mean(0).view(1)\n",
    "            errD = errD + lip_loss\n",
    "        elif lipschitz_constraint == 2:\n",
    "            interp_alpha.data.resize_(batch_size, 1)\n",
    "            interp_alpha.data.uniform_()\n",
    "            dist = ((input-input2)**2).sum(1)**0.5\n",
    "            errD_interp_vec = netD(interp_alpha.expand_as(input)*input+(1-interp_alpha.expand_as(input))*input2)\n",
    "            lip_est1 = (errD_fake_vec-errD_interp_vec).abs()/(interp_alpha*dist+1e-8)\n",
    "            lip_est2 = (errD_real_vec-errD_interp_vec).abs()/((1.0-interp_alpha)*dist+1e-8)\n",
    "            lip_loss = 0.5*penalty_weight*(((1.0-lip_est1)**2).mean(0).view(1)+((1.0-lip_est2)**2).mean(0).view(1))\n",
    "            errD = errD + lip_loss\n",
    "            \n",
    "        errD.backward()\n",
    "        optimizerD.step()\n",
    "\n",
    "    ############################\n",
    "    # (2) Update G network\n",
    "    ###########################\n",
    "    for p in netD.parameters():\n",
    "        p.requires_grad = False # to avoid computation\n",
    "    netG.zero_grad()\n",
    "    # in case our last batch was the tail batch of the dataloader,\n",
    "    # make sure we feed a full batch of noise\n",
    "    noise.data.resize_(opt_batchSize, 2)\n",
    "    noise.data.normal_(0, 1)\n",
    "    fake = netG(noise)\n",
    "    errG = netD(fake)\n",
    "    errG = errG.mean(0).view(1)\n",
    "    errG.backward()\n",
    "    optimizerG.step()\n",
    "    if (batches+1) % 50 == 0:\n",
    "      losses.append(errD.data[0])\n",
    "      generate_image(data, losses)\n",
    "      print('[%d/%d] Loss_D: %f Loss_G: %f Loss_D_real: %f Loss_D_fake %f Loss_lip %f'\n",
    "        % (batches, opt_niter,\n",
    "        errD.data[0], errG.data[0], errD_real.data[0], errD_fake.data[0], lip_loss.data[0]))\n",
    "\n",
    "IPython.display.clear_output(wait=True)\n",
    "print('[%d/%d] Loss_D: %f Loss_G: %f Loss_D_real: %f Loss_D_fake %f Loss_lip %f'\n",
    "        % (batches, opt_niter,\n",
    "        errD.data[0], errG.data[0], errD_real.data[0], errD_fake.data[0], lip_loss.data[0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "1E84A01FDEF64083873B362BD5734297"
   },
   "source": [
    "Things to try:\n",
    "- Apply Semi-Improved method to the real datasets of the articles.\n",
    "- Possibly increase penalty during the course of the training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "53DFCB1FE3624BF997EDACBCCF8AEF37",
    "collapsed": true
   },
   "source": [
    "I hope you enjoyed this little demo.\n",
    "I appreciate your feedback: Thomas Viehmann, tv@lernapparat.de"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "B9D0D9D95F7C4E4599F1A57491B30A7E"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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