homeworks/homework6/hw6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qCNBV60b8CHX"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.utils.data as data\n",
    "from torchvision.utils import make_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ftMgDK_cPFmD"
   },
   "outputs": [],
   "source": [
    "def plot_gan_training(losses, title):\n",
    "    n_itr = len(losses)\n",
    "    xs = np.arange(n_itr)\n",
    "\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    plt.plot(xs, losses)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Iterations')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p_mQWXNLGZxp"
   },
   "source": [
    "# Task 1 (4 points): Vanila GAN for 1d task\n",
    "\n",
    "In this task you will train simple GAN model for 1d distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2aSnhquU8zUk"
   },
   "outputs": [],
   "source": [
    "def generate_1d_data(count):\n",
    "    gaussian1 = np.random.normal(loc=-1, scale=0.25, size=(count // 2,))\n",
    "    gaussian2 = np.random.normal(loc=0.5, scale=0.5, size=(count // 2,))\n",
    "    data = (np.concatenate([gaussian1, gaussian2]) + 1).reshape([-1, 1])\n",
    "    return (data - data.min()) / (data.max() - data.min())\n",
    "\n",
    "\n",
    "def visualize_1d_data(data):\n",
    "    plt.figure(figsize=(7, 4))\n",
    "    plt.hist(data, bins=50)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "FZWKQr5R-3wF",
    "outputId": "728b044a-88ff-4829-e6f4-c85496455ce3"
   },
   "outputs": [],
   "source": [
    "COUNT = 20000\n",
    "\n",
    "train_data = generate_1d_data(COUNT)\n",
    "visualize_1d_data(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Np-gRwq9-7HT"
   },
   "outputs": [],
   "source": [
    "def make_step(loss_fn, x, generator, discriminator, optimizer):\n",
    "    loss = loss_fn(generator, discriminator, x)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    return loss\n",
    "\n",
    "\n",
    "def train_epoch(\n",
    "    generator, \n",
    "    discriminator, \n",
    "    gen_loss_fn, \n",
    "    discr_loss_fn, \n",
    "    train_loader, \n",
    "    gen_optimizer, \n",
    "    discr_optimizer, \n",
    "    discr_steps=1\n",
    "):\n",
    "    generator.train()\n",
    "    discriminator.train()\n",
    "\n",
    "    gen_losses, discr_losses = [], []\n",
    "    for idx, x in enumerate(train_loader):\n",
    "        x = x.cuda().float()\n",
    "        discr_loss = make_step(discr_loss_fn, x, generator, discriminator, discr_optimizer)\n",
    "        discr_losses.append(discr_loss.item())\n",
    "\n",
    "        if idx % discr_steps == 0:\n",
    "            gen_loss = make_step(gen_loss_fn, x, generator, discriminator, gen_optimizer)\n",
    "            gen_losses.append(gen_loss.item())\n",
    "    return {\n",
    "        'generator_losses': gen_losses,\n",
    "        'discriminator_losses': discr_losses\n",
    "    }\n",
    "\n",
    "\n",
    "def train_model(\n",
    "    generator, \n",
    "    discriminator, \n",
    "    gen_loss_fn, \n",
    "    discr_loss_fn, \n",
    "    train_loader, \n",
    "    epochs,\n",
    "    lr,\n",
    "    discr_steps=1\n",
    "):\n",
    "    gen_optimizer = optim.Adam(generator.parameters(), lr=lr, betas=(0, 0.9))\n",
    "    discr_optimizer = optim.Adam(discriminator.parameters(), lr=lr, betas=(0, 0.9))\n",
    "\n",
    "    train_losses = {}\n",
    "    for epoch in range(epochs):\n",
    "        train_loss = train_epoch(\n",
    "            generator, \n",
    "            discriminator, \n",
    "            gen_loss_fn, \n",
    "            discr_loss_fn,\n",
    "            train_loader, \n",
    "            gen_optimizer, \n",
    "            discr_optimizer, \n",
    "            discr_steps=discr_steps\n",
    "        )\n",
    "        \n",
    "        for k in train_loss.keys():\n",
    "            if k not in train_losses:\n",
    "                train_losses[k] = []\n",
    "            train_losses[k].extend(train_loss[k])\n",
    "\n",
    "    return train_losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rDRuBWI5HJvl"
   },
   "source": [
    "Generator and Discriminator models are simple MLP models.\n",
    "\n",
    "The objective function is \n",
    "$$min_{G} max_{D} \\mathbb{E}_{\\mathbf{x} \\sim \\pi(\\mathbf{x})} [\\log D(\\mathbf{x})] + \\mathbb{E}_{\\mathbf{z} \\sim p(\\mathbf{z})}[\\log (1-D(G(\\mathbf{z})))]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "usTdzZirKoyE"
   },
   "outputs": [],
   "source": [
    "class FullyConnectedMLP(nn.Module):\n",
    "    # do not change this class\n",
    "    def __init__(self, input_dim, hiddens, output_dim):\n",
    "        assert isinstance(hiddens, list)\n",
    "        super().__init__()\n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.hiddens = hiddens\n",
    "        \n",
    "        \n",
    "        # ====\n",
    "        # your code\n",
    "        model = []\n",
    "        self.net = nn.Sequential(*model)\n",
    "        # ====\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = x.shape[0]\n",
    "        x = x.view(batch_size, -1)\n",
    "        return self.net(x).view(batch_size, self.output_dim)\n",
    "\n",
    "\n",
    "class MLPGenerator(nn.Module):\n",
    "    def __init__(self, latent_dim, hiddens, data_dim):\n",
    "        super().__init__()\n",
    "        self.latent_dim = latent_dim\n",
    "        self.mlp = FullyConnectedMLP(latent_dim, hiddens, data_dim)\n",
    "    \n",
    "    def forward(self, z):\n",
    "        # ====\n",
    "        # your code\n",
    "        # apply mlp and (!)sigmoid activation\n",
    "        \n",
    "        # ====\n",
    "        \n",
    "    def sample(self, n):\n",
    "        # ====\n",
    "        # your code\n",
    "        # sample from uniform and apply the model\n",
    "\n",
    "        # ====\n",
    "\n",
    "class MLPDiscriminator(nn.Module):\n",
    "    def __init__(self, latent_dim, hiddens, data_dim):\n",
    "        super().__init__()\n",
    "        self.mlp = FullyConnectedMLP(latent_dim, hiddens, data_dim)\n",
    "    \n",
    "    def forward(self, z):\n",
    "        # ====\n",
    "        # your code\n",
    "        # apply mlp and sigmoid activation\n",
    "        \n",
    "        # ===="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qcQEyAkCMERX"
   },
   "outputs": [],
   "source": [
    "# ====\n",
    "# your code\n",
    "# choose these parameters\n",
    "BATCH_SIZE = \n",
    "GEN_HIDDENS = \n",
    "DISCR_HIDDENS = \n",
    "EPOCHS = \n",
    "LR = \n",
    "DISCR_STEPS =\n",
    "# ====\n",
    "\n",
    "train_loader = data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)\n",
    "\n",
    "# model\n",
    "generator = MLPGenerator(latent_dim=1, hiddens=GEN_HIDDENS, data_dim=1).cuda()\n",
    "discriminator = MLPDiscriminator(latent_dim=1, hiddens=DISCR_HIDDENS, data_dim=1).cuda()\n",
    "\n",
    "\n",
    "def gen_loss(generator, discriminator, x):\n",
    "    # ====\n",
    "    # your code\n",
    "    # sample data from generator (number of samples = x.shape[0])\n",
    "    fake_data = \n",
    "    return # log(1 - D(x))\n",
    "    # ====\n",
    "\n",
    "\n",
    "def discr_loss(generator, discriminator, x):\n",
    "    # ====\n",
    "    # your code\n",
    "    # sample data from generator (number of samples = x.shape[0])\n",
    "    fake_data = \n",
    "    return # - log(D(x_real))- log(1 - D(x_fake))\n",
    "    # ====\n",
    "    \n",
    "# train\n",
    "train_losses = train_model(\n",
    "    generator, \n",
    "    discriminator, \n",
    "    gen_loss, \n",
    "    discr_loss, \n",
    "    train_loader, \n",
    "    epochs=EPOCHS,\n",
    "    lr=LR,\n",
    "    discr_steps=DISCR_STEPS\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 683
    },
    "id": "zTFk7qheIwqb",
    "outputId": "05e830e1-a868-4018-ece0-1a2c969b685c"
   },
   "outputs": [],
   "source": [
    "plot_gan_training(train_losses['discriminator_losses'], 'Discriminator loss')\n",
    "plot_gan_training(train_losses['generator_losses'], 'Generator loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uQD2gmHdPuU0"
   },
   "outputs": [],
   "source": [
    "def make_inference(generator, discriminator, n_samples=5000):\n",
    "    generator.eval()\n",
    "    discriminator.eval()\n",
    "    xs = np.linspace(0, 1, 1000)\n",
    "    samples = generator.sample(n_samples).cpu().detach().numpy()\n",
    "    discr_output = discriminator(torch.FloatTensor(xs).cuda().unsqueeze(1)).cpu().detach().numpy()\n",
    "    return samples, xs, discr_output\n",
    "\n",
    "\n",
    "def plot_results(data, samples, xs, ys, title):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    plt.hist(samples, bins=50, density=True, alpha=0.7, label='fake')\n",
    "    plt.hist(data, bins=50, density=True, alpha=0.7, label='real')\n",
    "\n",
    "    plt.plot(xs, ys, label='discrim')\n",
    "    plt.legend()\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 338
    },
    "id": "Yy8IpWG9L5f-",
    "outputId": "74903b8e-0c3a-46f7-c2c1-15996f4149d5"
   },
   "outputs": [],
   "source": [
    "samples, xs, discr_output = make_inference(generator, discriminator)\n",
    "plot_results(train_data, samples, xs, discr_output, 'Results')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S4JTKu_GKd5A"
   },
   "source": [
    "Now we'll use the non-saturating formulation of the GAN objective. Now, we have two separate losses:\n",
    "$$L^{(D)} = \\mathbb{E}_{\\mathbf{x} \\sim \\pi(\\mathbf{x})} [\\log D(\\mathbf{x})] + \\mathbb{E}_{\\mathbf{z} \\sim p(\\mathbf{z})}[\\log (1-D(G(\\mathbf{z})))]$$\n",
    "$$L^{(G)} = - \\mathbb{E}_{\\mathbf{z} \\sim p(\\mathbf{z})} \\log(D(G(\\mathbf{z}))$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "A76u5yydQODY"
   },
   "outputs": [],
   "source": [
    "# ====\n",
    "# your code\n",
    "# choose these parameters\n",
    "BATCH_SIZE = \n",
    "GEN_HIDDENS = \n",
    "DISCR_HIDDENS = \n",
    "EPOCHS = \n",
    "LR = \n",
    "DISCR_STEPS = \n",
    "# ====\n",
    "\n",
    "train_loader = data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)\n",
    "\n",
    "# model\n",
    "generator = MLPGenerator(latent_dim=1, hiddens=GEN_HIDDENS, data_dim=1).cuda()\n",
    "discriminator = MLPDiscriminator(latent_dim=1, hiddens=DISCR_HIDDENS, data_dim=1).cuda()\n",
    "\n",
    "\n",
    "def gen_loss(generator, discriminator, x):\n",
    "    # ====\n",
    "    # your code\n",
    "    # sample data from generator (number of samples = x.shape[0])\n",
    "    fake_data = \n",
    "    return # -log(D(x))\n",
    "    # ====\n",
    "\n",
    "\n",
    "# train\n",
    "train_losses = train_model(\n",
    "    generator, \n",
    "    discriminator, \n",
    "    gen_loss, \n",
    "    discr_loss, \n",
    "    train_loader, \n",
    "    epochs=EPOCHS,\n",
    "    lr=LR,\n",
    "    discr_steps=DISCR_STEPS\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 683
    },
    "id": "WO6STzTOQ3Iq",
    "outputId": "8610d310-9f45-476e-9b4c-03f212a79e43"
   },
   "outputs": [],
   "source": [
    "plot_gan_training(train_losses['discriminator_losses'], 'Discriminator loss')\n",
    "plot_gan_training(train_losses['generator_losses'], 'Generator loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "id": "aV_y2bGgQ40M",
    "outputId": "12a080ff-7834-4880-ebdc-1ceaf9597e02"
   },
   "outputs": [],
   "source": [
    "samples, xs, discr_output = make_inference(generator, discriminator)\n",
    "plot_results(train_data, samples, xs, discr_output, 'Results')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h8zInnwKIakS"
   },
   "source": [
    "# Task 2 (4 points): WGAN-GP for CIFAR 10\n",
    "\n",
    "In this task you will fit Wasserstein GAN with Gradient Penalty model to the CIFAR10 dataset (https://drive.google.com/file/d/16j3nrJV821VOkkuRz7aYam8TyIXLnNme/view?usp=sharing).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B0l_BCGoaRs7"
   },
   "outputs": [],
   "source": [
    "def load_pickle(path):\n",
    "    with open(path, 'rb') as f:\n",
    "        data = pickle.load(f)\n",
    "    train_data, test_data = data['train'], data['test']\n",
    "    return train_data, test_data\n",
    "\n",
    "\n",
    "def show_samples(samples, title, nrow=10):\n",
    "    samples = (torch.FloatTensor(samples) / 255).permute(0, 3, 1, 2)\n",
    "    grid_img = make_grid(samples, nrow=nrow)\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    plt.imshow(grid_img.permute(1, 2, 0))\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def visualize_data(data, title):\n",
    "    idxs = np.random.choice(len(data), replace=False, size=(100,))\n",
    "    images = train_data[idxs]\n",
    "    show_samples(images, title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "dAvSLXlKZ5q7",
    "outputId": "a7c1bb98-d348-4f8b-ae04-2689a783b2fc"
   },
   "outputs": [],
   "source": [
    "train_data, test_data = load_pickle(os.path.join('drive', 'My Drive', 'DGM2020', 'homework_supplementary', 'cifar10.pkl'))\n",
    "visualize_data(train_data, 'CIFAR10 samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7Z6ajzfBSgtW"
   },
   "outputs": [],
   "source": [
    "def gradient_penalty(critic, real_data, fake_data):\n",
    "    batch_size = real_data.shape[0]\n",
    "    # ====\n",
    "    # your code\n",
    "    # Calculate interpolation\n",
    "    # x_t = t * x_real + (1 - t) x_fake\n",
    "    \n",
    "    # sample t\n",
    "    t = \n",
    "    \n",
    "    # create x_t\n",
    "    # be careful about shapes\n",
    "    interpolated = \n",
    "    interpolated.requires_grad = True\n",
    "    # ====\n",
    "\n",
    "    # ====\n",
    "    # your code\n",
    "    # apply critic to x_t\n",
    "    d_output = \n",
    "    # ====\n",
    "    gradients = torch.autograd.grad(outputs=d_output, inputs=interpolated,\n",
    "                                    grad_outputs=torch.ones(d_output.size()).cuda(),\n",
    "                                    create_graph=True, retain_graph=True)[0]\n",
    "\n",
    "    gradients = gradients.reshape(batch_size, -1)\n",
    "    \n",
    "    # ====\n",
    "    # your code\n",
    "    # compute gradient norm\n",
    "    gradients_norm = \n",
    "    # ====\n",
    "    return ((gradients_norm - 1) ** 2).mean()\n",
    "\n",
    "def train(\n",
    "    generator, \n",
    "    critic, \n",
    "    gen_optimizer,\n",
    "    critic_optimizer,\n",
    "    train_loader,\n",
    "    critic_steps, \n",
    "    batch_size,\n",
    "    n_epochs\n",
    "):\n",
    "    train_losses = []\n",
    "    Lambda = 10\n",
    "    critic.train()\n",
    "    generator.train()\n",
    "\n",
    "    curr_iter = 0\n",
    "    for epoch_i in range(n_epochs):\n",
    "        batch_loss_history = []\n",
    "\n",
    "        for batch_i, x in enumerate(tqdm(train_loader, desc=f'epoch {epoch_i}', leave=False)):\n",
    "            curr_iter += 1\n",
    "            x = x.clone().detach().cuda().float()\n",
    "            x = 2 * (x - 0.5)\n",
    "\n",
    "            # do a critic update\n",
    "            critic_optimizer.zero_grad()\n",
    "            fake_data = generator.sample(x.shape[0])\n",
    "            gp = gradient_penalty(critic, x, fake_data)\n",
    "            # ====\n",
    "            # your code\n",
    "            # D(x_fake) - D(x_real) + Lambda (Lambda = 10) * grad_pen\n",
    "            d_loss = \n",
    "            # ====\n",
    "            d_loss.backward()\n",
    "            critic_optimizer.step()\n",
    "            # generator update\n",
    "            if curr_iter % critic_steps == 0:\n",
    "                gen_optimizer.zero_grad()\n",
    "                fake_data = generator.sample(batch_size)\n",
    "                # ====\n",
    "                # your code\n",
    "                # -D(x_fake)\n",
    "                g_loss = \n",
    "                # ====\n",
    "                g_loss.backward()\n",
    "                gen_optimizer.step()\n",
    "\n",
    "                batch_loss_history.append(g_loss.data.cpu().numpy())\n",
    "\n",
    "        epoch_loss = np.mean(batch_loss_history)\n",
    "        train_losses.append(epoch_loss)\n",
    "\n",
    "    train_losses = np.array(train_losses)\n",
    "    return train_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7a7fjcx0vL8R"
   },
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    def __init__(self, dim=64):\n",
    "        super().__init__()\n",
    "        self.dim = dim \n",
    "        # ====\n",
    "        # your code\n",
    "        # apply linear layer with output units 4 * 4 * 4 * dim, then relu\n",
    "        self.preprocess = nn.Sequential()\n",
    "        \n",
    "        # apply transposed conv with stride 2, kernel size 2\n",
    "        # then BN, then relu\n",
    "        self.block1 = nn.Sequential()\n",
    "        \n",
    "        # apply transposed conv with stride 2, kernel size 2\n",
    "        # then BN, then relu\n",
    "        self.block2 = nn.Sequential()\n",
    "        \n",
    "        # ====\n",
    "        self.deconv_out = nn.ConvTranspose2d(dim, 3, 2, stride=2)\n",
    "        self.tanh = nn.Tanh()\n",
    "        self.noise = torch.distributions.Normal(torch.tensor(0.), torch.tensor(1.))\n",
    "\n",
    "    def forward(self, input):\n",
    "        output = self.preprocess(input)\n",
    "        output = output.view(-1, 4 * self.dim, 4, 4)\n",
    "        output = self.block1(output)\n",
    "        output = self.block2(output)\n",
    "        output = self.deconv_out(output)\n",
    "        output = self.tanh(output)\n",
    "        return output.view(-1, 3, 32, 32)\n",
    "\n",
    "    def sample(self, n_samples):\n",
    "        z = self.noise.sample([n_samples, 128]).cuda()\n",
    "        return self.forward(z)\n",
    "\n",
    "\n",
    "class Critic(nn.Module):\n",
    "    def __init__(self, dim):\n",
    "        super().__init__()\n",
    "        self.dim = dim\n",
    "        \n",
    "        # ====\n",
    "        # your code\n",
    "        # sequence of Conv2D with kernel size 3 stride 2 and LeakyRelU\n",
    "        self.net = nn.Sequential()\n",
    "        # ====\n",
    "        self.linear = nn.Linear(4 * 4 * 4 * dim, 1)\n",
    "\n",
    "    def forward(self, input):\n",
    "        output = self.net(input)\n",
    "        output = output.view(-1, 4 * 4 * 4 * self.dim)\n",
    "        output = self.linear(output)\n",
    "        return output"
   ]
  },
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    },
    "id": "aY6qzJ5MSuiX",
    "outputId": "0c28838e-4b7a-4885-fdab-1093f15f0409"
   },
   "outputs": [],
   "source": [
    "train_data, test_data = load_pickle(os.path.join('drive', 'My Drive', 'DGM2020', 'homework_supplementary', 'cifar10.pkl'))\n",
    "train_data = np.transpose(train_data, (0, 3, 1, 2)) / 255.0\n",
    "\n",
    "# ====\n",
    "# your code\n",
    "# choose these parameters\n",
    "BATCH_SIZE = \n",
    "DIM = \n",
    "N_ITERATIONS = \n",
    "CRITIC_STEPS = \n",
    "# ====\n",
    "\n",
    "N_EPOCHS = CRITIC_STEPS * N_ITERATIONS // len(train_loader)\n",
    "train_loader = data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)\n",
    "print('Total number of epochs:', N_EPOCHS)\n",
    "\n",
    "generator = Generator(dim=DIM).cuda()\n",
    "critic = Critic(dim=DIM).cuda()\n",
    "\n",
    "gen_optimizer = torch.optim.Adam(generator.parameters(), lr=2e-4, betas=(0, 0.9))\n",
    "critic_optimizer = torch.optim.Adam(critic.parameters(), lr=2e-4, betas=(0, 0.9))\n",
    "\n",
    "losses = train(\n",
    "    generator, \n",
    "    critic, \n",
    "    gen_optimizer,\n",
    "    critic_optimizer,\n",
    "    train_loader,\n",
    "    CRITIC_STEPS, \n",
    "    BATCH_SIZE, \n",
    "    N_EPOCHS\n",
    ")\n",
    "\n",
    "plot_gan_training(losses, 'Generator loss')\n",
    "\n",
    "generator.eval()\n",
    "critic.eval()\n",
    "with torch.no_grad():\n",
    "    samples = generator.sample(1000)\n",
    "    samples = samples.permute(0, 2, 3, 1).cpu().detach().numpy() * 0.5 + 0.5\n",
    "    \n",
    "\n",
    "show_samples(samples[:100] * 255.0, title='CIFAR-10 generated samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qmlPlN_PSgyh"
   },
   "source": [
    "# Task 3 (4 points): WGAN vs WGAN-GP\n",
    "\n",
    "Here your task is reproduce the experiment with a toy dataset from https://arxiv.org/pdf/1704.00028.pdf\n",
    "\n",
    "\n",
    "NN architectures and hyperparameters should be the same as in the author's implementation (except defined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_inference(generator, critic, n_samples=5000):\n",
    "    generator.eval()\n",
    "    critic.eval()\n",
    "    xs = np.linspace(-3, 3, 1000)\n",
    "    xg, yg = np.meshgrid(xs, xs)\n",
    "    grid = np.concatenate((xg.reshape(-1, 1), yg.reshape(-1, 1)), axis=-1)\n",
    "    samples = generator.sample(n_samples).cpu().detach().numpy()\n",
    "    critic_output = critic(torch.FloatTensor(grid).cuda()).cpu().detach().numpy()\n",
    "    critic_output = np.prod(critic_output, axis=-1).reshape((1000, 1000))\n",
    "    return samples, grid, critic_output\n",
    "\n",
    "\n",
    "def visualize_2d_data(train_data, train_labels=None):\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    plt.title('train data')\n",
    "    plt.scatter(train_data[:, 0], train_data[:, 1], s=1, c=train_labels)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "def generate_dataset(size):\n",
    "    \n",
    "    scale = 2\n",
    "    var = 0.02\n",
    "    centers = [\n",
    "        (1, 0),\n",
    "        (-1, 0),\n",
    "        (0, 1),\n",
    "        (0, -1),\n",
    "        (1. / np.sqrt(2), 1. / np.sqrt(2)),\n",
    "        (1. / np.sqrt(2), -1. / np.sqrt(2)),\n",
    "        (-1. / np.sqrt(2), 1. / np.sqrt(2)),\n",
    "        (-1. / np.sqrt(2), -1. / np.sqrt(2))\n",
    "    ]\n",
    "    \n",
    "    centers = [(scale * x, scale * y) for x, y in centers]\n",
    "    dataset = []\n",
    "    for i in range(size):\n",
    "        \n",
    "        \n",
    "        point = np.random.randn(2) * var\n",
    "        center = random.choice(centers)\n",
    "        point[0] += center[0]\n",
    "        point[1] += center[1]\n",
    "        dataset.append(point)\n",
    "    dataset = np.array(dataset, dtype='float32')\n",
    "    dataset /= 1.414  # stdev\n",
    "    \n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = generate_dataset(25600)\n",
    "visualize_2d_data(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MLP NNs for WGAN Generator / Critic\n",
    "\n",
    "class ToyGenerator(nn.Module):\n",
    "    def __init__(self, latent_dim, hiddens, data_dim):\n",
    "        super().__init__()\n",
    "        self.latent_dim = latent_dim\n",
    "        self.mlp = FullyConnectedMLP(latent_dim, hiddens, data_dim)\n",
    "    \n",
    "    def forward(self, z):\n",
    "        # ====\n",
    "        # your code\n",
    "        \n",
    "        # ====\n",
    "        \n",
    "    def sample(self, n):\n",
    "        # ====\n",
    "        # your code\n",
    "        \n",
    "        # ====\n",
    "\n",
    "\n",
    "class ToyCritic(nn.Module):\n",
    "    def __init__(self, latent_dim, hiddens, data_dim):\n",
    "        super().__init__()\n",
    "        self.mlp = FullyConnectedMLP(latent_dim, hiddens, data_dim)\n",
    "    \n",
    "    def forward(self, z):\n",
    "        # ====\n",
    "        # your code\n",
    "        \n",
    "        # ===="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WGAN train loop\n",
    "def train(\n",
    "    generator, \n",
    "    critic, \n",
    "    gen_optimizer,\n",
    "    critic_optimizer,\n",
    "    train_loader,\n",
    "    critic_steps, \n",
    "    batch_size,\n",
    "    n_epochs,\n",
    "    clip_c\n",
    "):\n",
    "    train_losses = []\n",
    "    critic.train()\n",
    "    generator.train()\n",
    "\n",
    "    curr_iter = 0\n",
    "    for epoch_i in range(n_epochs):\n",
    "        batch_loss_history = []\n",
    "\n",
    "        for batch_i, x in enumerate(tqdm(train_loader, desc=f'epoch {epoch_i}', leave=False)):\n",
    "            curr_iter += 1\n",
    "            x = x.clone().detach().cuda().float()\n",
    "            \n",
    "            # do a critic update\n",
    "            critic_optimizer.zero_grad()\n",
    "            fake_data = generator.sample(x.shape[0])\n",
    "            \n",
    "            # ====\n",
    "            # your code\n",
    "            # D(x_fake) - D(x_real)\n",
    "            d_loss = \n",
    "            # ====\n",
    "            d_loss.backward()\n",
    "            critic_optimizer.step()\n",
    "            # ===\n",
    "            # Сritic weights clipping in [-clip_c, clip_c]\n",
    "            # ===\n",
    "            \n",
    "            # generator update\n",
    "            if curr_iter % critic_steps == 0:\n",
    "                gen_optimizer.zero_grad()\n",
    "                fake_data = generator.sample(batch_size)\n",
    "                # ====\n",
    "                # your code\n",
    "                # -D(x_fake)\n",
    "                g_loss = \n",
    "                # ====\n",
    "                g_loss.backward()\n",
    "                gen_optimizer.step()\n",
    "\n",
    "                batch_loss_history.append(g_loss.data.cpu().numpy())\n",
    "\n",
    "        epoch_loss = np.mean(batch_loss_history)\n",
    "        train_losses.append(epoch_loss)\n",
    "\n",
    "    train_losses = np.array(train_losses)\n",
    "    return train_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WGAN-GP train loop\n",
    "def gradient_penalty(critic, real_data, fake_data):\n",
    "    batch_size = real_data.shape[0]\n",
    "    # ====\n",
    "    # your code\n",
    "    # Calculate interpolation\n",
    "    # x_t = t * x_real + (1 - t) x_fake\n",
    "    \n",
    "    # sample t\n",
    "    t = \n",
    "    \n",
    "    # create x_t\n",
    "    # be careful about shapes\n",
    "    interpolated = \n",
    "    interpolated.requires_grad = True\n",
    "    # ====\n",
    "    \n",
    "    \n",
    "    # ====\n",
    "    # your code\n",
    "    # apply critic to x_t\n",
    "    d_output =     \n",
    "    # ====\n",
    "    gradients = torch.autograd.grad(outputs=d_output, inputs=interpolated,\n",
    "                                    grad_outputs=torch.ones(d_output.size()).cuda(),\n",
    "                                    create_graph=True, retain_graph=True)[0]\n",
    "\n",
    "    gradients = gradients.reshape(batch_size, -1)\n",
    "    \n",
    "    # ====\n",
    "    # your code\n",
    "    # compute gradient norm\n",
    "    gradients_norm =   \n",
    "    # ====\n",
    "    return ((gradients_norm - 1) ** 2).mean()\n",
    "\n",
    "def train_gp(\n",
    "    generator, \n",
    "    critic, \n",
    "    gen_optimizer,\n",
    "    critic_optimizer,\n",
    "    train_loader,\n",
    "    critic_steps, \n",
    "    batch_size,\n",
    "    n_epochs, \n",
    "    lambda_coef\n",
    "):\n",
    "    train_losses = []\n",
    "    \n",
    "    critic.train()\n",
    "    generator.train()\n",
    "\n",
    "    curr_iter = 0\n",
    "    for epoch_i in range(n_epochs):\n",
    "        batch_loss_history = []\n",
    "\n",
    "        for batch_i, x in enumerate(tqdm(train_loader, desc=f'epoch {epoch_i}', leave=False)):\n",
    "            curr_iter += 1\n",
    "            x = x.clone().detach().cuda().float()\n",
    "            \n",
    "            # do a critic update\n",
    "            critic_optimizer.zero_grad()\n",
    "            fake_data = generator.sample(x.shape[0])\n",
    "            gp = gradient_penalty(critic, x, fake_data)\n",
    "            # ====\n",
    "            # your code\n",
    "            # D(x_fake) - D(x_real) + lambda_coef * grad_pen\n",
    "            d_loss = \n",
    "            # ====\n",
    "            d_loss.backward()\n",
    "            critic_optimizer.step()\n",
    "            # generator update\n",
    "            if curr_iter % critic_steps == 0:\n",
    "                gen_optimizer.zero_grad()\n",
    "                fake_data = generator.sample(batch_size)\n",
    "                # ====\n",
    "                # your code\n",
    "                # -D(x_fake)\n",
    "                g_loss = \n",
    "                # ====\n",
    "                g_loss.backward()\n",
    "                gen_optimizer.step()\n",
    "\n",
    "                batch_loss_history.append(g_loss.data.cpu().numpy())\n",
    "\n",
    "        epoch_loss = np.mean(batch_loss_history)\n",
    "        train_losses.append(epoch_loss)\n",
    "\n",
    "    train_losses = np.array(train_losses)\n",
    "    return train_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def visualize_critic_output(critic_output):\n",
    "    # ====\n",
    "    # your code\n",
    "    # plot critic outputs from \n",
    "    pass\n",
    "    # ====\n",
    "\n",
    "def visualize_critic_params(critic):\n",
    "    # ====\n",
    "    # your code\n",
    "    # plot critic params distribution\n",
    "    pass\n",
    "    # ===="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====\n",
    "# your code\n",
    "# choose these parameters\n",
    "BATCH_SIZE = \n",
    "GEN_HIDDENS = \n",
    "DISCR_HIDDENS = \n",
    "CRITIC_STEPS = \n",
    "CLIP_C = \n",
    "# ====\n",
    "\n",
    "N_EPOCHS = 300\n",
    "train_loader = data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====\n",
    "# your code\n",
    "gen_optimizer = \n",
    "critic_optimizer = \n",
    "# ====\n",
    "\n",
    "losses = train(\n",
    "    generator, \n",
    "    critic, \n",
    "    gen_optimizer,\n",
    "    critic_optimizer,\n",
    "    train_loader,\n",
    "    CRITIC_STEPS, \n",
    "    BATCH_SIZE, \n",
    "    N_EPOCHS,\n",
    "    CLIP_C\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_gan_training(losses, 'Generator loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples, grid, critic_output = make_inference(generator, critic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_2d_data(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_critic_output(critic_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_critic_params(critic)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WGAN-GP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====\n",
    "# your code\n",
    "# choose these parameters\n",
    "BATCH_SIZE = \n",
    "GEN_HIDDENS = \n",
    "DISCR_HIDDENS = \n",
    "CRITIC_STEPS = \n",
    "LAMBDA = \n",
    "# ====\n",
    "\n",
    "N_EPOCHS = 300\n",
    "train_loader = data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====\n",
    "# your code\n",
    "gen_optimizer = \n",
    "critic_optimizer = \n",
    "# ====\n",
    "\n",
    "losses = train_gp(\n",
    "    generator, \n",
    "    critic, \n",
    "    gen_optimizer,\n",
    "    critic_optimizer,\n",
    "    train_loader,\n",
    "    CRITIC_STEPS, \n",
    "    BATCH_SIZE, \n",
    "    N_EPOCHS,\n",
    "    LAMBDA\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples, grid, critic_output = make_inference(generator, critic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_critic_params(critic)"
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