learn_pytorch_42_pytorch实战项目之全连接生成对抗网络GAN基于MNIST数据集.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  GAN及其变体的原理\n",
    "参考论文：   \n",
    "基于生成对抗网络的模仿学习综述   计算机学报 2020,43(2) 作者：林嘉豪 章宗长   \n",
    "生成式对抗网络及其计算机视觉应用研究综述 中国图像图形学报 2018，23(10),作者：曹仰杰 贾丽丽   \n",
    "生成式对抗网络GAN 的研究进展与展望  自动化学报 2017,43(3),作者：王坤峰 苟超   \n",
    "生成对抗网络研究综述  计算机系统应用  2019,28(11),作者：邹秀芳, 朱定局  \n",
    "生成对抗网络GAN综述  计算机科学  2019,46(3),作者：程显毅  谢璐   \n",
    "\n",
    "更详细请参考原始论文!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "random_seed = 123\n",
    "generator_learning_rate = 0.001\n",
    "discriminator_learning_rate = 0.001\n",
    "NUM_EPOCHS = 100\n",
    "BATCH_SIZE = 128\n",
    "LATENT_DIM = 75\n",
    "IMG_SHAPE = (1, 28, 28)\n",
    "IMG_SIZE = 1\n",
    "for x in IMG_SHAPE:\n",
    "    IMG_SIZE *= x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMG_SIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "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()\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": 8,
   "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": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 100/100 | Batch 200/469 | Gen/Dis Loss: 0.8379/0.6768\n",
      "Epoch: 100/100 | Batch 300/469 | Gen/Dis Loss: 0.9681/0.6653\n",
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      "Time elapsed: 39.52 min\n",
      "Total Training Time: 39.52 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",
    "        valid = torch.ones(targets.size(0)).float().to(device)\n",
    "        fake = torch.zeros(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",
    "        gener_loss = F.binary_cross_entropy(discr_pred, valid)\n",
    "        \n",
    "        optim_gener.zero_grad()\n",
    "        gener_loss.backward()\n",
    "        optim_gener.step()\n",
    "        \n",
    "        # --------------------------\n",
    "        # Train Discriminator\n",
    "        # --------------------------        \n",
    "        \n",
    "        discr_pred_real = model.discriminator_forward(features.view(-1, IMG_SIZE))\n",
    "        real_loss = F.binary_cross_entropy(discr_pred_real, valid)\n",
    "        \n",
    "        discr_pred_fake = model.discriminator_forward(generated_features.detach())\n",
    "        fake_loss = F.binary_cross_entropy(discr_pred_fake, fake)\n",
    "        \n",
    "        discr_loss = 0.5*(real_loss + fake_loss)\n",
    "\n",
    "        optim_discr.zero_grad()\n",
    "        discr_loss.backward()\n",
    "        optim_discr.step()        \n",
    "        \n",
    "        discr_costs.append(discr_loss)\n",
    "        gener_costs.append(gener_loss)\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": "code",
   "execution_count": 10,
   "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "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": 11,
   "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": [
    "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}