fastai_DL_nbs/lesson07_wgan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "# 多行输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSun bedroom data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import *\n",
    "from fastai.vision.gan import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.LSUN_BEDROOMS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/lyc/.fastai/data/bedroom')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/lyc/.fastai/data/bedroom/7'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/f'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/5'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/3'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/6'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/8'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/d'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/1'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/b'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/c'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/9'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/models'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/a'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/e'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/0'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/4'),\n",
       " PosixPath('/home/lyc/.fastai/data/bedroom/2')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path\n",
    "path.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### feature losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(bs, size):\n",
    "    \"\"\"\n",
    "    不分割训练集和验证集\n",
    "    \"\"\"\n",
    "    return (GANItemList.from_folder(path, noise_sz=100)\n",
    "               .split_none()\n",
    "               .label_from_func(noop)\n",
    "               .transform(tfms=[[crop_pad(size=size, row_pct=(0,1), col_pct=(0,1))], []], size=size, tfm_y=True)\n",
    "               .databunch(bs=bs)\n",
    "               .normalize(stats = [torch.tensor([0.5,0.5,0.5]), torch.tensor([0.5,0.5,0.5])], do_x=False, do_y=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = get_data(128, 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch(rows=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GAN 表示 Generative Adversarial Nets 由 Ian Goodfellow 发明. 同时训练两个模型，一个生成器一个判别器. 生成器生成类似于真实数据的新数据，判别器分类新数据是否是真实的。生成器返回图片，判别器返回数值，(usually 0. for fake images and 1. for real ones)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- freeze生成器训练判别器的步骤:\n",
    "    - 获取一个batch的真实图片\n",
    "    - 生成一个batch的假图片\n",
    "    - 判别器计算损失; \n",
    "    - 奖励真实图片，惩罚假图片\n",
    "    - 更新判别器的权重\n",
    "- Freeze 判别器训练生成器的步骤:\n",
    "    - 生成一个batch的假图片\n",
    "    - 判别器评价生成的图片\n",
    "    - 奖励真实图片，惩罚假图片\n",
    "    - 更新生成器的权重\n",
    "- [Wassertein GAN](https://arxiv.org/pdf/1701.07875.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = basic_generator(in_size=32, n_channels=3, n_extra_layers=1)\n",
    "critic    = basic_critic   (in_size=32, n_channels=3, n_extra_layers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): ConvTranspose2d(100, 256, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (2): Sequential(\n",
       "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (3): Sequential(\n",
       "    (0): ConvTranspose2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (4): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (5): Tanh()\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (1): LeakyReLU(negative_slope=0.2, inplace)\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): Sequential(\n",
       "      (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (1): LeakyReLU(negative_slope=0.2, inplace)\n",
       "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (2): Sequential(\n",
       "    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (1): LeakyReLU(negative_slope=0.2, inplace)\n",
       "    (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (3): Sequential(\n",
       "    (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (1): LeakyReLU(negative_slope=0.2, inplace)\n",
       "    (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (4): Conv2d(256, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
       "  (5): Lambda()\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "critic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = GANLearner.wgan(data, generator, critic, switch_eval=False,\n",
    "                        opt_func = partial(optim.Adam, betas = (0.,0.99)), wd=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GANModule(\n",
       "  (generator): Sequential(\n",
       "    (0): Sequential(\n",
       "      (0): ConvTranspose2d(100, 256, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
       "      (1): ReLU(inplace)\n",
       "      (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): Sequential(\n",
       "      (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (1): ReLU(inplace)\n",
       "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (2): Sequential(\n",
       "      (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (1): ReLU(inplace)\n",
       "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (3): Sequential(\n",
       "      (0): ConvTranspose2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (1): ReLU(inplace)\n",
       "      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (4): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (5): Tanh()\n",
       "  )\n",
       "  (critic): Sequential(\n",
       "    (0): Sequential(\n",
       "      (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      (1): LeakyReLU(negative_slope=0.2, inplace)\n",
       "    )\n",
       "    (1): Sequential(\n",
       "      (0): Sequential(\n",
       "        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): LeakyReLU(negative_slope=0.2, inplace)\n",
       "        (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (2): Sequential(\n",
       "      (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (1): LeakyReLU(negative_slope=0.2, inplace)\n",
       "      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (3): Sequential(\n",
       "      (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (1): LeakyReLU(negative_slope=0.2, inplace)\n",
       "      (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (4): Conv2d(256, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
       "    (5): Lambda()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='4' class='' max='30', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      13.33% [4/30 2:05:54<13:38:22]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>gen_loss</th>\n",
       "      <th>disc_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.021306</td>\n",
       "      <td>#na#</td>\n",
       "      <td>-0.022490</td>\n",
       "      <td>-0.020275</td>\n",
       "      <td>19:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>#na#</td>\n",
       "      <td>0.004158</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>32:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.015662</td>\n",
       "      <td>#na#</td>\n",
       "      <td>0.008084</td>\n",
       "      <td>-0.026035</td>\n",
       "      <td>33:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.001665</td>\n",
       "      <td>#na#</td>\n",
       "      <td>-0.003149</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>40:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='2183' class='' max='2368', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      92.19% [2183/2368 41:45<03:32 -0.0007]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit(30, 1e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 切换到生成器并生成预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.gan_trainer.switch(gen_mode=True)\n",
    "learn.show_results(ds_type=DatasetType.Train, rows=8)"
   ]
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}