{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.1 图像增广"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-29T14:27:44.323427Z",
     "start_time": "2019-05-29T14:27:43.032842Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0.0\n",
      "cuda\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import time\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torchvision\n",
    "from PIL import Image\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"..\") \n",
    "import d2lzh_pytorch as d2l\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(torch.__version__)\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.1.1 常用的图像增广方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-29T14:27:44.619786Z",
     "start_time": "2019-05-29T14:27:44.326107Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "<Figure size 252x180 with 1 Axes>"
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   "source": [
    "d2l.set_figsize()\n",
    "img = Image.open('../../img/cat1.jpg')\n",
    "d2l.plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
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   "source": [
    "# 本函数已保存在d2lzh_pytorch包中方便以后使用\n",
    "def show_images(imgs, num_rows, num_cols, scale=2):\n",
    "    figsize = (num_cols * scale, num_rows * scale)\n",
    "    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)\n",
    "    for i in range(num_rows):\n",
    "        for j in range(num_cols):\n",
    "            axes[i][j].imshow(imgs[i * num_cols + j])\n",
    "            axes[i][j].axes.get_xaxis().set_visible(False)\n",
    "            axes[i][j].axes.get_yaxis().set_visible(False)\n",
    "    return axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-29T14:27:44.633413Z",
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    "def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):\n",
    "    Y = [aug(img) for _ in range(num_rows * num_cols)]\n",
    "    show_images(Y, num_rows, num_cols, scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1.1.1 翻转和裁剪"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "ExecuteTime": {
     "end_time": "2019-05-29T14:27:44.909361Z",
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   "source": [
    "shape_aug = torchvision.transforms.RandomResizedCrop(200, scale=(0.1, 1), ratio=(0.5, 2))\n",
    "apply(img, shape_aug)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1.1.2 变化颜色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-29T14:27:45.711617Z",
     "start_time": "2019-05-29T14:27:45.454846Z"
    }
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       "<Figure size 432x216 with 8 Axes>"
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    "color_aug = torchvision.transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)\n",
    "apply(img, color_aug)"
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   "source": [
    "### 9.1.1.3 叠加多个图像增广方法"
   ]
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   "source": [
    "augs = torchvision.transforms.Compose([\n",
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  },
  {
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   "source": [
    "## 9.1.2 使用图像增广训练模型"
   ]
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  {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
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       "<Figure size 460.8x230.4 with 32 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_imges = torchvision.datasets.CIFAR10(train=True, root=\"~/Datasets/CIFAR\", download=True)\n",
    "# all_imges的每一个元素都是(image, label)\n",
    "show_images([all_imges[i][0] for i in range(32)], 4, 8, scale=0.8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-29T14:27:50.996901Z",
     "start_time": "2019-05-29T14:27:50.993693Z"
    }
   },
   "outputs": [],
   "source": [
    "flip_aug = torchvision.transforms.Compose([\n",
    "     torchvision.transforms.RandomHorizontalFlip(),\n",
    "     torchvision.transforms.ToTensor()])\n",
    "\n",
    "no_aug = torchvision.transforms.Compose([\n",
    "     torchvision.transforms.ToTensor()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-29T14:27:51.003553Z",
     "start_time": "2019-05-29T14:27:50.998826Z"
    }
   },
   "outputs": [],
   "source": [
    "num_workers = 0 if sys.platform.startswith('win32') else 4\n",
    "def load_cifar10(is_train, augs, batch_size, root=\"~/Datasets/CIFAR\"):\n",
    "    dataset = torchvision.datasets.CIFAR10(root=root, train=is_train, transform=augs, download=True)\n",
    "    return DataLoader(dataset, batch_size=batch_size, shuffle=is_train, num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1.2.1 使用图像增广训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-29T14:27:51.010527Z",
     "start_time": "2019-05-29T14:27:51.005395Z"
    }
   },
   "outputs": [],
   "source": [
    "# 本函数已保存在d2lzh_pytorch包中方便以后使用\n",
    "def train(train_iter, test_iter, net, loss, optimizer, device, num_epochs):\n",
    "    net = net.to(device)\n",
    "    print(\"training on \", device)\n",
    "    batch_count = 0\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()\n",
    "        for X, y in train_iter:\n",
    "            X = X.to(device)\n",
    "            y = y.to(device)\n",
    "            y_hat = net(X)\n",
    "            l = loss(y_hat, y)\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "            train_l_sum += l.cpu().item()\n",
    "            train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()\n",
    "            n += y.shape[0]\n",
    "            batch_count += 1\n",
    "        test_acc = d2l.evaluate_accuracy(test_iter, net)\n",
    "        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec'\n",
    "              % (epoch + 1, train_l_sum / batch_count, train_acc_sum / n, test_acc, time.time() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-29T14:27:51.016923Z",
     "start_time": "2019-05-29T14:27:51.012423Z"
    }
   },
   "outputs": [],
   "source": [
    "def train_with_data_aug(train_augs, test_augs, lr=0.001):\n",
    "    batch_size, net = 256, d2l.resnet18(10)\n",
    "    optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "    loss = torch.nn.CrossEntropyLoss()\n",
    "    train_iter = load_cifar10(True, train_augs, batch_size)\n",
    "    test_iter = load_cifar10(False, test_augs, batch_size)\n",
    "    train(train_iter, test_iter, net, loss, optimizer, device, num_epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-29T14:48:32.502485Z",
     "start_time": "2019-05-29T14:27:51.018678Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "training on  cuda\n",
      "epoch 1, loss 1.3615, train acc 0.505, test acc 0.493, time 123.2 sec\n",
      "epoch 2, loss 0.5003, train acc 0.645, test acc 0.620, time 123.0 sec\n",
      "epoch 3, loss 0.2811, train acc 0.703, test acc 0.616, time 123.1 sec\n",
      "epoch 4, loss 0.1890, train acc 0.735, test acc 0.686, time 123.0 sec\n",
      "epoch 5, loss 0.1346, train acc 0.765, test acc 0.671, time 123.1 sec\n",
      "epoch 6, loss 0.1029, train acc 0.787, test acc 0.674, time 123.1 sec\n",
      "epoch 7, loss 0.0803, train acc 0.804, test acc 0.749, time 123.1 sec\n",
      "epoch 8, loss 0.0644, train acc 0.822, test acc 0.717, time 123.1 sec\n",
      "epoch 9, loss 0.0526, train acc 0.836, test acc 0.750, time 123.0 sec\n",
      "epoch 10, loss 0.0433, train acc 0.851, test acc 0.754, time 123.1 sec\n"
     ]
    }
   ],
   "source": [
    "train_with_data_aug(flip_aug, no_aug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
