{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.5 图像分类数据集（Fashion-MNIST）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.1\n",
      "0.2.1\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import sys\n",
    "sys.path.append(\"..\") # 为了导入上层目录的d2lzh_pytorch\n",
    "import d2lzh_pytorch as d2l\n",
    "\n",
    "print(torch.__version__)\n",
    "print(torchvision.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5.1 获取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mnist_train = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=True, download=True, transform=transforms.ToTensor())\n",
    "mnist_test = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=False, download=True, transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'torchvision.datasets.mnist.FashionMNIST'>\n",
      "60000 10000\n"
     ]
    }
   ],
   "source": [
    "print(type(mnist_train))\n",
    "print(len(mnist_train), len(mnist_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 28, 28]) torch.float32\n",
      "tensor(9)\n"
     ]
    }
   ],
   "source": [
    "feature, label = mnist_train[0]\n",
    "print(feature.shape, feature.dtype)  # Channel x Height X Width\n",
    "print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<PIL.Image.Image image mode=L size=28x28 at 0x114A41668>\n"
     ]
    }
   ],
   "source": [
    "mnist_PIL = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=True, download=True)\n",
    "PIL_feature, label = mnist_PIL[0]\n",
    "print(PIL_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 本函数已保存在d2lzh包中方便以后使用\n",
    "def get_fashion_mnist_labels(labels):\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [text_labels[int(i)] for i in labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 本函数已保存在d2lzh包中方便以后使用\n",
    "def show_fashion_mnist(images, labels):\n",
    "    d2l.use_svg_display()\n",
    "    # 这里的_表示我们忽略（不使用）的变量\n",
    "    _, figs = plt.subplots(1, len(images), figsize=(12, 12))\n",
    "    for f, img, lbl in zip(figs, images, labels):\n",
    "        f.imshow(img.view((28, 28)).numpy())\n",
    "        f.set_title(lbl)\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = [], []\n",
    "for i in range(10):\n",
    "    X.append(mnist_train[i][0])\n",
    "    y.append(mnist_train[i][1])\n",
    "show_fashion_mnist(X, get_fashion_mnist_labels(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5.2 读取小批量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "if sys.platform.startswith('win'):\n",
    "    num_workers = 0  # 0表示不用额外的进程来加速读取数据\n",
    "else:\n",
    "    num_workers = 4\n",
    "train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.57 sec\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "for X, y in train_iter:\n",
    "    continue\n",
    "print('%.2f sec' % (time.time() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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