2.3_图像分类常用的数据集处理方式.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import torchvision\n",
    "from torch.utils import data\n",
    "from torchvision import transforms\n",
    "from d2l import torch as d2l\n",
    "\n",
    "d2l.use_svg_display()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用框架自带的函数数据集\n",
    "trans=transforms.ToTensor()#将图片转成tensor\n",
    "mnist_train=torchvision.datasets.FashionMNIST(\n",
    "    root='./data',\n",
    "    train=True,\n",
    "    transform=trans,\n",
    "    download=True#默认从网络上下载)\n",
    ")\n",
    "mnist_test=torchvision.datasets.FashionMNIST(\n",
    "    root='./data',\n",
    "    train=False,\n",
    "    transform=trans,\n",
    "    download=True\n",
    ")\n",
    "len(mnist_train),len(mnist_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 两个可视化数据集的函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "text/plain": [
       "<Figure size 1350x300 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fashion_mnist_labels(labels):  \n",
    "    # 函数用于在数字标签索引及其文本名称之间进行转换\n",
    "    # #@save\n",
    "    \"\"\"返回Fashion-MNIST数据集的文本标签\"\"\"\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]\n",
    "\n",
    "def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):  #@save\n",
    "    \"\"\"绘制图像列表\"\"\"\n",
    "    figsize = (num_cols * scale, num_rows * scale)\n",
    "    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)\n",
    "    axes = axes.flatten()\n",
    "    for i, (ax, img) in enumerate(zip(axes, imgs)):\n",
    "        if torch.is_tensor(img):\n",
    "            # 图片张量\n",
    "            ax.imshow(img.numpy())\n",
    "        else:\n",
    "            # PIL图片\n",
    "            ax.imshow(img)\n",
    "        ax.axes.get_xaxis().set_visible(False)\n",
    "        ax.axes.get_yaxis().set_visible(False)\n",
    "        if titles:\n",
    "            ax.set_title(titles[i])\n",
    "    return axes\n",
    "\n",
    "print(mnist_train[0])\n",
    "X, y = next(iter(data.DataLoader(mnist_train, batch_size=18)))\n",
    "show_images(X.reshape(18, 28, 28), 2, 9, titles=get_fashion_mnist_labels(y));"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取大小为batch_size的小批量数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=128\n",
    "def get_dataloader_workers():\n",
    "    \"\"\"使用4个进程来读取数据。\"\"\"\n",
    "    return 4\n",
    "\n",
    "train_iter=data.DataLoader(mnist_train,batch_size,shuffle=True,num_workers=get_dataloader_workers())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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