106_torchvision数据集使用.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. torchvision数据集介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① torchvision中有很多数据集，当我们写代码时指定相应的数据集指定一些参数，它就可以自行下载。\n",
    "\n",
    "② CIFAR-10数据集包含60000张32×32的彩色图片，一共10个类别，其中50000张训练图片，10000张测试图片。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. torchvision数据集使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① 在 Anaconda 终端里面，激活py3.6.3环境，再跳转到该项目的路径下。\n",
    "\n",
    "② 运行python。导入torchvision包，输入train_set = torchvision.datasets.CIFAR10(root=\"./dataset\",train=True,download=True)命令，即下载数据集到到该文件夹下。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class CIFAR10 in module torchvision.datasets.cifar:\n",
      "\n",
      "class CIFAR10(torchvision.datasets.vision.VisionDataset)\n",
      " |  `CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.\n",
      " |  \n",
      " |  Args:\n",
      " |      root (string): Root directory of dataset where directory\n",
      " |          ``cifar-10-batches-py`` exists or will be saved to if download is set to True.\n",
      " |      train (bool, optional): If True, creates dataset from training set, otherwise\n",
      " |          creates from test set.\n",
      " |      transform (callable, optional): A function/transform that takes in an PIL image\n",
      " |          and returns a transformed version. E.g, ``transforms.RandomCrop``\n",
      " |      target_transform (callable, optional): A function/transform that takes in the\n",
      " |          target and transforms it.\n",
      " |      download (bool, optional): If true, downloads the dataset from the internet and\n",
      " |          puts it in root directory. If dataset is already downloaded, it is not\n",
      " |          downloaded again.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      CIFAR10\n",
      " |      torchvision.datasets.vision.VisionDataset\n",
      " |      torch.utils.data.dataset.Dataset\n",
      " |      typing.Generic\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __getitem__(self, index:int) -> Tuple[Any, Any]\n",
      " |      Args:\n",
      " |          index (int): Index\n",
      " |      \n",
      " |      Returns:\n",
      " |          tuple: (image, target) where target is index of the target class.\n",
      " |  \n",
      " |  __init__(self, root:str, train:bool=True, transform:Union[Callable, NoneType]=None, target_transform:Union[Callable, NoneType]=None, download:bool=False) -> None\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __len__(self) -> int\n",
      " |  \n",
      " |  download(self) -> None\n",
      " |  \n",
      " |  extra_repr(self) -> str\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  __args__ = None\n",
      " |  \n",
      " |  __extra__ = None\n",
      " |  \n",
      " |  __next_in_mro__ = <class 'object'>\n",
      " |      The most base type\n",
      " |  \n",
      " |  __orig_bases__ = (torchvision.datasets.vision.VisionDataset,)\n",
      " |  \n",
      " |  __origin__ = None\n",
      " |  \n",
      " |  __parameters__ = ()\n",
      " |  \n",
      " |  __tree_hash__ = -9223371924072727236\n",
      " |  \n",
      " |  base_folder = 'cifar-10-batches-py'\n",
      " |  \n",
      " |  filename = 'cifar-10-python.tar.gz'\n",
      " |  \n",
      " |  meta = {'filename': 'batches.meta', 'key': 'label_names', 'md5': '5ff9...\n",
      " |  \n",
      " |  test_list = [['test_batch', '40351d587109b95175f43aff81a1287e']]\n",
      " |  \n",
      " |  tgz_md5 = 'c58f30108f718f92721af3b95e74349a'\n",
      " |  \n",
      " |  train_list = [['data_batch_1', 'c99cafc152244af753f735de768cd75f'], ['...\n",
      " |  \n",
      " |  url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from torchvision.datasets.vision.VisionDataset:\n",
      " |  \n",
      " |  __repr__(self) -> str\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from torch.utils.data.dataset.Dataset:\n",
      " |  \n",
      " |  __add__(self, other:'Dataset[T_co]') -> 'ConcatDataset[T_co]'\n",
      " |  \n",
      " |  __getattr__(self, attribute_name)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from torch.utils.data.dataset.Dataset:\n",
      " |  \n",
      " |  register_datapipe_as_function(function_name, cls_to_register, enable_df_api_tracing=False) from typing.GenericMeta\n",
      " |  \n",
      " |  register_function(function_name, function) from typing.GenericMeta\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from torch.utils.data.dataset.Dataset:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from torch.utils.data.dataset.Dataset:\n",
      " |  \n",
      " |  __annotations__ = {'functions': typing.Dict[str, typing.Callable]}\n",
      " |  \n",
      " |  functions = {'concat': functools.partial(<function Dataset.register_da...\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods inherited from typing.Generic:\n",
      " |  \n",
      " |  __new__(cls, *args, **kwds)\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "help(torchvision.datasets.CIFAR10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 查看CIFAR10数据集内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "(<PIL.Image.Image image mode=RGB size=32x32 at 0x1A4275AAF28>, 3)\n",
      "['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
      "<PIL.Image.Image image mode=RGB size=32x32 at 0x1A4275AAA58>\n",
      "3\n",
      "cat\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "train_set = torchvision.datasets.CIFAR10(root=\"./dataset\",train=True,download=True) # root为存放数据集的相对路线\n",
    "test_set = torchvision.datasets.CIFAR10(root=\"./dataset\",train=False,download=True) # train=True是训练集，train=False是测试集  \n",
    "\n",
    "print(test_set[0])       # 输出的3是target \n",
    "print(test_set.classes)  # 测试数据集中有多少种\n",
    "\n",
    "img, target = test_set[0] # 分别获得图片、target\n",
    "print(img)\n",
    "print(target)\n",
    "\n",
    "print(test_set.classes[target]) # 3号target对应的种类\n",
    "img.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Tensorboard查看内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "torch.Size([3, 32, 32])\n",
      "torch.Size([3, 32, 32])\n",
      "torch.Size([3, 32, 32])\n",
      "torch.Size([3, 32, 32])\n",
      "torch.Size([3, 32, 32])\n",
      "torch.Size([3, 32, 32])\n",
      "torch.Size([3, 32, 32])\n",
      "torch.Size([3, 32, 32])\n",
      "torch.Size([3, 32, 32])\n",
      "torch.Size([3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "dataset_transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])     \n",
    "train_set = torchvision.datasets.CIFAR10(root=\"./dataset\",train=True,transform=dataset_transform,download=True) # 将ToTensor应用到数据集中的每一张图片，每一张图片转为Tensor数据类型     \n",
    "test_set = torchvision.datasets.CIFAR10(root=\"./dataset\",train=False,transform=dataset_transform,download=True)   \n",
    "\n",
    "writer = SummaryWriter(\"logs\") \n",
    "for i in range(10):\n",
    "    img, target = test_set[i]\n",
    "    writer.add_image(\"test_set\",img,i)\n",
    "    print(img.size())\n",
    "\n",
    "writer.close() # 一定要把读写关闭，否则显示不出来图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① 在 Anaconda 终端里面，激活py3.6.3环境，再输入 tensorboard --logdir=C:\\Users\\wangy\\Desktop\\03CV\\logs 命令，将网址赋值浏览器的网址栏，回车，即可查看tensorboard显示日志情况。"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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