4-torchvision数据集使用

1. torchvision数据集介绍

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。

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。

2. torchvision数据集使用

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。

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。

import torchvision
help(torchvision.datasets.CIFAR10)

Help on class CIFAR10 in module torchvision.datasets.cifar:

class CIFAR10(torchvision.datasets.vision.VisionDataset)
 |  `CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
 |
 |  Args:
 |      root (string): Root directory of dataset where directory
 |          ``cifar-10-batches-py`` exists or will be saved to if download is set to True.
 |      train (bool, optional): If True, creates dataset from training set, otherwise
 |          creates from test set.
 |      transform (callable, optional): A function/transform that takes in an PIL image
 |          and returns a transformed version. E.g, ``transforms.RandomCrop``
 |      target_transform (callable, optional): A function/transform that takes in the
 |          target and transforms it.
 |      download (bool, optional): If true, downloads the dataset from the internet and
 |          puts it in root directory. If dataset is already downloaded, it is not
 |          downloaded again.
 |
 |  Method resolution order:
 |      CIFAR10
 |      torchvision.datasets.vision.VisionDataset
 |      torch.utils.data.dataset.Dataset
 |      typing.Generic
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getitem__(self, index:int) -> Tuple[Any, Any]
 |      Args:
 |          index (int): Index
 |
 |      Returns:
 |          tuple: (image, target) where target is index of the target class.
 |
 |  __init__(self, root:str, train:bool=True, transform:Union[Callable, NoneType]=None, target_transform:Union[Callable, NoneType]=None, download:bool=False) -> None
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __len__(self) -> int
 |
 |  download(self) -> None
 |
 |  extra_repr(self) -> str
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __abstractmethods__ = frozenset()
 |
 |  __args__ = None
 |
 |  __extra__ = None
 |
 |  __next_in_mro__ = <class 'object'>
 |      The most base type
 |
 |  __orig_bases__ = (torchvision.datasets.vision.VisionDataset,)
 |
 |  __origin__ = None
 |
 |  __parameters__ = ()
 |
 |  __tree_hash__ = -9223371924072727236
 |
 |  base_folder = 'cifar-10-batches-py'
 |
 |  filename = 'cifar-10-python.tar.gz'
 |
 |  meta = {'filename': 'batches.meta', 'key': 'label_names', 'md5': '5ff9...
 |
 |  test_list = [['test_batch', '40351d587109b95175f43aff81a1287e']]
 |
 |  tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
 |
 |  train_list = [['data_batch_1', 'c99cafc152244af753f735de768cd75f'], ['...
 |
 |  url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from torchvision.datasets.vision.VisionDataset:
 |
 |  __repr__(self) -> str
 |      Return repr(self).
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from torch.utils.data.dataset.Dataset:
 |
 |  __add__(self, other:'Dataset[T_co]') -> 'ConcatDataset[T_co]'
 |
 |  __getattr__(self, attribute_name)
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from torch.utils.data.dataset.Dataset:
 |
 |  register_datapipe_as_function(function_name, cls_to_register, enable_df_api_tracing=False) from typing.GenericMeta
 |
 |  register_function(function_name, function) from typing.GenericMeta
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from torch.utils.data.dataset.Dataset:
 |
 |  __dict__
 |      dictionary for instance variables (if defined)
 |
 |  __weakref__
 |      list of weak references to the object (if defined)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from torch.utils.data.dataset.Dataset:
 |
 |  __annotations__ = {'functions': typing.Dict[str, typing.Callable]}
 |
 |  functions = {'concat': functools.partial(<function Dataset.register_da...
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from typing.Generic:
 |
 |  __new__(cls, *args, **kwds)
 |      Create and return a new object.  See help(type) for accurate signature.

3. 查看CIFAR10数据集内容

import torchvision
train_set = torchvision.datasets.CIFAR10(root="./dataset",train=True,download=True) # root为存放数据集的相对路线
test_set = torchvision.datasets.CIFAR10(root="./dataset",train=False,download=True) # train=True是训练集，train=False是测试集  

print(test_set[0])       # 输出的3是target
print(test_set.classes)  # 测试数据集中有多少种

img, target = test_set[0] # 分别获得图片、target
print(img)
print(target)

print(test_set.classes[target]) # 3号target对应的种类
img.show()

Files already downloaded and verified
Files already downloaded and verified
(<PIL.Image.Image image mode=RGB size=32x32 at 0x1A4275AAF28>, 3)
['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
<PIL.Image.Image image mode=RGB size=32x32 at 0x1A4275AAA58>
3
cat

4. Tensorboard查看内容

import torchvision
from torch.utils.tensorboard import SummaryWriter

dataset_transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
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
test_set = torchvision.datasets.CIFAR10(root="./dataset",train=False,transform=dataset_transform,download=True)   

writer = SummaryWriter("logs")
for i in range(10):
    img, target = test_set[i]
    writer.add_image("test_set",img,i)
    print(img.size())

writer.close() # 一定要把读写关闭，否则显示不出来图片

Files already downloaded and verified
Files already downloaded and verified
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])
torch.Size([3, 32, 32])

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   #绝对地址
或者使用下面指令：
tensorboard --logdir=logs    #相对地址

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。