from torch.utils.data import Dataset
help(Dataset)

Help on class Dataset in module torch.utils.data.dataset:

class Dataset(typing.Generic)
 |  An abstract class representing a :class:`Dataset`.
 |  
 |  All datasets that represent a map from keys to data samples should subclass
 |  it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
 |  data sample for a given key. Subclasses could also optionally overwrite
 |  :meth:`__len__`, which is expected to return the size of the dataset by many
 |  :class:`~torch.utils.data.Sampler` implementations and the default options
 |  of :class:`~torch.utils.data.DataLoader`.
 |  
 |  .. note::
 |    :class:`~torch.utils.data.DataLoader` by default constructs a index
 |    sampler that yields integral indices.  To make it work with a map-style
 |    dataset with non-integral indices/keys, a custom sampler must be provided.
 |  
 |  Method resolution order:
 |      Dataset
 |      typing.Generic
 |      builtins.object
 |  
 |  Methods defined here:
 |  
 |  __add__(self, other:'Dataset[T_co]') -> 'ConcatDataset[T_co]'
 |  
 |  __getattr__(self, attribute_name)
 |  
 |  __getitem__(self, index) -> +T_co
 |  
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |  
 |  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 defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |  
 |  __abstractmethods__ = frozenset()
 |  
 |  __annotations__ = {'functions': typing.Dict[str, typing.Callable]}
 |  
 |  __args__ = None
 |  
 |  __extra__ = None
 |  
 |  __next_in_mro__ = <class 'object'>
 |      The most base type
 |  
 |  __orig_bases__ = (typing.Generic[+T_co],)
 |  
 |  __origin__ = None
 |  
 |  __parameters__ = (+T_co,)
 |  
 |  __tree_hash__ = -9223371886060913604
 |  
 |  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.

from PIL import Image

img_path = "Data/FirstTypeData/train/ants/0013035.jpg"        
img = Image.open(img_path)
img.show()

from torch.utils.data import Dataset
from PIL import Image
import os

class MyData(Dataset):     
    def __init__(self,root_dir,label_dir):    # 该魔术方法当创建一个事例对象时，会自动调用该函数
        self.root_dir = root_dir # self.root_dir 相当于类中的全局变量
        self.label_dir = label_dir     
        self.path = os.path.join(self.root_dir,self.label_dir) # 字符串拼接，根据是Windows或Lixus系统情况进行拼接               
        self.img_path = os.listdir(self.path) # 获得路径下所有图片的地址
        
    def __getitem__(self,idx):
        img_name = self.img_path[idx]
        img_item_path = os.path.join(self.root_dir,self.label_dir,img_name)            
        img = Image.open(img_item_path)
        label = self.label_dir
        return img, label
    
    def __len__(self):
        return len(self.img_path)
    
root_dir = "Data/FirstTypeData/train"
ants_label_dir = "ants"
bees_label_dir = "bees"
ants_dataset = MyData(root_dir, ants_label_dir)
bees_dataset = MyData(root_dir, bees_label_dir)
print(len(ants_dataset))
print(len(bees_dataset))
train_dataset = ants_dataset + bees_dataset # train_dataset 就是两个数据集的集合了     
print(len(train_dataset))

img,label = train_dataset[200]
print("label：",label)
img.show()

124
121
245
label： bees

1. Pytorch加载数据¶

2. 常用数据集两种形式¶

3. 路径直接加载数据¶

4. Dataset加载数据¶