pytorch 图像数据集管理-编程知识

1.数据集的管理说明

2.数据集Dataset类说明

3.图像分类常用的类 ImageFolder

1.数据集的管理说明

pytorch使用Dataset来管理训练和测试数据集，前文说过

torchvision.datasets.MNIST

这些 torchvision.datasets里面的数据集都是继承Dataset而来，对Datasetd 管理使用DataLoader，我们使用的的时候，只需要把Dataset类放在DataLoader这个容器里面，在训练的时候 for循环从DataLoader容器里面取出批次的数据，对模型进行训练。

2.数据集Dataset类说明

我们可以继承Dataset类，对训练和测试数据进行管理，继承Dataset示例：

import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torch.utils.data import DataLoader
from torchvision import transforms
import os
import cv2
#继承from torch.utils.data import Dataset
class CDataSet(Dataset):def __init__(self,path):self.path = pathself.list = os.listdir(path)self.len = len(self.list)self.name = ['cloudy','rain','shine','sunrise']self.trans = transforms.ToTensor()def __len__(self):return self.lendef __getitem__(self, item):self.imgpath = os.path.join(self.path,self.list[item])print(self.imgpath)img = cv2.imread(self.imgpath)img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)img = cv2.resize(img,(100,100))img = self.trans(img)for i,n in enumerate(self.name):if n in self.imgpath:label = i+1breakreturn img,labelds = CDataSet(r'E:\test\pythonProject\dataset\cloudy')
dl = DataLoader(ds,batch_size=16,shuffle=True)
print(len(ds))
print(len(dl))
print(type(ds))
print(type(dl))
print(next(iter(dl)))'''
D:\anaconda3\python.exe E:\test\pythonProject\test.py 
300
19
<class '__main__.CDataSet'>
<class 'torch.utils.data.dataloader.DataLoader'>
E:\test\pythonProject\dataset\cloudy\cloudy294.jpg
E:\test\pythonProject\dataset\cloudy\cloudy156.jpg
E:\test\pythonProject\dataset\cloudy\cloudy149.jpg
E:\test\pythonProject\dataset\cloudy\cloudy148.jpg
E:\test\pythonProject\dataset\cloudy\cloudy3.jpg
E:\test\pythonProject\dataset\cloudy\cloudy106.jpg
E:\test\pythonProject\dataset\cloudy\cloudy137.jpg
E:\test\pythonProject\dataset\cloudy\cloudy276.jpg
E:\test\pythonProject\dataset\cloudy\cloudy147.jpg
E:\test\pythonProject\dataset\cloudy\cloudy8.jpg
E:\test\pythonProject\dataset\cloudy\cloudy164.jpg
E:\test\pythonProject\dataset\cloudy\cloudy293.jpg
E:\test\pythonProject\dataset\cloudy\cloudy116.jpg
E:\test\pythonProject\dataset\cloudy\cloudy56.jpg
E:\test\pythonProject\dataset\cloudy\cloudy187.jpg
E:\test\pythonProject\dataset\cloudy\cloudy177.jpg
[tensor([[[[0.2235, 0.2471, 0.3569,  ..., 0.1490, 0.1373, 0.1373],[0.2902, 0.4039, 0.4078,  ..., 0.1529, 0.1373, 0.1294],[0.3294, 0.4941, 0.4000,  ..., 0.1529, 0.1333, 0.1137],...,[0.0118, 0.0118, 0.0118,  ..., 0.0078, 0.0078, 0.0078],[0.0118, 0.0118, 0.0118,  ..., 0.0039, 0.0039, 0.0039],[0.0118, 0.0118, 0.0118,  ..., 0.0039, 0.0039, 0.0039]],[[0.2196, 0.2471, 0.3608,  ..., 0.1725, 0.1608, 0.1608],[0.2824, 0.3961, 0.4118,  ..., 0.1765, 0.1608, 0.1529],[0.3216, 0.4863, 0.4039,  ..., 0.1765, 0.1569, 0.1373],...,[0.0235, 0.0235, 0.0235,  ..., 0.0078, 0.0078, 0.0078],[0.0235, 0.0235, 0.0235,  ..., 0.0078, 0.0078, 0.0078],[0.0235, 0.0235, 0.0235,  ..., 0.0157, 0.0196, 0.0157]],[[0.3098, 0.3412, 0.4510,  ..., 0.2196, 0.2078, 0.2078],[0.3686, 0.4824, 0.4980,  ..., 0.2235, 0.2078, 0.2000],[0.4078, 0.5725, 0.4863,  ..., 0.2235, 0.2039, 0.1843],...,[0.0000, 0.0000, 0.0000,  ..., 0.0157, 0.0157, 0.0157],[0.0000, 0.0000, 0.0000,  ..., 0.0157, 0.0157, 0.0157],[0.0000, 0.0000, 0.0000,  ..., 0.0078, 0.0039, 0.0078]]],[[[0.7059, 0.6902, 0.6824,  ..., 0.5961, 0.6000, 0.6118],[0.6980, 0.6824, 0.6745,  ..., 0.6039, 0.6078, 0.6196],[0.6863, 0.6706, 0.6588,  ..., 0.6196, 0.6235, 0.6353],...,[0.2706, 0.2941, 0.2706,  ..., 0.2745, 0.2745, 0.2706],[0.2745, 0.2745, 0.2667,  ..., 0.2784, 0.2902, 0.2745],[0.2784, 0.2706, 0.2784,  ..., 0.2824, 0.3020, 0.2784]],[[0.7176, 0.7020, 0.6941,  ..., 0.6235, 0.6275, 0.6392],[0.7098, 0.6941, 0.6863,  ..., 0.6314, 0.6353, 0.6471],[0.6941, 0.6863, 0.6706,  ..., 0.6471, 0.6510, 0.6627],...,[0.2784, 0.3020, 0.2824,  ..., 0.2824, 0.2824, 0.2784],[0.2824, 0.2824, 0.2745,  ..., 0.2863, 0.2980, 0.2824],[0.2863, 0.2784, 0.2863,  ..., 0.2902, 0.3098, 0.2824]],[[0.7412, 0.7294, 0.7176,  ..., 0.6471, 0.6510, 0.6627],[0.7373, 0.7216, 0.7137,  ..., 0.6549, 0.6588, 0.6706],[0.7255, 0.7098, 0.6980,  ..., 0.6706, 0.6745, 0.6863],...,[0.1961, 0.2196, 0.2000,  ..., 0.2000, 0.2000, 0.1961],[0.2000, 0.2000, 0.1922,  ..., 0.2039, 0.2157, 0.2000],[0.2039, 0.1961, 0.2039,  ..., 0.2078, 0.2275, 0.2039]]],[[[0.3176, 0.3255, 0.3294,  ..., 0.5529, 0.5255, 0.4824],[0.3098, 0.3176, 0.3216,  ..., 0.5608, 0.5255, 0.4824],[0.3059, 0.3098, 0.3098,  ..., 0.5686, 0.4941, 0.4588],...,[0.4510, 0.4549, 0.3176,  ..., 0.2627, 0.3059, 0.3333],[0.3843, 0.4980, 0.4000,  ..., 0.3804, 0.4235, 0.3804],[0.4549, 0.6353, 0.7333,  ..., 0.4902, 0.5882, 0.6627]],[[0.3333, 0.3373, 0.3412,  ..., 0.5961, 0.5765, 0.5333],[0.3255, 0.3333, 0.3373,  ..., 0.6039, 0.5686, 0.5333],[0.3216, 0.3255, 0.3255,  ..., 0.6157, 0.5412, 0.5098],...,[0.4275, 0.4275, 0.3255,  ..., 0.2627, 0.2902, 0.3176],[0.3804, 0.4510, 0.3961,  ..., 0.3529, 0.3843, 0.3529],[0.4275, 0.5333, 0.6039,  ..., 0.4353, 0.5098, 0.5569]],[[0.3804, 0.3961, 0.4000,  ..., 0.6667, 0.6431, 0.6000],[0.3725, 0.3804, 0.3843,  ..., 0.6745, 0.6392, 0.6000],[0.3686, 0.3725, 0.3725,  ..., 0.6784, 0.6118, 0.5843],...,[0.3843, 0.3843, 0.3255,  ..., 0.2353, 0.2549, 0.2706],[0.3412, 0.3882, 0.3725,  ..., 0.2902, 0.3098, 0.2863],[0.3804, 0.4039, 0.4275,  ..., 0.3294, 0.3333, 0.3529]]],...,[[[0.5843, 0.6000, 0.6471,  ..., 0.3294, 0.3255, 0.3333],[0.5412, 0.5529, 0.6627,  ..., 0.3373, 0.3333, 0.3373],[0.5137, 0.5098, 0.6235,  ..., 0.3451, 0.3451, 0.3412],...,[0.2980, 0.1098, 0.0824,  ..., 0.0000, 0.0000, 0.0000],[0.0078, 0.0000, 0.0039,  ..., 0.0000, 0.0000, 0.0000],[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]],[[0.5843, 0.6000, 0.6471,  ..., 0.3294, 0.3255, 0.3333],[0.5412, 0.5529, 0.6627,  ..., 0.3373, 0.3333, 0.3373],[0.5137, 0.5098, 0.6235,  ..., 0.3451, 0.3451, 0.3412],...,[0.2980, 0.1098, 0.0824,  ..., 0.0000, 0.0000, 0.0000],[0.0078, 0.0000, 0.0039,  ..., 0.0000, 0.0000, 0.0000],[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]],[[0.5843, 0.6000, 0.6471,  ..., 0.3294, 0.3255, 0.3333],[0.5412, 0.5529, 0.6627,  ..., 0.3373, 0.3333, 0.3373],[0.5137, 0.5098, 0.6235,  ..., 0.3451, 0.3451, 0.3412],...,[0.2980, 0.1098, 0.0824,  ..., 0.0000, 0.0000, 0.0000],[0.0078, 0.0000, 0.0039,  ..., 0.0000, 0.0000, 0.0000],[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]]],[[[0.5608, 0.5843, 0.6196,  ..., 0.4431, 0.4314, 0.4275],[0.5529, 0.5725, 0.6039,  ..., 0.4510, 0.4392, 0.4392],[0.5569, 0.5647, 0.5922,  ..., 0.4588, 0.4510, 0.4549],...,[0.1020, 0.0784, 0.0627,  ..., 0.1255, 0.1373, 0.1216],[0.0431, 0.0627, 0.0510,  ..., 0.0902, 0.1176, 0.1294],[0.0902, 0.1059, 0.0588,  ..., 0.0902, 0.0941, 0.1020]],[[0.6275, 0.6510, 0.6863,  ..., 0.5020, 0.4902, 0.4863],[0.6235, 0.6392, 0.6706,  ..., 0.5098, 0.4980, 0.4980],[0.6196, 0.6314, 0.6588,  ..., 0.5176, 0.5098, 0.5098],...,[0.1373, 0.1176, 0.0980,  ..., 0.1569, 0.1725, 0.1569],[0.0784, 0.0941, 0.0863,  ..., 0.1255, 0.1529, 0.1647],[0.1255, 0.1412, 0.0941,  ..., 0.1255, 0.1294, 0.1373]],[[0.6039, 0.6275, 0.6627,  ..., 0.4824, 0.4706, 0.4667],[0.5961, 0.6157, 0.6471,  ..., 0.4902, 0.4784, 0.4784],[0.5961, 0.6078, 0.6353,  ..., 0.4980, 0.4902, 0.4941],...,[0.1255, 0.1020, 0.0863,  ..., 0.1451, 0.1608, 0.1451],[0.0667, 0.0863, 0.0745,  ..., 0.1137, 0.1412, 0.1529],[0.1137, 0.1294, 0.0824,  ..., 0.1137, 0.1176, 0.1255]]],[[[0.1922, 0.1882, 0.1843,  ..., 0.1608, 0.1647, 0.1686],[0.1961, 0.1922, 0.1882,  ..., 0.1686, 0.1686, 0.1725],[0.2000, 0.2000, 0.1961,  ..., 0.1804, 0.1804, 0.1843],...,[0.3686, 0.3882, 0.3961,  ..., 0.3098, 0.3098, 0.3098],[0.3765, 0.3882, 0.3882,  ..., 0.2980, 0.2980, 0.2980],[0.3725, 0.3804, 0.3804,  ..., 0.2941, 0.2941, 0.2941]],[[0.1922, 0.1882, 0.1843,  ..., 0.1608, 0.1647, 0.1686],[0.1961, 0.1922, 0.1882,  ..., 0.1686, 0.1686, 0.1725],[0.2000, 0.2000, 0.1961,  ..., 0.1804, 0.1804, 0.1843],...,[0.3686, 0.3882, 0.3961,  ..., 0.3098, 0.3098, 0.3098],[0.3765, 0.3882, 0.3882,  ..., 0.2980, 0.2980, 0.2980],[0.3725, 0.3804, 0.3804,  ..., 0.2941, 0.2941, 0.2941]],[[0.1922, 0.1882, 0.1843,  ..., 0.1608, 0.1647, 0.1686],[0.1961, 0.1922, 0.1882,  ..., 0.1686, 0.1686, 0.1725],[0.2000, 0.2000, 0.1961,  ..., 0.1804, 0.1804, 0.1843],...,[0.3686, 0.3882, 0.3961,  ..., 0.3098, 0.3098, 0.3098],[0.3765, 0.3882, 0.3882,  ..., 0.2980, 0.2980, 0.2980],[0.3725, 0.3804, 0.3804,  ..., 0.2941, 0.2941, 0.2941]]]]), tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])]进程已结束，退出代码为 0'''

这里用到的文件夹如图：

注意：这里主要写

def __init__(self,path):

def __len__(self):

def __getitem__(self, item):

这三个函数

3.图像分类常用的类 ImageFolder

ImageFolder 使用示例：

首先整理图像分类分别放在不同的文件夹里面：

然后直接使用 ImageFolder 装载 dataset 文件夹，就会自动分类图片形成数据集可以直接使用：

import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torch.utils.data import DataLoader
from torchvision import transformstrans = transforms.Compose([transforms.Resize((96,96)),transforms.ToTensor()])
ds = datasets.ImageFolder("./dataset",transform=trans)test_ds,train_ds = torch.utils.data.random_split(ds,[len(ds)//5,len(ds)-len(ds)//5])#注意这里需要整除因为这里使用整数
dl = DataLoader(train_ds,batch_size=16,shuffle=True)print(ds.classes)
print(ds.class_to_idx)
print(len(test_ds))
print(len(train_ds))
print(next(iter(dl)))'''
D:\anaconda3\python.exe E:\test\pythonProject\test.py 
['cloudy', 'rain', 'shine', 'sunrise']
{'cloudy': 0, 'rain': 1, 'shine': 2, 'sunrise': 3}
225
900
[tensor([[[[0.0980, 0.0745, 0.0706,  ..., 0.4431, 0.4314, 0.4157],[0.0627, 0.0667, 0.0706,  ..., 0.4941, 0.4510, 0.4510],[0.1529, 0.1451, 0.1412,  ..., 0.3882, 0.4275, 0.4510],...,[0.1176, 0.1176, 0.1176,  ..., 0.1333, 0.1255, 0.1608],[0.1137, 0.1137, 0.1137,  ..., 0.1373, 0.1569, 0.2039],[0.1098, 0.1098, 0.1098,  ..., 0.1294, 0.1961, 0.2824]],[[0.2745, 0.2314, 0.2118,  ..., 0.3843, 0.3725, 0.3569],[0.1922, 0.1765, 0.1686,  ..., 0.4353, 0.3922, 0.3922],[0.2275, 0.2000, 0.1843,  ..., 0.3294, 0.3725, 0.3961],...,[0.0353, 0.0353, 0.0353,  ..., 0.0784, 0.0667, 0.1059],[0.0314, 0.0314, 0.0314,  ..., 0.0784, 0.0824, 0.1216],[0.0275, 0.0275, 0.0275,  ..., 0.0745, 0.1137, 0.1725]],[[0.4471, 0.4118, 0.3961,  ..., 0.3647, 0.3529, 0.3373],[0.3490, 0.3373, 0.3333,  ..., 0.4235, 0.3804, 0.3765],[0.3529, 0.3333, 0.3255,  ..., 0.3216, 0.3608, 0.3882],...,[0.0235, 0.0235, 0.0235,  ..., 0.0431, 0.0353, 0.0549],[0.0196, 0.0196, 0.0196,  ..., 0.0471, 0.0392, 0.0392],[0.0157, 0.0157, 0.0157,  ..., 0.0353, 0.0549, 0.0706]]],[[[0.0941, 0.0941, 0.0196,  ..., 0.1490, 0.1961, 0.1490],[0.1059, 0.1137, 0.0471,  ..., 0.1529, 0.1412, 0.1176],[0.0745, 0.1255, 0.1059,  ..., 0.1569, 0.1373, 0.1176],...,[0.2196, 0.2549, 0.3059,  ..., 0.4000, 0.3922, 0.3765],[0.2118, 0.2471, 0.3020,  ..., 0.3804, 0.3686, 0.3608],[0.1922, 0.2235, 0.2784,  ..., 0.3882, 0.3843, 0.3725]],[[0.2000, 0.1725, 0.0431,  ..., 0.1686, 0.2196, 0.1569],[0.2196, 0.2039, 0.0706,  ..., 0.1765, 0.1647, 0.1373],[0.2000, 0.2275, 0.1373,  ..., 0.1804, 0.1608, 0.1412],...,[0.2157, 0.2510, 0.3059,  ..., 0.3804, 0.3686, 0.3647],[0.2118, 0.2471, 0.3020,  ..., 0.3686, 0.3529, 0.3569],[0.1922, 0.2235, 0.2784,  ..., 0.3843, 0.3804, 0.3686]],[[0.1961, 0.1765, 0.0627,  ..., 0.1725, 0.2196, 0.1647],[0.2118, 0.2039, 0.0941,  ..., 0.1804, 0.1647, 0.1451],[0.1882, 0.2235, 0.1569,  ..., 0.1843, 0.1608, 0.1608],...,[0.1961, 0.2314, 0.2980,  ..., 0.3804, 0.3686, 0.3608],[0.1961, 0.2314, 0.2941,  ..., 0.3647, 0.3529, 0.3490],[0.1843, 0.2118, 0.2706,  ..., 0.3765, 0.3725, 0.3608]]],[[[0.7804, 0.7804, 0.7804,  ..., 0.6627, 0.6588, 0.6549],[0.7765, 0.7765, 0.7765,  ..., 0.6588, 0.6549, 0.6510],[0.7725, 0.7725, 0.7725,  ..., 0.6471, 0.6431, 0.6431],...,[0.1216, 0.1333, 0.1490,  ..., 0.1647, 0.1647, 0.1608],[0.1216, 0.1255, 0.1451,  ..., 0.1725, 0.1725, 0.1765],[0.1176, 0.1255, 0.1451,  ..., 0.1686, 0.1569, 0.1451]],[[0.7843, 0.7843, 0.7843,  ..., 0.6667, 0.6627, 0.6588],[0.7804, 0.7804, 0.7804,  ..., 0.6627, 0.6588, 0.6549],[0.7765, 0.7765, 0.7765,  ..., 0.6510, 0.6471, 0.6471],...,[0.1608, 0.1490, 0.1373,  ..., 0.1686, 0.1686, 0.1647],[0.1569, 0.1451, 0.1294,  ..., 0.1765, 0.1765, 0.1804],[0.1569, 0.1412, 0.1294,  ..., 0.1725, 0.1608, 0.1490]],[[0.8039, 0.8039, 0.8039,  ..., 0.6863, 0.6824, 0.6784],[0.8000, 0.8000, 0.8000,  ..., 0.6824, 0.6784, 0.6745],[0.7961, 0.7961, 0.7961,  ..., 0.6706, 0.6667, 0.6667],...,[0.0706, 0.0667, 0.0745,  ..., 0.1059, 0.1059, 0.1020],[0.0745, 0.0667, 0.0745,  ..., 0.1137, 0.1137, 0.1176],[0.0745, 0.0706, 0.0745,  ..., 0.1098, 0.0980, 0.0863]]],...,[[[0.0275, 0.1059, 0.2157,  ..., 0.0196, 0.0196, 0.0196],[0.0235, 0.1020, 0.1765,  ..., 0.0235, 0.0235, 0.0196],[0.0196, 0.0902, 0.1255,  ..., 0.0314, 0.0314, 0.0275],...,[0.0784, 0.1059, 0.1255,  ..., 0.1294, 0.1020, 0.0745],[0.0745, 0.0863, 0.1020,  ..., 0.0627, 0.0588, 0.0431],[0.0588, 0.0667, 0.0824,  ..., 0.0667, 0.0627, 0.0353]],[[0.0275, 0.1059, 0.2157,  ..., 0.0157, 0.0157, 0.0157],[0.0235, 0.1020, 0.1765,  ..., 0.0235, 0.0235, 0.0196],[0.0196, 0.0902, 0.1255,  ..., 0.0314, 0.0314, 0.0275],...,[0.0588, 0.0863, 0.1059,  ..., 0.1059, 0.0824, 0.0549],[0.0549, 0.0667, 0.0824,  ..., 0.0471, 0.0431, 0.0275],[0.0392, 0.0471, 0.0627,  ..., 0.0588, 0.0510, 0.0275]],[[0.0275, 0.1059, 0.2157,  ..., 0.0275, 0.0275, 0.0235],[0.0235, 0.1020, 0.1765,  ..., 0.0314, 0.0314, 0.0275],[0.0196, 0.0902, 0.1255,  ..., 0.0392, 0.0392, 0.0353],...,[0.0471, 0.0745, 0.0941,  ..., 0.1059, 0.0824, 0.0549],[0.0431, 0.0549, 0.0706,  ..., 0.0431, 0.0392, 0.0235],[0.0275, 0.0353, 0.0510,  ..., 0.0510, 0.0471, 0.0235]]],[[[0.1412, 0.1412, 0.1412,  ..., 0.1647, 0.1686, 0.1765],[0.1451, 0.1373, 0.1333,  ..., 0.1647, 0.1686, 0.1765],[0.1490, 0.1412, 0.1373,  ..., 0.1725, 0.1765, 0.1843],...,[0.0039, 0.0039, 0.0039,  ..., 0.0118, 0.0078, 0.0078],[0.0039, 0.0039, 0.0039,  ..., 0.0078, 0.0039, 0.0039],[0.0039, 0.0039, 0.0039,  ..., 0.0078, 0.0039, 0.0039]],[[0.2118, 0.2078, 0.2078,  ..., 0.2353, 0.2353, 0.2353],[0.2157, 0.2118, 0.2078,  ..., 0.2392, 0.2392, 0.2431],[0.2196, 0.2157, 0.2118,  ..., 0.2431, 0.2431, 0.2431],...,[0.0039, 0.0039, 0.0039,  ..., 0.0118, 0.0078, 0.0078],[0.0039, 0.0039, 0.0039,  ..., 0.0078, 0.0039, 0.0039],[0.0039, 0.0039, 0.0039,  ..., 0.0078, 0.0039, 0.0039]],[[0.3137, 0.3137, 0.3216,  ..., 0.3373, 0.3373, 0.3255],[0.3176, 0.3137, 0.3216,  ..., 0.3412, 0.3412, 0.3412],[0.3137, 0.3176, 0.3294,  ..., 0.3451, 0.3451, 0.3451],...,[0.0039, 0.0039, 0.0039,  ..., 0.0118, 0.0078, 0.0078],[0.0039, 0.0039, 0.0039,  ..., 0.0078, 0.0039, 0.0039],[0.0039, 0.0039, 0.0039,  ..., 0.0078, 0.0039, 0.0039]]],[[[0.0157, 0.0157, 0.0157,  ..., 0.0980, 0.0941, 0.0824],[0.0196, 0.0196, 0.0196,  ..., 0.0980, 0.0941, 0.0824],[0.0235, 0.0235, 0.0235,  ..., 0.0980, 0.0941, 0.0824],...,[0.0078, 0.0078, 0.0039,  ..., 0.0157, 0.0196, 0.0196],[0.0039, 0.0039, 0.0039,  ..., 0.0157, 0.0118, 0.0039],[0.0000, 0.0000, 0.0000,  ..., 0.0157, 0.0078, 0.0000]],[[0.0510, 0.0510, 0.0510,  ..., 0.1294, 0.1255, 0.1333],[0.0549, 0.0549, 0.0549,  ..., 0.1294, 0.1255, 0.1333],[0.0588, 0.0588, 0.0588,  ..., 0.1294, 0.1255, 0.1333],...,[0.0078, 0.0078, 0.0039,  ..., 0.0118, 0.0157, 0.0157],[0.0039, 0.0039, 0.0039,  ..., 0.0118, 0.0078, 0.0000],[0.0000, 0.0000, 0.0000,  ..., 0.0118, 0.0039, 0.0000]],[[0.1647, 0.1647, 0.1647,  ..., 0.2824, 0.2784, 0.2706],[0.1686, 0.1686, 0.1686,  ..., 0.2824, 0.2784, 0.2706],[0.1725, 0.1725, 0.1725,  ..., 0.2824, 0.2784, 0.2706],...,[0.0157, 0.0157, 0.0118,  ..., 0.0353, 0.0392, 0.0392],[0.0118, 0.0118, 0.0118,  ..., 0.0353, 0.0314, 0.0235],[0.0078, 0.0078, 0.0078,  ..., 0.0353, 0.0275, 0.0196]]]]), tensor([3, 1, 0, 3, 3, 2, 1, 0, 0, 0, 2, 3, 0, 0, 3, 3])]进程已结束，退出代码为 0'''

注意：这里使用函数

train_ds,test_ds = torch.utils.data.random_split(ds,[len(ds)//5,len(ds)-len(ds)//5])#注意这里需要整除，因为这里需要使用整数。

把数据集分为了训练和测试数据集，从Dataset继承的类都可以用这个分类，记住Dataset和DataLoader这个基础类是在torch里面，而关于图片的处理类基本都在torchvision 里面，比如图片的转换到tensor，图片放大缩小功能。