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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#xff0c;前文说过
torchvision.datasets.MNIST 这些 torchvision.datasets里面的数据集都是继承Dataset而来#xff0c…目录
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 i1breakreturn img,labelds CDataSet(rE:\test\pythonProject\dataset\cloudy)
dl DataLoader(ds,batch_size16,shuffleTrue)
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,transformtrans)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_size16,shuffleTrue)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。
