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 = path
self.list = os.listdir(path)
self.len = len(self.list)
self.name = ['cloudy','rain','shine','sunrise']
self.trans = transforms.ToTensor()
def __len__(self):
return self.len
def __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+1
break
return img,label
ds = 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]],
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[[0.7176, 0.7020, 0.6941, ..., 0.6235, 0.6275, 0.6392],
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[0.6941, 0.6863, 0.6706, ..., 0.6471, 0.6510, 0.6627],
...,
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[0.2863, 0.2784, 0.2863, ..., 0.2902, 0.3098, 0.2824]],
[[0.7412, 0.7294, 0.7176, ..., 0.6471, 0.6510, 0.6627],
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...,
[0.1961, 0.2196, 0.2000, ..., 0.2000, 0.2000, 0.1961],
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[[[0.3176, 0.3255, 0.3294, ..., 0.5529, 0.5255, 0.4824],
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...,
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[0.3255, 0.3333, 0.3373, ..., 0.6039, 0.5686, 0.5333],
[0.3216, 0.3255, 0.3255, ..., 0.6157, 0.5412, 0.5098],
...,
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[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],
...,
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[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 transforms
trans = 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],
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...,
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...,
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...,
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[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。
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