pytorch加载本地数据集（实例）

数据集： Stanford Dogs

操作：

• dataset :

from torch.utils.data import Dataset, DataLoader

class Mydata(Dataset):
    def __init__(self, root, train=True, transform=None, 
                 target_transform=None):
        self.root = root
        self.train = train
        self.transform = transform
        self.target_transform = target_transform
        
        file_list_label = os.listdir(self.root)
        self.label = []
        self.data = []
        for index, i in enumerate(file_list_label):
            file_list_img = os.listdir(self.root + '/' + i)
            for j in file_list_img:
                imge = Image.open(root + '/' + i + '/' + j).convert('RGB')
                self.label.append(index)
                self.data.append(imge)
    def __getitem__(self, index):
        img, target = self.data[index], self.label[index]
  
        

        if self.transform is not None:
            imgee = self.transform(img)
            #print(imgee.shape)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return imgee, target
    def __len__(self):
        return len(self.data)
mydata = Mydata('./imgg', transform=transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor()]))

注意transforms.Compose的先后顺序。
torchvision.transforms解析

• datasetloader :
  cifarLoader = torch.utils.data.DataLoader(cifarSet, batch_size= 10, shuffle= False, num_workers= 2)