pytorch常用的数据预处理

DataLoader

• 使用dataloader方便数据取出。定义CustomDataset类方便对接dataloader类型

class CustomDataset(torch.utils.data.Dataset):
    def __init__(self):
        # TODO
        # 1. Initialize file paths or a list of file names. 
        pass
    def __getitem__(self, index):
        # TODO
        # 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
        # 2. Preprocess the data (e.g. torchvision.Transform).
        # 3. Return a data pair (e.g. image and label).
        pass
    def __len__(self):
        # You should change 0 to the total size of your dataset.
        return 0 
custom_dataset = CustomDataset()
train_loader=torch.utils.data.DataLoader(dataset=custom_dataset,batch_size=64,shuffle=True)
data_iter=iter(train_loader)#迭代器方便取minibatch
images,labels=data_iter.next()#取出一个mini-batch的数据

举例：