Thanks for your warning,Similar to mnist single-channel dataset how to customize Dataset , each time there is not an error, but when test, the data size is 0, God knows how to get it,thank you very much
my code like this
def default_loader(path):
    return Image.open(path).convert('L')
class myImageFloder(data.Dataset):
   def __init__(self, root, label,train=True,transform=None, target_transform=None,   loader=default_loader):
        fh = open(label)#实现标签数据的读入
        c = 0
        imgs = []
        class_names = []
        for line in fh.readlines():
             if c == 0:
                 class_names = [n.strip() for n in line.rstrip().split('   ')]#claassname为标签的属性名
             else:
                cls = line.split()
                fn = cls.pop(0)
                if os.path.isfile(os.path.join(root, fn)):
                    imgs.append((fn, tuple([float(v) for v in cls])))
              c = c + 1
              self.root = root
              self.imgs = imgs
              self.classes = class_names
              self.transform = transform
              self.target_transform = target_transform
              self.loader = loader
              self.train = train
  def __getitem__(self, index):
      if self.train:
          img, target = self.train_data[index], self.train_labels[index]
     else:
          img, target = self.test_data[index], self.test_labels[index]
     img = Image.fromarray(img.numpy(), mode='L')
     if self.transform is not None:
         img = self.transform(img)
     if self.target_transform is not None:
         target = self.target_transform(target)
    return img, target
def __len__(self):
    if self.train:
         return 60000
   else:
         return 10000
def getName(self):
     return self.classes

mytransform = transforms.Compose([transforms.ToTensor()])
train_dataset =myImageFloder(root = "/home/zw/ztfd_data/mnist02/train",
                    label = "/home/zw/ztfd_data/mnist02/train.txt",
                    train=True,transform = mytransform)
test_dataset = myImageFloder(root = "/home/zw/ztfd_data/mnist02/test",
                     label = "/home/zw/ztfd_data/mnist02/test.txt",
                     train=False,transform = mytransform)
print(len(train_dataset))
print(len(test_dataset))
train_loader=torch.utils.data.DataLoader(
myImageFloder(root = "/home/zw/ztfd_data/mnist02/train",
        label = "/home/zw/ztfd_data/mnist02/train.txt",transform=mytransform),
       batch_size=64,shuffle=True)
test_loader=torch.utils.data.DataLoader(
myImageFloder(root = "/home/zw/ztfd_data/mnist02/test",
         label = "/home/zw/ztfd_data/mnist02/test.txt", transform=mytransform),
         batch_size=64, shuffle=True)
print(len(train_loader))
print(len(test_loader))

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