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Я создаю свой собственный набор данных:

 class MyDataset(Dataset):  def __init__(self, folders):   self.folders = folders       def __len__(self):   return len(self.folders)      def __getitem__(self, item):    pos_file_list = glob(self.folders[item]   "/*")   positive_img = pos_file_list[1]    positive_img = mpimg.imread(positive_img)   positive_img = np.transpose(positive_img, (2,0,1))    # positive_img have the type: lt;class 'numpy.ndarray'gt;, shape: (3, 128, 128)   return positive_img  

И я использую его с:

 batch_size = 128  train_ds = MyDataset(train_folder_list)   oTrainDL = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=2)    for i, imgs in enumerate(oTrainDL):   break  

Я получаю следующую гарантию:

 UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:189.)  return default_collate([torch.as_tensor(b) for b in batch])  

Почему я получаю гарантийное сообщение ? Как я могу это исправить ?

изменение с return positive_img на:

 return torch.tensor(positive_img)