ValueError：图块无法扩展到图像pytorch之外

如何解决ValueError：图块无法扩展到图像pytorch之外

在训练模型时，我正在将NumPy数组作为图像加载到PyTorch中，这给了我这个错误，我尝试了所有操作，但找不到帮助...，我正在训练分类器模型..... ................................................... ................................................... ................................................... .........................

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ValueError                                Traceback (most recent call last)
<ipython-input-12-b4d3f7be01c1> in <module>
      1 # training
----> 2 trained_model = train(n_epochs,np.Inf,loaders,model,optimizer,criterion)

<ipython-input-10-b4d180a2c041> in train(n_epochs,valid_loss_min_input,criterion,device,checkpoint_path,best_model_path)
     29         ###################
     30         model.train()
---> 31         for batch_idx,(data,target) in enumerate(loaders['train']):
     32             # move to gpu
     33             data,target = data.to(device),target.to(device)

G:\anaconda3\envs\data_env\lib\site-packages\torch\utils\data\DataLoader.py in __next__(self)
    343 
    344     def __next__(self):
--> 345         data = self._next_data()
    346         self._num_yielded += 1
    347         if self._dataset_kind == _DatasetKind.Iterable and \

G:\anaconda3\envs\data_env\lib\site-packages\torch\utils\data\DataLoader.py in _next_data(self)
    383     def _next_data(self):
    384         index = self._next_index()  # may raise stopiteration
--> 385         data = self._dataset_fetcher.fetch(index)  # may raise stopiteration
    386         if self._pin_memory:
    387             data = _utils.pin_memory.pin_memory(data)

G:\anaconda3\envs\data_env\lib\site-packages\torch\utils\data\_utils\fetch.py in fetch(self,possibly_batched_index)
     42     def fetch(self,possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

G:\anaconda3\envs\data_env\lib\site-packages\torch\utils\data\_utils\fetch.py in <listcomp>(.0)
     42     def fetch(self,possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

G:\anaconda3\envs\data_env\lib\site-packages\torchvision\datasets\folder.py in __getitem__(self,index)
    135         sample = self.loader(path)
    136         if self.transform is not None:
--> 137             sample = self.transform(sample)
    138         if self.target_transform is not None:
    139             target = self.target_transform(target)

G:\anaconda3\envs\data_env\lib\site-packages\torchvision\transforms\transforms.py in __call__(self,img)
     59     def __call__(self,img):
     60         for t in self.transforms:
---> 61             img = t(img)
     62         return img
     63 

<ipython-input-3-88cdee8f0d6c> in __call__(self,img)
      7         im = np.asarray(img)
      8         im = detect(im)
----> 9         img = Image.fromarray(im)
     10         img = img.resize(size=(128,128))
     11         return img

G:\anaconda3\envs\data_env\lib\site-packages\PIL\Image.py in fromarray(obj,mode)
   2768             obj = obj.tostring()
   2769 
-> 2770     return frombuffer(mode,size,obj,"raw",rawmode,1)
   2771 
   2772 

G:\anaconda3\envs\data_env\lib\site-packages\PIL\Image.py in frombuffer(mode,data,decoder_name,*args)
   2708             return im
   2709 
-> 2710     return frombytes(mode,args)
   2711 
   2712 

G:\anaconda3\envs\data_env\lib\site-packages\PIL\Image.py in frombytes(mode,*args)
   2648 
   2649     im = new(mode,size)
-> 2650     im.frombytes(data,args)
   2651     return im
   2652 

G:\anaconda3\envs\data_env\lib\site-packages\PIL\Image.py in frombytes(self,*args)
    795         # unpack data
    796         d = _getdecoder(self.mode,args)
--> 797         d.setimage(self.im)
    798         s = d.decode(data)
    799 

ValueError: tile cannot extend outside the image

在自定义函数中，我试图对图像进行预处理，然后调用它 当我测试加载一小部分用于显示的代码时，效果很好