I see. You can use a custom collate function to get around this. Similar to:

  
    
    
    How to create a dataloader with variable-size input vision
  
  
    By default, torch stacks the input image to from a tensor of size N*C*H*W, so every image in the batch must have the same height and width. In order to load a batch with variable size input image, we have to use our own collate_fn which is used to pack a batch of images. 
For image classification, the input to collate_fn is a list of with size batch_size. Each element is a tuple where the first element is the input image(a torch.FloatTensor) and the second element is the image label which is sim…