如何用新的填充张量就地替换torch.tensor？

如何解决如何用新的填充张量就地替换torch.tensor？

我试图弄清楚我如何用一个新的torch.tensor覆盖位于dict中的torch.tensor对象，由于填充，该torch.tensor会更长一些。

# pad the tensor
zeros = torch.zeros(55).long()
zeros[zeros == 0] = 100  # change to padding
temp_input = torch.cat([batch['input_ids'][0][0],zeros],dim=-1) # cat
temp_input.shape  # [567]

batch['input_ids'][0][0].shape  # [512]

batch['input_ids'][0][0] = temp_input
# The expanded size of the tensor (512) must match the existing size (567) at non-singleton dimension 0.  Target sizes: [512].  Tensor sizes: [567]

我正在努力寻找一种方法来就地扩展张量或在尺寸变化时覆盖它们。

该字典是从火炬的DataLoader发出的，看起来像这样：

{'input_ids': tensor([[[  101,3720,2011,...,25786,2135,102]],[[  101,1017,2233,0]],1996,2899,14262,20693,2197,2305,2000,102]]]),'attn_mask': tensor([[[1,1,1]],[[1,1]]]),'cats': tensor([[-0.6410,0.1481,-2.1568,-0.6976],[-0.4725,0.7869],[-0.6410,-0.9842,-0.6976]],grad_fn=<StackBackward>),'target': tensor([[1],[0],[1],[1]]),'idx': tensor([1391,4000,293,830])}