[英]Resize PyTorch Tensor
I am currently using the tensor.resize() function to resize a tensor to a new shape t = t.resize(1, 2, 3)
.我目前正在使用 tensor.resize() 函数将张量调整为新形状
t = t.resize(1, 2, 3)
。
This gives me a deprecation warning:这给了我一个弃用警告:
non-inplace resize is deprecated
不推荐使用非就地调整大小
Hence, I wanted to switch over to the tensor.resize_()
function, which seems to be the appropriate in-place replacement.因此,我想切换到
tensor.resize_()
函数,这似乎是适当的就地替换。 However, this leaves me with an然而,这给我留下了一个
cannot resize variables that require grad
无法调整需要 grad 的变量的大小
error.错误。 I can fall back to
我可以回到
from torch.autograd._functions import Resize
Resize.apply(t, (1, 2, 3))
which is what tensor.resize() does in order to avoid the deprecation warning.这就是 tensor.resize() 为了避免弃用警告所做的。 This doesn't seem like an appropriate solution but rather a hack to me.
这对我来说似乎不是一个合适的解决方案,而是一个黑客。 How do I correctly make use of
tensor.resize_()
in this case?在这种情况下,如何正确使用
tensor.resize_()
?
You can instead choose to go with tensor.reshape(new_shape)
or torch.reshape(tensor, new_shape)
as in:您可以选择使用
tensor.reshape(new_shape)
或torch.reshape(tensor, new_shape)
如下所示:
# a `Variable` tensor
In [15]: ten = torch.randn(6, requires_grad=True)
# this would throw RuntimeError error
In [16]: ten.resize_(2, 3)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-16-094491c46baa> in <module>()
----> 1 ten.resize_(2, 3)
RuntimeError: cannot resize variables that require grad
The above RuntimeError
can be resolved or avoided by using tensor.reshape(new_shape)
可以通过使用
tensor.reshape(new_shape)
解决或避免上述RuntimeError
In [17]: ten.reshape(2, 3)
Out[17]:
tensor([[-0.2185, -0.6335, -0.0041],
[-1.0147, -1.6359, 0.6965]])
# yet another way of changing tensor shape
In [18]: torch.reshape(ten, (2, 3))
Out[18]:
tensor([[-0.2185, -0.6335, -0.0041],
[-1.0147, -1.6359, 0.6965]])
Please can you try something like:请你试试这样的:
import torch
x = torch.tensor([[1, 2], [3, 4], [5, 6]])
print(":::",x.resize_(2, 2))
print("::::",x.resize_(3, 3))
Simply use t = t.contiguous().view(1, 2, 3)
if you don't really want to change its data.如果您真的不想更改其数据
t = t.contiguous().view(1, 2, 3)
只需使用t = t.contiguous().view(1, 2, 3)
。
If not the case, the in-place resize_
operation will break the grad computation graph of t
.如果不是这种情况,就地
resize_
操作将破坏t
的梯度计算图。
If it doesn't matter to you, just use t = t.data.resize_(1,2,3)
.如果对您来说无关紧要,只需使用
t = t.data.resize_(1,2,3)
。
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