What's the difference between reshape and view

问题:

In numpy, we use ndarray.reshape() for reshaping an array.

I noticed that in pytorch, people use torch.view(...) for the same purpose, but at the same time, there is also a torch.reshape(...) existing.

So I am wondering what the differences are between them and when I should use either of them?

回答1:

torch.view has existed for a long time. It will return a tensor with the new shape. The returned tensor will share the underling data with the original tensor. See the documentation here.

On the other hand, it seems that torch.reshape has been introduced recently in version 0.4. According to the document, this method will

Returns a tensor with the same data and number of elements as input, but with the specified shape. When possible, the returned tensor will be a view of input. Otherwise, it will be a copy. Contiguous inputs and inputs with compatible strides can be reshaped without copying, but you should not depend on the copying vs. viewing behavior.

It means that torch.reshape may return a copy or a view of the original tensor. You can not count on that to return a view or a copy. According to the developer:

if you need a copy use clone() if you need the same storage use view(). The semantics of reshape() are that it may or may not share the storage and you don't know beforehand.

回答2:

Although both torch.view and torch.reshape are used to reshape tensors, here are the differences between them.

1. As the name suggests, torch.view merely creates a view of the original tensor. The new tensor will always share its data with the original tensor. This means that if you change the original tensor, the reshaped tensor will change and vice versa.

>>> z = torch.zeros(3, 2)
>>> x = z.view(2, 3)
>>> z.fill_(1)
>>> x
tensor([[1., 1., 1.],
        [1., 1., 1.]])

2. To ensure that the new tensor always shares its data with the original, torch.view imposes some contiguity constraints on the shapes of the two tensors [docs]. More often than not this is not a concern, but sometimes torch.view throws an error even if the shapes of the two tensors are compatible. Here's a famous counter-example.

>>> z = torch.zeros(3, 2)
>>> y = z.t()
>>> y.size()
torch.Size([2, 3])
>>> y.view(6)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: invalid argument 2: view size is not compatible with input tensor's
size and stride (at least one dimension spans across two contiguous subspaces).
Call .contiguous() before .view().

3. torch.reshape doesn't impose any contiguity constraints, but also doesn't guarantee data sharing. The new tensor may be a view of the original tensor, or it may be a new tensor altogether.

>>> z = torch.zeros(3, 2)
>>> y = z.reshape(6)
>>> x = z.t().reshape(6)
>>> z.fill_(1)
tensor([[1., 1.],
        [1., 1.],
        [1., 1.]])
>>> y
tensor([1., 1., 1., 1., 1., 1.])
>>> x
tensor([0., 0., 0., 0., 0., 0.])

TL;DR:
If you just want to reshape tensors, use torch.reshape. If you're also concerned about memory usage and want to ensure that the two tensors share the same data, use torch.view.