Difference between view, reshape, transpose and permute in PyTorch
Contents
PyTorch provides a lot of methods for the Tensor type. Some of these methods
may be confusing for new users. Here, I would like to talk about
view()
vs
reshape()
,
transpose()
vs
permute()
.
view() vs reshape() and transpose()
view() vs transpose()
Both view()
and reshape()
can be used to change the size or shape of
tensors. But they are slightly different.
The view()
has existed for a long time. It will return a tensor with the new
shape. The returned tensor shares the underling data with the original tensor.
If you change the tensor value in the returned tensor, the corresponding value
in the viewed tensor also changes.
On the other hand, it seems that reshape()
has been introduced 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.
As a side note, I found that torch version 0.4.1 and 1.0.1 behaves differently
when you print the id
of original tensor and viewing tensor:
In [1]: import torch
In [2]: a = torch.rand(3, 4)
In [3]: id(a), id(a.storage())
Out[3]: (2236511690472, 2236511611848)
In [4]: b = a.view(2, 6)
In [5]: id(b), id(b.storage())
Out[5]: (2236523527984, 2236470501128)
You see that id
of a.storage()
and b.storage()
is not the same. Isn’t
that their underlying data the same? Why this difference?
I filed an issue in the
PyTorch repo and got answers from the developer. It turns out that to find the
data pointer, we have to use the data_ptr()
method. You will find that their
data pointers are the same.
view() vs transpose()
transpose()
, like view()
can also be used to change the shape of a tensor
and it also returns a new tensor sharing the data with the original tensor:
Returns a tensor that is a transposed version of input. The given dimensions dim0 and dim1 are swapped.
The resulting out tensor shares itâ€™s underlying storage with the input tensor, so changing the content of one would change the content of the other.
One difference is that view()
can only operate on contiguous tensor and the
returned tensor is still contiguous. transpose()
can operate both on
contiguous and noncontiguous tensor. Unlike view()
, the returned tensor may
be not contiguous any more.
But what does contiguous mean?
There is a good answer on SO
which discusses the meaning of contiguous
in Numpy. It also applies to
PyTorch.
As I understand, contiguous
in PyTorch means if the neighboring elements in
the tensor are actually next to each other in memory. Let’s take a simple
example:
x = torch.tensor([[1, 2, 3], [4, 5, 6]]) # x is contiguous
y = torch.transpose(0, 1) # y is noncontiguous
Tensor x
and y
in the above example share the same memory space^{1}.
print(x.data_ptr()) # 94018404758288
print(y.data_ptr()) # 94018404758288
If you check their contiguity with
is_contiguous()
,
you will find that x
is contiguous but y
is not.
print(x.is_contiguous()) # True
print(y.is_contiguous()) # False
Since x is contiguous, x[0][0] and x[0][1] are next to each other in memory. But y[0][0] and y[0][1] is not.
A lot of tensor operations requires that the tensor should be contiguous,
otherwise, an error will be thrown. To make a noncontiguous tensor become
contiguous, use call the
contiguous()
,
which will return a new contiguous tensor. In plain words, it will create a new
memory space for the new tensor and copy the value from the noncontiguous
tensor to the new tensor.
transpose() and permute()
permute()
and tranpose()
are similar. transpose()
can only swap two
dimension. But permute()
can swap all the dimensions. For example:
x = torch.rand(16, 32, 3)
y = x.tranpose(0, 2)
z = x.permute(2, 1, 0)
Note that, in permute()
, you must provide the new order of all the
dimensions. In transpose()
, you can only provide two dimensions. tranpose()
can be thought as a special case of permute()
method in for 2D tensors.
References
 https://discuss.pytorch.org/t/inpytorch04isitrecommendedtousereshapethanviewwhenitispossible/17034
 tensor data pointers.
 view after transpose raises noncontiguous error.
 When to use which, permute, view, transpose.
 Difference between reshape() and view().

To show a tensor’s memory address, use
tensor.data_ptr()
. ↩︎