torch.Tensor.view¶
-
Tensor.
view
(*shape) → Tensor¶ Returns a new tensor with the same data as the
self
tensor but of a differentshape
.The returned tensor shares the same data and must have the same number of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions that satisfy the following contiguity-like condition that ,
Otherwise, it will not be possible to view
self
tensor asshape
without copying it (e.g., viacontiguous()
). When it is unclear whether aview()
can be performed, it is advisable to usereshape()
, which returns a view if the shapes are compatible, and copies (equivalent to callingcontiguous()
) otherwise.- Parameters
shape (torch.Size or int...) – the desired size
Example:
>>> x = torch.randn(4, 4) >>> x.size() torch.Size([4, 4]) >>> y = x.view(16) >>> y.size() torch.Size([16]) >>> z = x.view(-1, 8) # the size -1 is inferred from other dimensions >>> z.size() torch.Size([2, 8]) >>> a = torch.randn(1, 2, 3, 4) >>> a.size() torch.Size([1, 2, 3, 4]) >>> b = a.transpose(1, 2) # Swaps 2nd and 3rd dimension >>> b.size() torch.Size([1, 3, 2, 4]) >>> c = a.view(1, 3, 2, 4) # Does not change tensor layout in memory >>> c.size() torch.Size([1, 3, 2, 4]) >>> torch.equal(b, c) False
-
view
(dtype) → Tensor
Returns a new tensor with the same data as the
self
tensor but of a differentdtype
.dtype
must have the same number of bytes per element asself
’s dtype.Warning
This overload is not supported by TorchScript, and using it in a Torchscript program will cause undefined behavior.
- Parameters
dtype (
torch.dtype
) – the desired dtype
Example:
>>> x = torch.randn(4, 4) >>> x tensor([[ 0.9482, -0.0310, 1.4999, -0.5316], [-0.1520, 0.7472, 0.5617, -0.8649], [-2.4724, -0.0334, -0.2976, -0.8499], [-0.2109, 1.9913, -0.9607, -0.6123]]) >>> x.dtype torch.float32 >>> y = x.view(torch.int32) >>> y tensor([[ 1064483442, -1124191867, 1069546515, -1089989247], [-1105482831, 1061112040, 1057999968, -1084397505], [-1071760287, -1123489973, -1097310419, -1084649136], [-1101533110, 1073668768, -1082790149, -1088634448]], dtype=torch.int32) >>> y[0, 0] = 1000000000 >>> x tensor([[ 0.0047, -0.0310, 1.4999, -0.5316], [-0.1520, 0.7472, 0.5617, -0.8649], [-2.4724, -0.0334, -0.2976, -0.8499], [-0.2109, 1.9913, -0.9607, -0.6123]]) >>> x.view(torch.int16) Traceback (most recent call last): File "<stdin>", line 1, in <module> RuntimeError: Viewing a tensor as a new dtype with a different number of bytes per element is not supported.