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 $d, d+1, \dots, d+k$ that satisfy the following contiguitylike condition that $\forall i = d, \dots, d+k1$,
$\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]$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
.If the element size of
dtype
is different than that ofself.dtype
, then the size of the last dimension of the output will be scaled proportionally. For instance, ifdtype
element size is twice that ofself.dtype
, then each pair of elements in the last dimension ofself
will be combined, and the size of the last dimension of the output will be half that ofself
. Ifdtype
element size is half that ofself.dtype
, then each element in the last dimension ofself
will be split in two, and the size of the last dimension of the output will be double that ofself
. For this to be possible, the following conditions must be true:self.dim()
must be greater than 0.self.stride(1)
must be 1.
Additionally, if the element size of
dtype
is greater than that ofself.dtype
, the following conditions must be true as well:self.size(1)
must be divisible by the ratio between the element sizes of the dtypes.self.storage_offset()
must be divisible by the ratio between the element sizes of the dtypes.The strides of all dimensions, except the last dimension, must be divisible by the ratio between the element sizes of the dtypes.
If any of the above conditions are not met, an error is thrown.
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.cfloat) tensor([[ 0.00470.0310j, 1.49990.5316j], [0.1520+0.7472j, 0.56170.8649j], [2.47240.0334j, 0.29760.8499j], [0.2109+1.9913j, 0.96070.6123j]]) >>> x.view(torch.cfloat).size() torch.Size([4, 2]) >>> x.view(torch.uint8) tensor([[ 0, 202, 154, 59, 182, 243, 253, 188, 185, 252, 191, 63, 240, 22, 8, 191], [227, 165, 27, 190, 128, 72, 63, 63, 146, 203, 15, 63, 22, 106, 93, 191], [205, 59, 30, 192, 112, 206, 8, 189, 7, 95, 152, 190, 12, 147, 89, 191], [ 43, 246, 87, 190, 235, 226, 254, 63, 111, 240, 117, 191, 177, 191, 28, 191]], dtype=torch.uint8) >>> x.view(torch.uint8).size() torch.Size([4, 16])