torch.Tensor.view#

Tensor.view(*shape)→Tensor#

Returns a new tensor with the same data as theself tensor but of adifferentshape.

The returned tensor shares the same data and must have the same numberof elements, but may have a different size. For a tensor to be viewed, the newview size must be compatible with its original size and stride, i.e., each newview dimension must either be a subspace of an original dimension, or only spanacross original dimensions$d,d+1,\dots ,d+k$d,d+1,…,d+k that satisfy the followingcontiguity-like condition that$\mathrm{\forall }i=d,\dots ,d+k-1$∀i=d,…,d+k−1,

$$\text{stride}[i]=\text{stride}[i+1]\times \text{size}[i+1]$$stride[i]=stride[i+1]×size[i+1]

Otherwise, it will not be possible to viewself tensor asshapewithout copying it (e.g., viacontiguous()). When it is unclear whether aview() can be performed, it is advisable to usereshape(), whichreturns a view if the shapes are compatible, and copies (equivalent to callingcontiguous()) otherwise.

Parameters

shape (torch.Size orint...) – 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 theself tensor but of adifferentdtype.

If the element size ofdtype is different than that ofself.dtype,then the size of the last dimension of the output will be scaledproportionally. 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 outputwill be half that ofself. Ifdtype element size is half thatofself.dtype, then each element in the last dimension ofself willbe split in two, and the size of the last dimension of the output will bedouble that ofself. For this to be possible, the following conditionsmust be true:

• self.dim() must be greater than 0.

• self.stride(-1) must be 1.

Additionally, if the element size ofdtype 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 elementsizes of the dtypes.

• self.storage_offset() must be divisible by the ratio between theelement sizes of the dtypes.

• The strides of all dimensions, except the last dimension, must bedivisible 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 Torchscriptprogram will cause undefined behavior.

Parameters

dtype (torch.dtype) – the desired dtype

Example:

>>>x=torch.randn(4,4)>>>xtensor([[ 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.dtypetorch.float32>>>y=x.view(torch.int32)>>>ytensor([[ 1064483442, -1124191867,  1069546515, -1089989247],        [-1105482831,  1061112040,  1057999968, -1084397505],        [-1071760287, -1123489973, -1097310419, -1084649136],        [-1101533110,  1073668768, -1082790149, -1088634448]],    dtype=torch.int32)>>>y[0,0]=1000000000>>>xtensor([[ 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([[ 0.0047-0.0310j,  1.4999-0.5316j],        [-0.1520+0.7472j,  0.5617-0.8649j],        [-2.4724-0.0334j, -0.2976-0.8499j],        [-0.2109+1.9913j, -0.9607-0.6123j]])>>>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])