torch.transpose¶
- torch.transpose(input, dim0, dim1) Tensor ¶
Returns a tensor that is a transposed version of
input
. The given dimensionsdim0
anddim1
are swapped.If
input
is a strided tensor then the resultingout
tensor shares its underlying storage with theinput
tensor, so changing the content of one would change the content of the other.If
input
is a sparse tensor then the resultingout
tensor does not share the underlying storage with theinput
tensor.If
input
is a sparse tensor with compressed layout (SparseCSR, SparseBSR, SparseCSC or SparseBSC) the argumentsdim0
anddim1
must be both batch dimensions, or must both be sparse dimensions. The batch dimensions of a sparse tensor are the dimensions preceding the sparse dimensions.Note
Transpositions which interchange the sparse dimensions of a SparseCSR or SparseCSC layout tensor will result in the layout changing between the two options. Transposition of the sparse dimensions of a ` SparseBSR` or SparseBSC layout tensor will likewise generate a result with the opposite layout.
- Parameters:
Example:
>>> x = torch.randn(2, 3) >>> x tensor([[ 1.0028, -0.9893, 0.5809], [-0.1669, 0.7299, 0.4942]]) >>> torch.transpose(x, 0, 1) tensor([[ 1.0028, -0.1669], [-0.9893, 0.7299], [ 0.5809, 0.4942]])
See also
torch.t()
.