- torch.transpose(input, dim0, dim1) Tensor ¶
Returns a tensor that is a transposed version of
input. The given dimensions
inputis a strided tensor then the resulting
outtensor shares its underlying storage with the
inputtensor, so changing the content of one would change the content of the other.
inputis a sparse tensor then the resulting
outtensor does not share the underlying storage with the
inputis a sparse tensor with compressed layout (SparseCSR, SparseBSR, SparseCSC or SparseBSC) the arguments
dim1must be both batch dimensions, or must both be sparse dimensions. The batch dimensions of a sparse tensor are the dimensions preceding the sparse dimensions.
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.
>>> 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]])