torch.triu_indices#

torch.triu_indices(row,col,offset=0,*,dtype=torch.long,device='cpu',layout=torch.strided)→Tensor#

Returns the indices of the upper triangular part of arow bycol matrix in a 2-by-N Tensor, where the first row contains rowcoordinates of all indices and the second row contains column coordinates.Indices are ordered based on rows and then columns.

The upper triangular part of the matrix is defined as the elements on andabove the diagonal.

The argumentoffset controls which diagonal to consider. Ifoffset = 0, all elements on and above the main diagonal areretained. A positive value excludes just as many diagonals above the maindiagonal, and similarly a negative value includes just as many diagonals belowthe main diagonal. The main diagonal are the set of indices$\{(i,i)\}${(i,i)} for$i\in [0,\min \{d_1,d_2\}-1]$i∈[0,min{d1​,d2​}−1]where$d_1,d_2$d1​,d2​ are the dimensions of the matrix.

Note

When running on CUDA,row*col must be less than$2^{59}$259 toprevent overflow during calculation.

Parameters:

• row (int) – number of rows in the 2-D matrix.

• col (int) – number of columns in the 2-D matrix.

• offset (int) – diagonal offset from the main diagonal.Default: if not provided, 0.

Keyword Arguments:

• dtype (torch.dtype, optional) – the desired data type of returned tensor,only supporttorch.int,torch.long. Default: ifNone,torch.long.

• device (torch.device, optional) – the desired device of returned tensor.Default: ifNone, uses the current device for the default tensor type(seetorch.set_default_device()).device will be the CPUfor CPU tensor types and the current CUDA device for CUDA tensor types.

• layout (torch.layout, optional) – currently only supporttorch.strided.

Example:

>>>a=torch.triu_indices(3,3)>>>atensor([[0, 0, 0, 1, 1, 2],        [0, 1, 2, 1, 2, 2]])>>>a=torch.triu_indices(4,3,-1)>>>atensor([[0, 0, 0, 1, 1, 1, 2, 2, 3],        [0, 1, 2, 0, 1, 2, 1, 2, 2]])>>>a=torch.triu_indices(4,3,1)>>>atensor([[0, 0, 1],        [1, 2, 2]])