torch.sparse_csr_tensor#

torch.sparse_csr_tensor(crow_indices,col_indices,values,size=None,*,dtype=None,device=None,pin_memory=False,requires_grad=False,check_invariants=None)→Tensor#

Constructs asparse tensor in CSR (Compressed Sparse Row) with specifiedvalues at the givencrow_indices andcol_indices. Sparse matrix multiplication operationsin CSR format are typically faster than that for sparse tensors in COO format. Make you have a lookatthe note on the data type of the indices.

Note

If thedevice argument is not specified the device of the givenvalues and indices tensor(s) must match. If, however, theargument is specified the input Tensors will be converted to thegiven device and in turn determine the device of the constructedsparse tensor.

Parameters

• crow_indices (array_like) – (B+1)-dimensional array of size(*batchsize,nrows+1). The last element of each batchis the number of non-zeros. This tensor encodes the index invalues and col_indices depending on where the given rowstarts. Each successive number in the tensor subtracted by thenumber before it denotes the number of elements in a givenrow.

• col_indices (array_like) – Column coordinates of each element invalues. (B+1)-dimensional tensor with the same lengthas values.

• values (array_list) – Initial values for the tensor. Can be a list,tuple, NumPyndarray, scalar, and other types thatrepresents a (1+K)-dimensional tensor whereK is the numberof dense dimensions.

• size (list, tuple,torch.Size, optional) – Size of thesparse tensor:(*batchsize,nrows,ncols,*densesize). Ifnot provided, the size will be inferred as the minimum sizebig enough to hold all non-zero elements.

Keyword Arguments

• dtype (torch.dtype, optional) – the desired data type ofreturned tensor. Default: if None, infers data type fromvalues.

• device (torch.device, optional) – the desired device ofreturned tensor. Default: if None, uses the current devicefor the default tensor type (seetorch.set_default_device()).device will bethe CPU for CPU tensor types and the current CUDA device forCUDA tensor types.

• pin_memory (bool,optional) – If set, returned tensor would be allocated inthe pinned memory. Works only for CPU tensors. Default:False.

• requires_grad (bool,optional) – If autograd should record operations on thereturned tensor. Default:False.

• check_invariants (bool,optional) – If sparse tensor invariants are checked.Default: as returned bytorch.sparse.check_sparse_tensor_invariants.is_enabled(),initially False.

Example:

>>>crow_indices=[0,2,4]>>>col_indices=[0,1,0,1]>>>values=[1,2,3,4]>>>torch.sparse_csr_tensor(torch.tensor(crow_indices,dtype=torch.int64),...torch.tensor(col_indices,dtype=torch.int64),...torch.tensor(values),dtype=torch.double)tensor(crow_indices=tensor([0, 2, 4]),       col_indices=tensor([0, 1, 0, 1]),       values=tensor([1., 2., 3., 4.]), size=(2, 2), nnz=4,       dtype=torch.float64, layout=torch.sparse_csr)