torch.sparse_csr_tensor¶
-
torch.
sparse_csr_tensor
(crow_indices, col_indices, values, size=None, *, dtype=None, device=None, requires_grad=False) → Tensor¶ Constructs a sparse tensor in CSR (Compressed Sparse Row) with specified values at the given
crow_indices
andcol_indices
. Sparse matrix multiplication operations in CSR format are typically faster than that for sparse tensors in COO format. Make you have a look at the note on the data type of the indices.- Parameters
crow_indices (array_like) – One-dimensional array of size size[0] + 1. The last element is the number of non-zeros. This tensor encodes the index in values and col_indices depending on where the given row starts. Each successive number in the tensor subtracted by the number before it denotes the number of elements in a given row.
col_indices (array_like) – Column co-ordinates of each element in values. Strictly one dimensional tensor with the same length as values.
values (array_list) – Initial values for the tensor. Can be a list, tuple, NumPy
ndarray
, scalar, and other types.size (list, tuple,
torch.Size
, optional) – Size of the sparse tensor. If not provided, the size will be inferred as the minimum size big enough to hold all non-zero elements.
- Keyword Arguments
dtype (
torch.dtype
, optional) – the desired data type of returned tensor. Default: if None, infers data type fromvalues
.device (
torch.device
, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (seetorch.set_default_tensor_type()
).device
will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default:
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)