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 and col_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 from values.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.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)