torch / torch
torch.sparse_coo_tensor¶
-
torch.
sparse_coo_tensor
(indices, values, size=None, *, dtype=None, device=None, requires_grad=False) → Tensor¶ Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given
indices
with the givenvalues
. A sparse tensor can be uncoalesced, in that case, there are duplicate coordinates in the indices, and the value at that index is the sum of all duplicate value entries: torch.sparse.- Parameters
indices (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy
ndarray
, scalar, and other types. Will be cast to atorch.LongTensor
internally. The indices are the coordinates of the non-zero values in the matrix, and thus should be two-dimensional where the first dimension is the number of tensor dimensions and the second dimension is the number of non-zero values.values (array_like) – Initial values for the tensor. Can be a list, tuple, NumPy
ndarray
, scalar, and other types.size (list, tuple, or
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:
>>> i = torch.tensor([[0, 1, 1], [2, 0, 2]]) >>> v = torch.tensor([3, 4, 5], dtype=torch.float32) >>> torch.sparse_coo_tensor(i, v, [2, 4]) tensor(indices=tensor([[0, 1, 1], [2, 0, 2]]), values=tensor([3., 4., 5.]), size=(2, 4), nnz=3, layout=torch.sparse_coo) >>> torch.sparse_coo_tensor(i, v) # Shape inference tensor(indices=tensor([[0, 1, 1], [2, 0, 2]]), values=tensor([3., 4., 5.]), size=(2, 3), nnz=3, layout=torch.sparse_coo) >>> torch.sparse_coo_tensor(i, v, [2, 4], dtype=torch.float64, device=torch.device('cuda:0')) tensor(indices=tensor([[0, 1, 1], [2, 0, 2]]), values=tensor([3., 4., 5.]), device='cuda:0', size=(2, 4), nnz=3, dtype=torch.float64, layout=torch.sparse_coo) # Create an empty sparse tensor with the following invariants: # 1. sparse_dim + dense_dim = len(SparseTensor.shape) # 2. SparseTensor._indices().shape = (sparse_dim, nnz) # 3. SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:]) # # For instance, to create an empty sparse tensor with nnz = 0, dense_dim = 0 and # sparse_dim = 1 (hence indices is a 2D tensor of shape = (1, 0)) >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1]) tensor(indices=tensor([], size=(1, 0)), values=tensor([], size=(0,)), size=(1,), nnz=0, layout=torch.sparse_coo) # and to create an empty sparse tensor with nnz = 0, dense_dim = 1 and # sparse_dim = 1 >>> S = torch.sparse_coo_tensor(torch.empty([1, 0]), torch.empty([0, 2]), [1, 2]) tensor(indices=tensor([], size=(1, 0)), values=tensor([], size=(0, 2)), size=(1, 2), nnz=0, layout=torch.sparse_coo)
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