Returns a sparse copy of the tensor. PyTorch supports sparse tensors in coordinate format.
>>> d = torch.tensor([[0, 0, 0], [9, 0, 10], [0, 0, 0]])
>>> d
tensor([[ 0, 0, 0],
[ 9, 0, 10],
[ 0, 0, 0]])
>>> d.to_sparse()
tensor(indices=tensor([[1, 1],
[0, 2]]),
values=tensor([ 9, 10]),
size=(3, 3), nnz=2, layout=torch.sparse_coo)
>>> d.to_sparse(1)
tensor(indices=tensor([[1]]),
values=tensor([[ 9, 0, 10]]),
size=(3, 3), nnz=1, layout=torch.sparse_coo)
Returns a sparse tensor with the specified layout and blocksize. If the self is strided, the number of dense dimensions could be specified, and a hybrid sparse tensor will be created, with dense_dim dense dimensions and self.dim() - 2 - dense_dim batch dimension.
Note
If the self layout and blocksize parameters match with the specified layout and blocksize, return self. Otherwise, return a sparse tensor copy of self.
>>> x = torch.tensor([[1, 0], [0, 0], [2, 3]])
>>> x.to_sparse(layout=torch.sparse_coo)
tensor(indices=tensor([[0, 2, 2],
[0, 0, 1]]),
values=tensor([1, 2, 3]),
size=(3, 2), nnz=3, layout=torch.sparse_coo)
>>> x.to_sparse(layout=torch.sparse_bsr, blocksize=(1, 2))
tensor(crow_indices=tensor([0, 1, 1, 2]),
col_indices=tensor([0, 0]),
values=tensor([[[1, 0]],
[[2, 3]]]), size=(3, 2), nnz=2, layout=torch.sparse_bsr)
>>> x.to_sparse(layout=torch.sparse_bsr, blocksize=(2, 1))
RuntimeError: Tensor size(-2) 3 needs to be divisible by blocksize[0] 2
>>> x.to_sparse(layout=torch.sparse_csr, blocksize=(3, 1))
RuntimeError: to_sparse for Strided to SparseCsr conversion does not use specified blocksize
>>> x = torch.tensor([[[1], [0]], [[0], [0]], [[2], [3]]])
>>> x.to_sparse(layout=torch.sparse_csr, dense_dim=1)
tensor(crow_indices=tensor([0, 1, 1, 3]),
col_indices=tensor([0, 0, 1]),
values=tensor([[1],
[2],
[3]]), size=(3, 2, 1), nnz=3, layout=torch.sparse_csr)
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