Note
If the device argument is not specified the device of the given values and indices tensor(s) must match. If, however, the argument is specified the input Tensors will be converted to the given device and in turn determine the device of the constructed sparse tensor.
>>> 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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