torch.sparse_coo_tensor#

torch.sparse_coo_tensor(indices,values,size=None,*,dtype=None,device=None,pin_memory=False,requires_grad=False,check_invariants=None,is_coalesced=None)→Tensor#

Constructs asparse tensor in COO(rdinate) format with specified values at the givenindices.

Note

This function returns anuncoalesced tensor whenis_coalesced isunspecified orNone.

Note

If thedevice argument is not specified the device of the givenvalues and indices tensor(s) must match. If, however, theargument is specified the input Tensors will be converted to thegiven device and in turn determine the device of the constructedsparse tensor.

Parameters

• indices (array_like) – Initial data for the tensor. Can be a list, tuple,NumPyndarray, scalar, and other types. Will be cast to atorch.LongTensorinternally. The indices are the coordinates of the non-zero values in the matrix, and thusshould be two-dimensional where the first dimension is the number of tensor dimensions andthe second dimension is the number of non-zero values.

• values (array_like) – Initial values for the tensor. Can be a list, tuple,NumPyndarray, scalar, and other types.

• size (list, tuple, ortorch.Size, optional) – Size of the sparse tensor. If notprovided the size will be inferred as the minimum size big enough to hold all non-zeroelements.

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_device()).device will be the CPUfor CPU tensor types and the current CUDA device for CUDA tensor types.

• pin_memory (bool,optional) – If set, returned tensor would be allocated inthe pinned memory. Works only for CPU tensors. Default:False.

• requires_grad (bool,optional) – If autograd should record operations on thereturned tensor. Default:False.

• check_invariants (bool,optional) – If sparse tensor invariants are checked.Default: as returned bytorch.sparse.check_sparse_tensor_invariants.is_enabled(),initially False.

• is_coalesced (bool,optional) – When``True``, the caller isresponsible for providing tensor indices that correspond to acoalesced tensor. If thecheck_invariants flag isFalse, no error will be raised if the prerequisites are notmet and this will lead to silently incorrect results. To forcecoalescion please usecoalesce() on the resultingTensor.Default: None: except for trivial cases (e.g. nnz < 2) theresulting Tensor has is_coalesced set toFalse`.

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 inferencetensor(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)