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import numpy as np
import scipy.sparse
import torch
from torch import from_numpy
def to_torch_sparse(index, value, m, n):
return torch.sparse_coo_tensor(index.detach(), value, (m, n))
def from_torch_sparse(A):
return A.indices().detach(), A.values()
def to_scipy(index, value, m, n):
assert not index.is_cuda and not value.is_cuda
(row, col), data = index.detach(), value.detach()
return scipy.sparse.coo_matrix((data, (row, col)), (m, n))
def from_scipy(A):
A = A.tocoo()
row, col, value = A.row.astype(np.int64), A.col.astype(np.int64), A.data
row, col, value = from_numpy(row), from_numpy(col), from_numpy(value)
index = torch.stack([row, col], dim=0)
return index, value
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