def test_denseToSparse(self, dtype):
if not cusparse.check_availability('denseToSparse'):
pytest.skip('denseToSparse is not available')
x = cupy.random.uniform(0, 1, self.shape).astype(dtype)
x[x < self.density] = 0
y = cusparse.denseToSparse(x, format=self.format)
assert y.format == self.format
testing.assert_array_equal(x, y.todense())
def dense2csr(a):
if a.dtype.char in 'fdFD':
if cusparse.check_availability('denseToSparse'):
return cusparse.denseToSparse(a, format='csr')
else:
return cusparse.dense2csr(a)
m, n = a.shape
a = cupy.ascontiguousarray(a)
indptr = cupy.zeros(m + 1, dtype=numpy.int32)
info = cupy.zeros(m * n + 1, dtype=numpy.int32)
cupy_dense2csr_step1()(m, n, a, indptr, info)
indptr = cupy.cumsum(indptr, dtype=numpy.int32)
info = cupy.cumsum(info, dtype=numpy.int32)
nnz = int(indptr[-1])
indices = cupy.empty(nnz, dtype=numpy.int32)
data = cupy.empty(nnz, dtype=a.dtype)
cupy_dense2csr_step2()(m, n, a, info, indices, data)
return csr_matrix((data, indices, indptr), shape=(m, n))
def _convert_dense(self, x):
if cusparse.check_availability('denseToSparse'):
m = cusparse.denseToSparse(x, format='csc')
else:
m = cusparse.dense2csc(x)
return m.data, m.indices, m.indptr
