def make_sparse_tensor(values: TensorProto, indices: TensorProto,
dims: Sequence[int]) -> SparseTensorProto:
sparse = SparseTensorProto()
sparse.values.CopyFrom(values)
sparse.indices.CopyFrom(indices)
sparse.dims.extend(dims)
return sparse
def make_sparse_tensor(
values, # type: TensorProto
indices, # type: TensorProto
dims # type: Sequence[int]
): # type: (...) -> SparseTensorProto
sparse = SparseTensorProto()
sparse.values.CopyFrom(values)
sparse.indices.CopyFrom(indices)
sparse.dims.extend(dims)
return sparse
def _build_random_sparse_tensor(shape, sparsity):
array_size = numpy.prod(shape)
num_sparse = int(array_size * sparsity)
array = numpy.random.randn(num_sparse)
indices = numpy.random.choice(
numpy.arange(array_size), replace=False, size=num_sparse
).astype(numpy.int64)
return SparseTensorProto(
values=numpy_helper.from_array(array),
indices=numpy_helper.from_array(indices),
dims=shape,
)
def make_sparse(self,
shape, # type: Sequence[int]
values, # type: Sequence[int]
indices_shape, # type: Sequence[int]
indices # type: Sequence[int]
): # type: (...) -> SparseTensorProto
sparse = SparseTensorProto()
sparse.dims.extend(shape)
nnz = len(values)
sparse.values.CopyFrom(helper.make_tensor('spval', TensorProto.INT64, (nnz,), values))
sparse.indices.CopyFrom(helper.make_tensor('spind', TensorProto.INT64, indices_shape, indices))
return sparse
def make_sparse(self,
shape: Sequence[int],
values: Sequence[int],
indices_shape: Sequence[int],
indices: Sequence[int],
name: Text = 'spval'
) -> SparseTensorProto:
sparse = SparseTensorProto()
sparse.dims.extend(shape)
nnz = len(values)
sparse.values.CopyFrom(helper.make_tensor(name, TensorProto.INT64, (nnz,), values))
sparse.indices.CopyFrom(helper.make_tensor('spind', TensorProto.INT64, indices_shape, indices))
return sparse
def make_sparse_tensor(values: TensorProto, indices: TensorProto,
dims: Sequence[int]) -> SparseTensorProto:
"""Construct a SparseTensorProto
Arguments:
values (TensorProto): the values
indices (TensorProto): the indices
dims: the shape
Returns:
SparseTensorProto
"""
sparse = SparseTensorProto()
sparse.values.CopyFrom(values)
sparse.indices.CopyFrom(indices)
sparse.dims.extend(dims)
return sparse
def check_sparse_tensor(sparse: SparseTensorProto,
ctx: C.CheckerContext = DEFAULT_CONTEXT) -> None:
C.check_sparse_tensor(sparse.SerializeToString(), ctx)