def _from_compatible_tensor_list(self, tensor_list):
tensor_list = gen_sparse_ops.deserialize_sparse(
tensor_list[0], self._dtype)
indices, values, dense_shape = tensor_list
rank = self._shape.ndims
indices.set_shape([None, rank])
# We restore the dense_shape from the SparseTypeSpec. This is necessary
# for shape inference when using placeholder SparseTensors in function
# tracing.
if self._shape.is_fully_defined():
dense_shape = ops.convert_to_tensor(self._shape,
dtype=dtypes.int64,
name="shape")
elif (self._shape.rank is not None
and any(dim.value is not None for dim in self._shape.dims)):
# array_ops imports sparse_tensor.py. Local import to avoid import cycle.
from tensorflow.python.ops import array_ops # pylint: disable=g-import-not-at-top
pieces = array_ops.unstack(dense_shape, num=self._shape.rank)
for i, dim in enumerate(self._shape.dims):
if dim.value is not None:
pieces[i] = constant_op.constant(dim.value,
dense_shape.dtype)
dense_shape = array_ops.stack(pieces)
else:
dense_shape.set_shape([rank])
return SparseTensor(indices, values, dense_shape)
def _from_compatible_tensor_list(self, tensor_list): tensor_list = gen_sparse_ops.deserialize_sparse(tensor_list[0], self._dtype) result = SparseTensor(*tensor_list) rank = self._shape.ndims result.indices.set_shape([None, rank]) result.dense_shape.set_shape([rank]) return result
