def arcs_as_tensor(self) -> torch.Tensor:
'''Return the core part of the Fsa (the arcs) serialized to a Tensor.
This can be passed to the constructor, along with the aux_labels if
present, to reconstruct this object.
A more convenient way to serialize a Tensor is to use `as_dict`
and `from_dict`
'''
return _k2.fsa_to_tensor(self.arcs)
def arcs_as_tensor(self) -> torch.Tensor:
'''Return the core part of the Fsa (the arcs) serialized to a Tensor
of int32 type, with shape (num_arcs, 4); the floats are reinterpreted
as int32 and will appear as garbage if printed. This can be passed
to the constructor, along with the aux_labels if present, to
reconstruct this object. A more convenient way to serialize a Tensor
is to use :func:`as_dict` and :func:`from_dict`
'''
return _k2.fsa_to_tensor(self.arcs)
def to_tensor(fsa: Fsa) -> torch.Tensor:
'''Convert an Fsa to a Tensor.
You can save the tensor to disk and read it later
to construct an Fsa.
Note:
The returned Tensor contains only the transition rules, e.g.,
arcs. You may want to save its aux_labels separately if any.
Args:
fsa:
The input Fsa.
Returns:
A `torch.Tensor` of dtype `torch.int32`. It is a 2-D tensor
if the input is a single FSA. It is a 1-D tensor if the input
is a vector of FSAs.
'''
return _k2.fsa_to_tensor(fsa.arcs)
def as_dict(self) -> Dict[str, Any]:
'''Convert this Fsa to a dict (probably for purposes of serialization
, e.g., torch.save).
Caution:
`self.requires_grad` attribute is not saved.
Returns:
A `dict` that can be used to reconstruct this FSA by using
`Fsa.from_dict`.
'''
ans = dict()
ans['arcs'] = _k2.fsa_to_tensor(self.arcs)
for name, value in self.named_tensor_attr(include_scores=False):
ans[name] = value
for name, value in self.named_non_tensor_attr():
ans[name] = value
return ans