def __init__(self,
tensor: torch.Tensor,
aux_labels: Optional[torch.Tensor] = None) -> None:
'''Build an Fsa from a tensor with optional aux_labels.
It is useful when loading an Fsa from file.
Args:
tensor:
A torch tensor of dtype `torch.int32` with 4 columns.
Each row represents an arc. Column 0 is the src_state,
column 1 the dest_state, column 2 the label, and column
3 the score.
Caution:
Scores are floats and their binary pattern is
**reinterpreted** as integers and saved in a tensor
of dtype `torch.int32`.
aux_labels:
Optional. If not None, it associates an aux_label with every arc,
so it has as many rows as `tensor`. It is a 1-D tensor of dtype
`torch.int32`.
Returns:
An instance of Fsa.
'''
self._init_internal()
self.arcs: RaggedArc = _fsa_from_tensor(tensor)
self._init_properties()
self._tensor_attr['scores'] = _as_float(self.arcs.values()[:, -1])
if aux_labels is not None:
self.aux_labels = aux_labels.to(torch.int32)
def from_tensor(cls,
tensor: torch.Tensor,
aux_labels: Optional[torch.Tensor] = None) -> 'Fsa':
'''Build an Fsa from a tensor with optional aux_labels.
It is useful when loading an Fsa from file.
Args:
tensor:
A torch tensor of dtype `torch.int32` with 4 columns.
Each row represents an arc. Column 0 is the src_state,
column 1 the dest_state, column 2 the label, and column
3 the score.
Caution:
Scores are floats and their binary pattern is
**reinterpreted** as integers and saved in a tensor
of dtype `torch.int32`.
aux_labels:
Optional. If not None, it associates an aux_label with every arc,
so it has as many rows as `tensor`. It is a 1-D tensor of dtype
`torch.int32`.
Returns:
An instance of Fsa.
'''
ans = cls.__new__(cls)
super(Fsa, ans).__init__()
ans._fsa = _fsa_from_tensor(tensor)
ans._aux_labels = aux_labels
return ans
def __init__(self,
arcs: Union[torch.Tensor, RaggedArc],
aux_labels: Optional[torch.Tensor] = None,
properties=None) -> None:
'''Build an Fsa from a tensor with optional aux_labels.
It is useful when loading an Fsa from file.
Args:
tensor:
A torch tensor of dtype `torch.int32` with 4 columns.
Each row represents an arc. Column 0 is the src_state,
column 1 the dest_state, column 2 the label, and column
3 the score.
Caution:
Scores are floats and their binary pattern is
**reinterpreted** as integers and saved in a tensor
of dtype `torch.int32`.
aux_labels:
Optional. If not None, it associates an aux_label with every arc,
so it has as many rows as `tensor`. It is a 1-D tensor of dtype
`torch.int32`.
properties:
Tensor properties if known (should only be provided by
internal code, as they are not checked; intended for use
by Fsa.clone())
Returns:
An instance of Fsa.
'''
if isinstance(arcs, torch.Tensor):
arcs: RaggedArc = _fsa_from_tensor(arcs)
assert isinstance(arcs, RaggedArc)
# Accessing self.__dict__ bypasses __setattr__.
self.__dict__['arcs'] = arcs
self.__dict__['_properties'] = properties
# - `_tensor_attr`
# It saves attribute values of type torch.Tensor. `shape[0]` of
# attribute values have to be equal to the number of arcs
# in the FSA. There are a couple of standard ones, 'aux_labels'
# (present for transducers), and 'scores'.
#
# - `_non_tensor_attr`
# It saves non-tensor attributes, e.g., :class:`SymbolTable`.
#
# - `_cache`
# It contains tensors for autograd. Users should NOT manipulate it.
# The dict is filled in automagically.
#
# The `_cache` dict contains the following attributes:
#
# - `state_batches`:
# returned by :func:`_k2._get_state_batches`
# - `dest_states`:
# returned by :func:`_k2._get_dest_states`
# - `incoming_arcs`:
# returned by :func:`_k2._get_incoming_arcs`
# - `entering_arc_batches`:
# returned by :func:`_k2._get_entering_arc_index_batches`
# - `leaving_arc_batches`:
# returned by :func:`_k2._get_leaving_arc_index_batches`
# - `forward_scores_tropical`:
# returned by :func:`_k2._get_forward_scores_float`
# with `log_semiring=False`
# - `forward_scores_log`:
# returned by :func:`_k2._get_forward_scores_float` or
# :func:`_get_forward_scores_double` with `log_semiring=True`
# - `tot_scores_tropical`:
# returned by :func:`_k2._get_tot_scores_float` or
# :func:`_k2._get_tot_scores_double` with
# `forward_scores_tropical`.
# - `tot_scores_log`:
# returned by :func:`_k2._get_tot_scores_float` or
# :func:`_k2._get_tot_scores_double` with
# `forward_scores_log`.
# - `backward_scores_tropical`:
# returned by :func:`_k2._get_backward_scores_float` or
# :func:`_k2._get_backward_scores_double` with
# `log_semiring=False`
# - `backward_scores_log_semiring`:
# returned by :func:`_k2._get_backward_scores_float` or
# :func:`_k2._get_backward_scores_double` with
# `log_semiring=True`
# - `entering_arcs`:
# returned by :func:`_k2._get_forward_scores_float` or
# :func:`_get_forward_scores_double` with `log_semiring=False`
for name in ['_tensor_attr', '_non_tensor_attr', '_cache']:
self.__dict__[name] = dict()
self._tensor_attr['scores'] = _as_float(self.arcs.values()[:, -1])
if aux_labels is not None:
self.aux_labels = aux_labels.to(torch.int32)
# Access the properties field (it's a @property, i.e. it has a
# getter) which sets up the properties and also checks that
# the FSA is valid.
_ = self.properties
