updown.utils.decoding

updown.utils.decoding.select_best_beam(beams:torch.Tensor, beam_log_probabilities:torch.Tensor) → torch.Tensor

Select the best beam out of a set of decoded beams.

Parameters

beams: torch.Tensor

A tensor of shape (batch_size, num_states, max_decoding_steps) containing decoded beams by BeamSearch. These beams are sorted according to their likelihood (descending) in beam_size dimension.

beam_log_probabilities: torch.Tensor

A tensor of shape (batch_size, num_states, beam_size) containing likelihood of decoded beams.

updown.utils.decoding.select_best_beam_with_constraints(beams:torch.Tensor, beam_log_probabilities:torch.Tensor, given_constraints:torch.Tensor, min_constraints_to_satisfy:int=2) → torch.Tensor

Select the best beam which satisfies specified minimum constraints out of a total number of given constraints.

Note

The implementation of this function goes hand-in-hand with the FSM building implementation in build() - it defines which state satisfies which (basically, how many) constraints. If the “definition” of states change, then selection of beams also changes accordingly.

Parameters

beams: torch.Tensor

A tensor of shape (batch_size, num_states, beam_size, max_decoding_steps) containing decoded beams by ConstrainedBeamSearch. These beams are sorted according to their likelihood (descending) in beam_size dimension.

beam_log_probabilities: torch.Tensor

A tensor of shape (batch_size, num_states, beam_size) containing likelihood of decoded beams.

given_constraints: torch.Tensor

A tensor of shape (batch_size, ) containing number of constraints given at the start of decoding.

min_constraints_to_satisfy: int, optional (default = 2)

Minimum number of constraints to satisfy. This is either 2, or given_constraints if they are less than 2. Beams corresponding to states not satisfying at least these number of constraints will be dropped. Only up to 3 supported.

Returns

torch.Tensor

Decoded sequence (beam) which has highest likelihood among beams satisfying constraints.