(self, timestep, k)
return hyp[::-1], torch.stack(attn[::-1])
Walk back to construct the full hypothesis.
Walk back to construct the full hypothesis.
Walk back to construct the full hypothesis .
def get_hyp(self, timestep, k):
        """
        Walk back to construct the full hypothesis.
        """
        hyp, attn = [], []
        for j in range(len(self.prev_ks[:timestep]) - 1, -1, -1):
            hyp.append(self.next_ys[j + 1][k])
            attn.append(self.attn[j][k])
            k = self.prev_ks[j][k]
        return hyp[::-1], torch.stack(attn[::-1])
def get_hyp ( self , timestep , k ) : hyp , attn = [ ] , [ ] for j in range ( len ( self . prev_ks [ : timestep ] ) - 1 , - 1 , - 1 ) : hyp . append ( self . next_ys [ j + 1 ] [ k ] ) attn . append ( self . attn [ j ] [ k ] ) k = self . prev_ks [ j ] [ k ] return hyp [ : : - 1 ] , torch . stack ( attn [ : : - 1 ] )