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import torch
import torch.nn as nn
import torch
from torch.autograd import Variable
import copy
class Beam(object):
def __init__(self, size,sos,eos):
self.size = size
self.tt = torch.cuda
# The score for each translation on the beam.
self.scores = self.tt.FloatTensor(size).zero_()
# The backpointers at each time-step.
self.prevKs = []
# The outputs at each time-step.
self.nextYs = [self.tt.LongTensor(size)
.fill_(0)]
self.nextYs[0][:] = sos
# Has EOS topped the beam yet.
self._eos = eos
self.eosTop = False
# Time and k pair for finished.
self.finished = []
def getCurrentState(self):
"Get the outputs for the current timestep."
batch = self.tt.LongTensor(self.nextYs[-1]).view(-1, 1)
return batch
def getCurrentOrigin(self):
"Get the backpointers for the current timestep."
return self.prevKs[-1]
def advance(self, wordLk):
"""
Given prob over words for every last beam `wordLk` and attention
`attnOut`: Compute and update the beam search.
Parameters:
* `wordLk`- probs of advancing from the last step (K x words)
* `attnOut`- attention at the last step
Returns: True if beam search is complete.
"""
numWords = wordLk.size(1)
# Sum the previous scores.
if len(self.prevKs) > 0:
beamLk = wordLk + self.scores.unsqueeze(1).expand_as(wordLk)
# Don't let EOS have children.
for i in range(self.nextYs[-1].size(0)):
if self.nextYs[-1][i] == self._eos:
beamLk[i] = -1e20
else:
beamLk = wordLk[0]
flatBeamLk = beamLk.view(-1)
bestScores, bestScoresId = flatBeamLk.topk(self.size, 0, True, True)
self.scores = bestScores
# bestScoresId is flattened beam x word array, so calculate which
# word and beam each score came from
prevK = bestScoresId // numWords
self.prevKs.append(prevK)
self.nextYs.append((bestScoresId - prevK * numWords))
for i in range(self.nextYs[-1].size(0)):
if self.nextYs[-1][i] == self._eos:
s = self.scores[i]
self.finished.append((s, len(self.nextYs) - 1, i))
# End condition is when top-of-beam is EOS and no global score.
if self.nextYs[-1][0] == self._eos:
self.eosTop = True
def done(self):
return self.eosTop and len(self.finished) >=self.size
def getFinal(self):
if len(self.finished) == 0:
self.finished.append((self.scores[0], len(self.nextYs) - 1, 0))
self.finished.sort(key=lambda a: -a[0])
if len(self.finished) != self.size:
unfinished=[]
for i in range(self.nextYs[-1].size(0)):
if self.nextYs[-1][i] != self._eos:
s = self.scores[i]
unfinished.append((s, len(self.nextYs) - 1, i))
unfinished.sort(key=lambda a: -a[0])
self.finished+=unfinished[:self.size-len(self.finished)]
return self.finished[:self.size]
def getHyp(self, beam_res):
"""
Walk back to construct the full hypothesis.
"""
hyps=[]
for _,timestep, k in beam_res:
hyp = []
for j in range(len(self.prevKs[:timestep]) - 1, -1, -1):
hyp.append(self.nextYs[j+1][k])
k = self.prevKs[j][k]
hyps.append(hyp[::-1])
return hyps
def buildTargetTokens(self, preds):
sentence=[]
for pred in preds:
tokens = []
for tok in pred:
if tok==self._eos:
break
tokens.append(tok)
sentence.append(tokens)
return sentence
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