def _get_reward(self, y_hat, y, n_gram=6):
# NLTK의 sentence_gleu 함수를 사용하여,y와 y_hat의 GLEU 값을 구해서 출력
# This method gets the reward based on the sampling result and reference sentence.
# For now, we uses GLEU in NLTK, but you can used your own well-defined reward function.
# In addition, GLEU is variation of BLEU, and it is more fit to reinforcement learning.
# Since we don't calculate reward score exactly as same as multi-bleu.perl,
# (especialy we do have different tokenization,) I recommend to set n_gram to 6.
# |y| = (batch_size, length1)
# |y_hat| = (batch_size, length2)
scores = []
# Actually, below is really far from parallized operations.
# Thus, it may cause slow training.
for b in range(y.size(0)):
ref = []
hyp = []
for t in range(y.size(1)):
ref += [str(int(y[b, t]))]
if y[b, t] == data_loader.EOS:
break
for t in range(y_hat.size(1)):
hyp += [str(int(y_hat[b, t]))]
if y_hat[b, t] == data_loader.EOS:
break
# for nltk.bleu & nltk.gleu
scores += [score_func([ref], hyp, max_len=n_gram) * 100.]
# for utils.score_sentence
# scores += [score_func(ref, hyp, 4, smooth = 1)[-1] * 100.]
scores = torch.FloatTensor(scores).to(y.device)
# |scores| = (batch_size)
return scores
def _get_reward(y_hat, y, n_gram=6):
# This method gets the reward based on the sampling result and reference sentence.
# For now, we uses GLEU in NLTK, but you can used your own well-defined reward function.
# In addition, GLEU is variation of BLEU, and it is more fit to reinforcement learning.
# Since we don't calculate reward score exactly as same as multi-bleu.perl,
# (especialy we do have different tokenization,) I recommend to set n_gram to 6.
# |y| = (batch_size, length1)
# |y_hat| = (batch_size, length2)
with torch.no_grad():
scores = []
for b in range(y.size(0)):
ref = []
hyp = []
for t in range(y.size(-1)):
ref += [str(int(y[b, t]))]
if y[b, t] == data_loader.EOS:
break
for t in range(y_hat.size(-1)):
hyp += [str(int(y_hat[b, t]))]
if y_hat[b, t] == data_loader.EOS:
break
# Below lines are slower than naive for loops in above.
# ref = y[b].masked_select(y[b] != data_loader.PAD).tolist()
# hyp = y_hat[b].masked_select(y_hat[b] != data_loader.PAD).tolist()
# for nltk.bleu & nltk.gleu
scores += [score_func([ref], hyp, max_len=n_gram) * 100.]
scores = torch.FloatTensor(scores).to(y.device)
# |scores| = (batch_size)
return scores
