def validate(self, valid_iter, step=0):
""" Validate model.
valid_iter: validate data iterator
Returns:
:obj:`nmt.Statistics`: validation loss statistics
"""
# Set model in validating mode.
self.model.eval()
stats = Statistics()
with torch.no_grad():
for batch in valid_iter:
src = batch.src
labels = batch.labels
segs = batch.segs
clss = batch.clss
mask = batch.mask
mask_cls = batch.mask_cls
sent_scores, mask = self.model(src, segs, clss, mask, mask_cls)
loss = self.loss(sent_scores, labels.float())
loss = (loss * mask.float()).sum()
batch_stats = Statistics(float(loss.cpu().data.numpy()), len(labels))
stats.update(batch_stats)
self._report_step(0, step, valid_stats=stats)
return stats
def test(self, test_iter, step, cal_lead=False, cal_oracle=False):
""" Validate model.
valid_iter: validate data iterator
Returns:
:obj:`nmt.Statistics`: validation loss statistics
"""
# Set model in validating mode.
def _get_ngrams(n, text):
ngram_set = set()
text_length = len(text)
max_index_ngram_start = text_length - n
for i in range(max_index_ngram_start + 1):
ngram_set.add(tuple(text[i:i + n]))
return ngram_set
def _block_tri(c, p):
tri_c = _get_ngrams(3, c.split())
for s in p:
tri_s = _get_ngrams(3, s.split())
if len(tri_c.intersection(tri_s))>0:
return True
return False
if (not cal_lead and not cal_oracle):
self.model.eval()
stats = Statistics()
can_path = '%s_step%d.txt'%(self.args.result_path,step)
gold_path = '%s_step%d.gold' % (self.args.result_path, step)
with open(can_path, 'w') as save_pred:
with open(gold_path, 'w') as save_gold:
with torch.no_grad():
for batch in test_iter:
src = batch.src
labels = batch.labels
segs = batch.segs
clss = batch.clss
mask = batch.mask
mask_cls = batch.mask_cls
gold = []
pred = []
if (cal_lead):
selected_ids = [list(range(batch.clss.size(1)))] * batch.batch_size
elif (cal_oracle):
selected_ids = [[j for j in range(batch.clss.size(1)) if labels[i][j] == 1] for i in
range(batch.batch_size)]
else:
sent_scores, mask = self.model(src, segs, clss, mask, mask_cls)
loss = self.loss(sent_scores, labels.float())
loss = (loss * mask.float()).sum()
batch_stats = Statistics(float(loss.cpu().data.numpy()), len(labels))
stats.update(batch_stats)
sent_scores = sent_scores + mask.float()
sent_scores = sent_scores.cpu().data.numpy()
selected_ids = np.argsort(-sent_scores, 1)
# selected_ids = np.sort(selected_ids,1)
for i, idx in enumerate(selected_ids):
_pred = []
if(len(batch.src_str[i])==0):
continue
for j in selected_ids[i][:len(batch.src_str[i])]:
if(j>=len( batch.src_str[i])):
continue
candidate = batch.src_str[i][j].strip()
if(self.args.block_trigram):
if(not _block_tri(candidate,_pred)):
_pred.append(candidate)
else:
_pred.append(candidate)
if ((not cal_oracle) and (not self.args.recall_eval) and len(_pred) == 3):
break
_pred = '<q>'.join(_pred)
if(self.args.recall_eval):
_pred = ' '.join(_pred.split()[:len(batch.tgt_str[i].split())])
pred.append(_pred)
gold.append(batch.tgt_str[i])
for i in range(len(gold)):
save_gold.write(gold[i].strip()+'\n')
for i in range(len(pred)):
save_pred.write(pred[i].strip()+'\n')
# if(step!=-1 and self.args.report_rouge):
# rouges = test_rouge(self.args.temp_dir, can_path, gold_path)
# logger.info('Rouges at step %d \n%s' % (step, rouge_results_to_str(rouges)))
self._report_step(0, step, valid_stats=stats)
return stats