def collect_metrics(self, outputs, target, epoch=-1):
"""
collect_metrics
"""
num_samples = target.size(0)
metrics = Pack(num_samples=num_samples)
loss = 0
# test begin
# nll = self.nll(torch.log(outputs.posterior_attn+1e-10), outputs.attn_index)
# loss += nll
# attn_acc = attn_accuracy(outputs.posterior_attn, outputs.attn_index)
# metrics.add(attn_acc=attn_acc)
# metrics.add(loss=loss)
# return metrics
# test end
logits = outputs.logits
scores = -self.nll_loss(logits, target, reduction=False)
nll_loss = self.nll_loss(logits, target)
num_words = target.ne(self.padding_idx).sum().item()
acc = accuracy(logits, target, padding_idx=self.padding_idx)
metrics.add(nll=(nll_loss, num_words), acc=acc)
# persona loss
if 'attn_index' in outputs:
attn_acc = attn_accuracy(outputs.cue_attn, outputs.attn_index)
metrics.add(attn_acc=attn_acc)
per_logits = torch.log(outputs.cue_attn +
self.eps) # cue_attn(batch_size, sent_num)
per_labels = outputs.attn_index ##(batch_size)
use_per_loss = self.persona_loss(
per_logits, per_labels) # per_labels(batch_size)
metrics.add(use_per_loss=use_per_loss)
loss += 0.7 * use_per_loss
loss += 0.3 * nll_loss
else:
loss += nll_loss
metrics.add(loss=loss)
return metrics, scores
def collect_metrics(self, outputs, target, epoch=-1):
"""
collect_metrics
"""
num_samples = target.size(0)
metrics = Pack(num_samples=num_samples)
loss = 0
# test begin
# nll = self.nll(torch.log(outputs.posterior_attn+1e-10), outputs.attn_index)
# loss += nll
# attn_acc = attn_accuracy(outputs.posterior_attn, outputs.attn_index)
# metrics.add(attn_acc=attn_acc)
# metrics.add(loss=loss)
# return metrics
# test end
logits = outputs.logits
scores = -self.nll_loss(logits, target, reduction=False)
nll_loss = self.nll_loss(logits, target)
num_words = target.ne(self.padding_idx).sum().item()
acc = accuracy(logits, target, padding_idx=self.padding_idx)
metrics.add(nll=(nll_loss, num_words), acc=acc)
if self.use_posterior:
kl_loss = self.kl_loss(torch.log(outputs.prior_attn + 1e-10),
outputs.posterior_attn.detach())
metrics.add(kl=kl_loss)
if self.use_bow:
bow_logits = outputs.bow_logits
bow_labels = target[:, :-1]
bow_logits = bow_logits.repeat(1, bow_labels.size(-1), 1)
bow = self.nll_loss(bow_logits, bow_labels)
loss += bow
metrics.add(bow=bow)
if self.use_dssm:
mse = self.mse_loss(outputs.dssm, outputs.reply_vec.detach())
loss += mse
metrics.add(mse=mse)
pos_logits = outputs.pos_logits
pos_target = torch.ones_like(pos_logits)
neg_logits = outputs.neg_logits
neg_target = torch.zeros_like(neg_logits)
pos_loss = F.binary_cross_entropy_with_logits(pos_logits,
pos_target,
reduction='none')
neg_loss = F.binary_cross_entropy_with_logits(neg_logits,
neg_target,
reduction='none')
loss += (pos_loss + neg_loss).mean()
metrics.add(pos_loss=pos_loss.mean(), neg_loss=neg_loss.mean())
if epoch == -1 or epoch > self.pretrain_epoch or \
(self.use_bow is not True and self.use_dssm is not True):
loss += nll_loss
loss += kl_loss
if self.use_pg:
posterior_probs = outputs.posterior_attn.gather(
1, outputs.indexs.view(-1, 1))
reward = -perplexity(logits, target, self.weight,
self.padding_idx) * 100
pg_loss = -(reward.detach() -
self.baseline) * posterior_probs.view(-1)
pg_loss = pg_loss.mean()
loss += pg_loss
metrics.add(pg_loss=pg_loss, reward=reward.mean())
if 'attn_index' in outputs:
attn_acc = attn_accuracy(outputs.posterior_attn,
outputs.attn_index)
metrics.add(attn_acc=attn_acc)
else:
loss += nll_loss
metrics.add(loss=loss)
return metrics, scores