#dep_loss_masked = torch.where(deps>0, dep_loss, zero) # This zeros out all positions where deps == 0
#dep_loss_tot = dep_loss_masked.sum() / batch_size
dep_loss_tot = dep_loss.masked_fill_( deps==0, 0. ).sum()
factor_hints="Factor hints (class_loss=%8.4f, deps_loss=%10.4f, fac=%.8f)" % (
class_loss_tot.item()/batch_size*100.,
dep_loss_tot.item()/batch_size*100.,
class_loss_tot.item()/dep_loss_tot.item(), )
#factor hints : (231.14927673339844, 225.23297119140625, 1.0262674932124587)
batch_loss = class_loss_tot + args.dep_fac * dep_loss_tot
batch_loss.backward()
model_opt.step()
loss_this = batch_loss.item()
loss_recent_tot += loss_this
if idx % 10 == 0:
print('%.1f%% of epoch %d' % (idx / float(len(train_loader)) * 100, epoch,), end='\r') # Python 3 FTW!
if idx % 100 == 0:
print(epoch, idx, factor_hints)
sentences_since_last_check = (idx-idx_loss_check)*batch_size
#if sentences_since_last_check > 50000: # Potentially save every 50000 sentences (~30mins on TitanX)
if sentences_since_last_check > 200000: # Potentially save every 200000 sentences (~2hrs on TitanX)
loss_recent = loss_recent_tot / float(sentences_since_last_check) # loss per sentence
batch = [x_ids, x_perm_ids, s_ids, s_perm_ids, y_a, y_b]
if args.feature_mix:
ce_loss_x, ce_loss_s, scl_loss = model.forward_feature_mix(batch)
else:
ce_loss_x, ce_loss_s, scl_loss = model(batch)
ce_loss = (ce_loss_x + ce_loss_s) / 2
if not args.with_summary:
ce_loss = ce_loss_x
loss = args.lambd * ce_loss + (1 - args.lambd) * scl_loss
# print(ce_loss_x, ce_loss_s, scl_loss)
loss.backward()
count += 1
if (count % args.num_accum == 0):
optimizer.step()
recoder.log_train(ce_loss_x, ce_loss_s, scl_loss, loss)
step += 1
optimizer.zero_grad()
if (step >= args.steps):
break
if (step % args.log_step == 0):
begin_eval = True
if (step % 10 == 0):
bar.update(10)
# step += 1
