Medix-AI/coqui-tts
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coqui-tts
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train.py - replace data[0] with item()

This commit is contained in:
Eren Golge 2018-05-10 16:22:17 -07:00
parent 6bea3f1f93
commit 3970491451
1 changed files with 15 additions and 17 deletions
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32
train.py
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@ -118,19 +118,18 @@ def train(model, criterion, data_loader, optimizer, epoch):
epoch_time += step_time epoch_time += step_time
# update # update
progbar.update(num_iter+1, values=[('total_loss', loss.data[0]), progbar.update(num_iter+1, values=[('total_loss', loss.item()),
('linear_loss', ('linear_loss', linear_loss.item()),
linear_loss.data[0]), ('mel_loss', mel_loss.item()),
('mel_loss', mel_loss.data[0]), ('grad_norm', grad_norm.item())])
('grad_norm', grad_norm)]) avg_linear_loss += linear_loss.item()
avg_linear_loss += linear_loss.data[0] avg_mel_loss += mel_loss.item()
avg_mel_loss += mel_loss.data[0]
# Plot Training Iter Stats # Plot Training Iter Stats
tb.add_scalar('TrainIterLoss/TotalLoss', loss.data[0], current_step) tb.add_scalar('TrainIterLoss/TotalLoss', loss.item(), current_step)
tb.add_scalar('TrainIterLoss/LinearLoss', linear_loss.data[0], tb.add_scalar('TrainIterLoss/LinearLoss', linear_loss.item(),
current_step) current_step)
tb.add_scalar('TrainIterLoss/MelLoss', mel_loss.data[0], current_step) tb.add_scalar('TrainIterLoss/MelLoss', mel_loss.item(), current_step)
tb.add_scalar('Params/LearningRate', optimizer.param_groups[0]['lr'], tb.add_scalar('Params/LearningRate', optimizer.param_groups[0]['lr'],
current_step) current_step)
tb.add_scalar('Params/GradNorm', grad_norm, current_step) tb.add_scalar('Params/GradNorm', grad_norm, current_step)
@ -139,7 +138,7 @@ def train(model, criterion, data_loader, optimizer, epoch):
if current_step % c.save_step == 0: if current_step % c.save_step == 0:
if c.checkpoint: if c.checkpoint:
# save model # save model
save_checkpoint(model, optimizer, linear_loss.data[0], save_checkpoint(model, optimizer, linear_loss.item(),
OUT_PATH, current_step, epoch) OUT_PATH, current_step, epoch)
# Diagnostic visualizations # Diagnostic visualizations
@ -225,13 +224,12 @@ def evaluate(model, criterion, data_loader, current_step):
epoch_time += step_time epoch_time += step_time
# update # update
progbar.update(num_iter+1, values=[('total_loss', loss.data[0]), progbar.update(num_iter+1, values=[('total_loss', loss.item()),
('linear_loss', ('linear_loss', linear_loss.item()),
linear_loss.data[0]), ('mel_loss', mel_loss.item())])
('mel_loss', mel_loss.data[0])])
avg_linear_loss += linear_loss.data[0] avg_linear_loss += linear_loss.item()
avg_mel_loss += mel_loss.data[0] avg_mel_loss += mel_loss.item()
# Diagnostic visualizations # Diagnostic visualizations
idx = np.random.randint(mel_input.shape[0]) idx = np.random.randint(mel_input.shape[0])