From c8bfe731d6395158b8799f89150b77ebd95b14c5 Mon Sep 17 00:00:00 2001 From: Eren Golge Date: Thu, 10 May 2018 16:22:17 -0700 Subject: [PATCH] train.py - replace data[0] with item() --- train.py | 32 +++++++++++++++----------------- 1 file changed, 15 insertions(+), 17 deletions(-) diff --git a/train.py b/train.py index 4758699..233c8f6 100644 --- a/train.py +++ b/train.py @@ -118,19 +118,18 @@ def train(model, criterion, data_loader, optimizer, epoch): epoch_time += step_time # update - progbar.update(num_iter+1, values=[('total_loss', loss.data[0]), - ('linear_loss', - linear_loss.data[0]), - ('mel_loss', mel_loss.data[0]), - ('grad_norm', grad_norm)]) - avg_linear_loss += linear_loss.data[0] - avg_mel_loss += mel_loss.data[0] + progbar.update(num_iter+1, values=[('total_loss', loss.item()), + ('linear_loss', linear_loss.item()), + ('mel_loss', mel_loss.item()), + ('grad_norm', grad_norm.item())]) + avg_linear_loss += linear_loss.item() + avg_mel_loss += mel_loss.item() # Plot Training Iter Stats - tb.add_scalar('TrainIterLoss/TotalLoss', loss.data[0], current_step) - tb.add_scalar('TrainIterLoss/LinearLoss', linear_loss.data[0], + tb.add_scalar('TrainIterLoss/TotalLoss', loss.item(), current_step) + tb.add_scalar('TrainIterLoss/LinearLoss', linear_loss.item(), 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'], 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 c.checkpoint: # save model - save_checkpoint(model, optimizer, linear_loss.data[0], + save_checkpoint(model, optimizer, linear_loss.item(), OUT_PATH, current_step, epoch) # Diagnostic visualizations @@ -225,13 +224,12 @@ def evaluate(model, criterion, data_loader, current_step): epoch_time += step_time # update - progbar.update(num_iter+1, values=[('total_loss', loss.data[0]), - ('linear_loss', - linear_loss.data[0]), - ('mel_loss', mel_loss.data[0])]) + progbar.update(num_iter+1, values=[('total_loss', loss.item()), + ('linear_loss', linear_loss.item()), + ('mel_loss', mel_loss.item())]) - avg_linear_loss += linear_loss.data[0] - avg_mel_loss += mel_loss.data[0] + avg_linear_loss += linear_loss.item() + avg_mel_loss += mel_loss.item() # Diagnostic visualizations idx = np.random.randint(mel_input.shape[0])