From 5bf3ff345f9e45a1855b4da3b7dbb0961ed7b4ce Mon Sep 17 00:00:00 2001 From: Eren G Date: Thu, 12 Jul 2018 18:09:02 +0200 Subject: [PATCH] Remove tabs from logging --- train.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/train.py b/train.py index f7d50f3c..837b5f24 100644 --- a/train.py +++ b/train.py @@ -150,9 +150,9 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st, # ('grad_norm_st', grad_norm_st.item())]) if current_step % c.print_step == 0: - print(" | | > Step:{}\tGlobalStep:{}\tTotalLoss:{:.5f}\tLinearLoss:{:.5f}\tMelLoss:\ - {:.5f}\tStopLoss:{:.5f}\tGradNorm:{:.5f}\t\ - GradNormST:{:.5f}\tStepTime:{:.2f}".format(num_iter, current_step, + print(" | | > Step:{} GlobalStep:{} TotalLoss:{:.5f} LinearLoss:{:.5f} MelLoss:\ + {:.5f} StopLoss:{:.5f} GradNorm:{:.5f} \ + GradNormST:{:.5f} StepTime:{:.2f}".format(num_iter, current_step, loss.item(), linear_loss.item(), mel_loss.item(), @@ -215,9 +215,9 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st, avg_total_loss = avg_mel_loss + avg_linear_loss + avg_stop_loss # print epoch stats - print(" | | > EPOCH END -- GlobalStep:{}\tAvgTotalLoss:{:.5f}\t\ - AvgLinearLoss:{:.5f}\tAvgMelLoss:{:.5f}\t\ - AvgStopLoss:{:.5f}\tEpochTime:{:.2f}".format(current_step, + print(" | | > EPOCH END -- GlobalStep:{} AvgTotalLoss:{:.5f} \ + AvgLinearLoss:{:.5f} AvgMelLoss:{:.5f} \ + AvgStopLoss:{:.5f} EpochTime:{:.2f}".format(current_step, avg_total_loss, avg_linear_loss, avg_mel_loss, @@ -290,8 +290,8 @@ def evaluate(model, criterion, criterion_st, data_loader, current_step): # ('mel_loss', mel_loss.item()), # ('stop_loss', stop_loss.item())]) if current_step % c.print_step == 0: - print(" | | > TotalLoss: {:.5f}\t LinearLoss: {:.5f}\t MelLoss: \ - {:.5f}\t StopLoss: {:.5f}\t".format(loss.item(), + print(" | | > TotalLoss: {:.5f} LinearLoss: {:.5f} MelLoss: \ + {:.5f} StopLoss: {:.5f} ".format(loss.item(), linear_loss.item(), mel_loss.item(), stop_loss.item())) @@ -434,7 +434,7 @@ def main(args): train_loss, current_step = train( model, criterion, criterion_st, train_loader, optimizer, optimizer_st, epoch) val_loss = evaluate(model, criterion, criterion_st, val_loader, current_step) - print(" | > Train Loss: {:.5f}\t Validation Loss: {:.5f}".format(train_loss, val_loss)) + print(" | > Train Loss: {:.5f} Validation Loss: {:.5f}".format(train_loss, val_loss)) best_loss = save_best_model(model, optimizer, val_loss, best_loss, OUT_PATH, current_step, epoch)