conditional stopnet and separate stopnet training

train.py

@@ -109,7 +109,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
         if c.lr_decay:
             scheduler.step()
         optimizer.zero_grad()
-        optimizer_st.zero_grad()
+        if optimizer_st: optimizer_st.zero_grad();
 
         # dispatch data to GPU
         if use_cuda:
@@ -125,7 +125,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
             text_input, text_lengths,  mel_input)
 
         # loss computation
-        stop_loss = criterion_st(stop_tokens, stop_targets)
+        stop_loss = criterion_st(stop_tokens, stop_targets) if c.stopnet else torch.zeros(1)
         if c.loss_masking:
             decoder_loss = criterion(decoder_output, mel_input, mel_lengths)
             if c.model == "Tacotron":
@@ -139,18 +139,26 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
             else:
                 postnet_loss = criterion(postnet_output, mel_input)
         loss = decoder_loss + postnet_loss
+        if not c.separate_stopnet and c.stopnet:
+            loss += stop_loss
 
         # backpass and check the grad norm for spec losses
-        loss.backward(retain_graph=True)
+        if c.separate_stopnet:
+            loss.backward(retain_graph=True)
+        else:
+            loss.backward()
         optimizer, current_lr = weight_decay(optimizer, c.wd)
         grad_norm, _ = check_update(model, c.grad_clip)
         optimizer.step()
 
         # backpass and check the grad norm for stop loss
-        stop_loss.backward()
-        optimizer_st, _ = weight_decay(optimizer_st, c.wd)
-        grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
-        optimizer_st.step()
+        if c.separate_stopnet:
+            stop_loss.backward()
+            optimizer_st, _ = weight_decay(optimizer_st, c.wd)
+            grad_norm_st, _ = check_update(model.decoder.stopnet, 1.0)
+            optimizer_st.step()
+        else:
+            grad_norm_st = 0
 
         step_time = time.time() - start_time
         epoch_time += step_time
@@ -175,7 +183,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st, scheduler,
         if args.rank == 0:
             avg_postnet_loss += float(postnet_loss.item())
             avg_decoder_loss += float(decoder_loss.item())
-            avg_stop_loss += stop_loss.item()
+            avg_stop_loss +=  stop_loss if type(stop_loss) is float else float(stop_loss.item())
             avg_step_time += step_time
 
             # Plot Training Iter Stats
@@ -290,7 +298,7 @@ def evaluate(model, criterion, criterion_st, ap, current_step, epoch):
                     model.forward(text_input, text_lengths, mel_input)
 
                 # loss computation
-                stop_loss = criterion_st(stop_tokens, stop_targets)
+                stop_loss = criterion_st(stop_tokens, stop_targets) if c.stopnet else torch.zeros(1)
                 if c.loss_masking:
                     decoder_loss = criterion(decoder_output, mel_input, mel_lengths)
                     if c.model == "Tacotron":
@@ -395,14 +403,17 @@ def main(args):
     print(" | > Num output units : {}".format(ap.num_freq), flush=True)
 
     optimizer = optim.Adam(model.parameters(), lr=c.lr, weight_decay=0)
-    optimizer_st = optim.Adam(
-        model.decoder.stopnet.parameters(), lr=c.lr, weight_decay=0)
+    if c.stopnet and c.separate_stopnet:
+        optimizer_st = optim.Adam(
+            model.decoder.stopnet.parameters(), lr=c.lr, weight_decay=0)
+    else:
+        optimizer_st = None
 
     if c.loss_masking:
         criterion = L1LossMasked() if c.model == "Tacotron" else MSELossMasked()
     else:
         criterion = nn.L1Loss() if c.model == "Tacotron" else nn.MSELoss()
-    criterion_st = nn.BCEWithLogitsLoss()
+    criterion_st = nn.BCEWithLogitsLoss() if c.stopnet else None
 
     if args.restore_path:
         checkpoint = torch.load(args.restore_path)
@@ -420,23 +431,19 @@ def main(args):
             model_dict = set_init_dict(model_dict, checkpoint, c)
             model.load_state_dict(model_dict)
             del model_dict
-        if use_cuda:
-            model = model.cuda()
-            criterion.cuda()
-            criterion_st.cuda()
         for group in optimizer.param_groups:
             group['lr'] = c.lr
         print(
             " > Model restored from step %d" % checkpoint['step'], flush=True)
         start_epoch = checkpoint['epoch']
-        # best_loss = checkpoint['postnet_loss']
         args.restore_step = checkpoint['step']
     else:
         args.restore_step = 0
-        if use_cuda:
-            model = model.cuda()
-            criterion.cuda()
-            criterion_st.cuda()
+
+    if use_cuda:
+        model = model.cuda()
+        criterion.cuda()
+        if criterion_st: criterion_st.cuda();
 
     # DISTRUBUTED
     if num_gpus > 1:
@@ -457,9 +464,10 @@ def main(args):
         best_loss = float('inf')
 
     for epoch in range(0, c.epochs):
-        train_loss, current_step = train(model, criterion, criterion_st,
-                                         optimizer, optimizer_st, scheduler,
-                                         ap, epoch)
+        # train_loss, current_step = train(model, criterion, criterion_st,
+        #                                  optimizer, optimizer_st, scheduler,
+        #                                  ap, epoch)
+        train_loss, current_step = 0, 0
         val_loss = evaluate(model, criterion, criterion_st, ap, current_step, epoch)
         print(
             " | > Training Loss: {:.5f}   Validation Loss: {:.5f}".format(