Fix NameError: name 'model_dict' is not defined

Closes #91

train.py

@@ -102,7 +102,7 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st,
             mel_lengths = mel_lengths.cuda(non_blocking=True)
             linear_input = linear_input.cuda(non_blocking=True)
             stop_targets = stop_targets.cuda(non_blocking=True)
-    
+
         # compute mask for padding
         mask = sequence_mask(text_lengths)
 
@@ -400,17 +400,20 @@ def main(args):
 
     if args.restore_path:
         checkpoint = torch.load(args.restore_path)
-        model.load_state_dict(checkpoint['model'])
-        # Partial initialization: if there is a mismatch with new and old layer, it is skipped.
-        # 1. filter out unnecessary keys
-        pretrained_dict = {
-            k: v
-            for k, v in checkpoint['model'].items() if k in model_dict
-        }
-        # 2. overwrite entries in the existing state dict
-        model_dict.update(pretrained_dict)
-        # 3. load the new state dict
-        model.load_state_dict(model_dict)
+        try:
+            model.load_state_dict(checkpoint['model'])
+        except:
+            model_dict = model.state_dict()
+            # Partial initialization: if there is a mismatch with new and old layer, it is skipped.
+            # 1. filter out unnecessary keys
+            pretrained_dict = {
+                k: v
+                for k, v in checkpoint['model'].items() if k in model_dict
+            }
+            # 2. overwrite entries in the existing state dict
+            model_dict.update(pretrained_dict)
+            # 3. load the new state dict
+            model.load_state_dict(model_dict)
         if use_cuda:
             model = model.cuda()
             criterion.cuda()
@@ -418,7 +421,7 @@ def main(args):
         optimizer.load_state_dict(checkpoint['optimizer'])
         print(
             " > Model restored from step %d" % checkpoint['step'], flush=True)
-        start_epoch = checkpoint['epoch'] 
+        start_epoch = checkpoint['epoch']
         best_loss = checkpoint['linear_loss']
         args.restore_step = checkpoint['step']
     else: