partial initialization fix

train.py

@@ -367,11 +367,11 @@ def main(args):
     criterion = L1LossMasked()
     criterion_st = nn.BCELoss()
 
-    partial_init_flag = False
     if args.restore_path:
         checkpoint = torch.load(args.restore_path)
         try:
             model.load_state_dict(checkpoint['model'])
+            optimizer.load_state_dict(checkpoint['optimizer'])
         except:
             print(" > Partial model initialization.")
             partial_init_flag = True
@@ -385,7 +385,7 @@ def main(args):
             # 2. filter out different size layers
             pretrained_dict = {
                 k: v
-                for k, v in checkpoint['model'].items() if v.numel() == model_dict[k].numel()
+                for k, v in pretrained_dict.items() if v.numel() == model_dict[k].numel()
             }
             # 3. overwrite entries in the existing state dict
             model_dict.update(pretrained_dict)
@@ -396,8 +396,6 @@ def main(args):
             model = model.cuda()
             criterion.cuda()
             criterion_st.cuda()
-        if not partial_init_flag:
-            optimizer.load_state_dict(checkpoint['optimizer'])
         for group in optimizer.param_groups:
                 group['lr'] = c.lr
         print(