partial model initialization

train.py

@@ -401,6 +401,16 @@ 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)
         if use_cuda:
             model = model.cuda()
             criterion.cuda()