bug fix for partial model initialization, if model is not initialized, it is tried to init model partially with only matching layers in size

train.py

@@ -360,17 +360,21 @@ 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:
+            print(" > Partial model initialization.")
+            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()