pair param with key when load trained model params

ppocr/utils/save_load.py

@@ -56,9 +56,25 @@ def load_model(config, model, optimizer=None):
     if checkpoints:
         if checkpoints.endswith('pdparams'):
             checkpoints = checkpoints.replace('.pdparams', '')
-        assert os.path.exists(checkpoints + ".pdopt"), \
-            f"The {checkpoints}.pdopt does not exists!"
-        load_pretrained_params(model, checkpoints)
+        assert os.path.exists(checkpoints + ".pdparams"), \
+        f"The {checkpoints}.pdparams does not exists!"
+        
+        # load params from trained model
+        params = paddle.load(checkpoints + '.pdparams')
+        state_dict = model.state_dict()
+        new_state_dict = {}
+        for key, value in state_dict.items():
+            if key not in params:
+                logger.warning(f"{key} not in loaded params {params.keys()} !")
+            pre_value = params[key]
+            if list(value.shape) == list(pre_value.shape):
+                new_state_dict[key] = pre_value
+            else:
+                logger.warning(
+                    f"The shape of model params {key} {value.shape} not matched with loaded params shape {pre_value.shape} !"
+                )
+        model.set_state_dict(new_state_dict)
+
         optim_dict = paddle.load(checkpoints + '.pdopt')
         if optimizer is not None:
             optimizer.set_state_dict(optim_dict)
@@ -92,7 +108,7 @@ def load_pretrained_params(model, path):
         if list(state_dict[k1].shape) == list(params[k2].shape):
             new_state_dict[k1] = params[k2]
         else:
-            logger.info(
+            logger.warning(
                 f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
             )
     model.set_state_dict(new_state_dict)