pair param with key when load trained model params

ppocr/utils/save_load.py

@@ -57,22 +57,23 @@ def load_model(config, model, optimizer=None):
         if checkpoints.endswith('pdparams'):
             checkpoints = checkpoints.replace('.pdparams', '')
         assert os.path.exists(checkpoints + ".pdparams"), \
-        f"The {checkpoints}.pdparams does not exists!"
-        
+            "The {}.pdparams does not exists!".format(checkpoints)
+
         # 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()} !")
+                logger.warning("{} not in loaded params {} !".format(
+                    key, 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} !"
-                )
+                    "The shape of model params {} {} not matched with loaded params shape {} !".
+                    format(key, value.shape, pre_value.shape))
         model.set_state_dict(new_state_dict)
 
         optim_dict = paddle.load(checkpoints + '.pdopt')
@@ -99,7 +100,7 @@ def load_pretrained_params(model, path):
     if path.endswith('pdparams'):
         path = path.replace('.pdparams', '')
     assert os.path.exists(path + ".pdparams"), \
-        f"The {path}.pdparams does not exists!"
+        "The {}.pdparams does not exists!".format(path)
 
     params = paddle.load(path + '.pdparams')
     state_dict = model.state_dict()
@@ -109,10 +110,10 @@ def load_pretrained_params(model, path):
             new_state_dict[k1] = params[k2]
         else:
             logger.warning(
-                f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
-            )
+                "The shape of model params {} {} not matched with loaded params {} {} !".
+                format(k1, state_dict[k1].shape, k2, params[k2].shape))
     model.set_state_dict(new_state_dict)
-    logger.info(f"load pretrain successful from {path}")
+    logger.info("load pretrain successful from {}".format(path))
     return model