convert fp16 params to fp32 when params is fp16 format

ppocr/utils/save_load.py

@@ -53,6 +53,7 @@ def load_model(config, model, optimizer=None, model_type='det'):
     checkpoints = global_config.get('checkpoints')
     pretrained_model = global_config.get('pretrained_model')
     best_model_dict = {}
+    is_float16 = False
 
     if model_type == 'vqa':
         checkpoints = config['Architecture']['Backbone']['checkpoints']
@@ -90,7 +91,6 @@ def load_model(config, model, optimizer=None, model_type='det'):
         params = paddle.load(checkpoints + '.pdparams')
         state_dict = model.state_dict()
         new_state_dict = {}
-        is_float16 = False
         for key, value in state_dict.items():
             if key not in params:
                 logger.warning("{} not in loaded params {} !".format(
@@ -107,7 +107,6 @@ def load_model(config, model, optimizer=None, model_type='det'):
                     "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)
-
         if is_float16:
             logger.info(
                 "The parameter type is float16, which is converted to float32 when loading"
@@ -130,9 +129,10 @@ def load_model(config, model, optimizer=None, model_type='det'):
                 best_model_dict['start_epoch'] = states_dict['epoch'] + 1
         logger.info("resume from {}".format(checkpoints))
     elif pretrained_model:
-        load_pretrained_params(model, pretrained_model)
+        is_float16 = load_pretrained_params(model, pretrained_model)
     else:
         logger.info('train from scratch')
+    best_model_dict['is_float16'] = is_float16
     return best_model_dict
 
 
@@ -167,7 +167,7 @@ def load_pretrained_params(model, path):
             "The parameter type is float16, which is converted to float32 when loading"
         )
     logger.info("load pretrain successful from {}".format(path))
-    return model
+    return is_float16
 
 
 def save_model(model,