simplify log of loading weights (#2674)

README_en.md

@@ -188,7 +188,7 @@ The relationship between COCO mAP and FPS on Tesla V100 of representative models
 
 - `PP-YOLO` achieves mAP of 45.9% on COCO and 72.9FPS on Tesla V100. Both precision and speed surpass [YOLOv4](https://arxiv.org/abs/2004.10934)
 
-- `PP-YOLO v2` is optimized version of `PP-YOLO` which has mAP of 49.5% and 60FPS on Tesla V100
+- `PP-YOLO v2` is optimized version of `PP-YOLO` which has mAP of 49.5% and 68.9FPS on Tesla V100
 
 - All these models can be get in [Model Zoo](#ModelZoo)
 

ppdet/utils/checkpoint.py

@@ -157,28 +157,21 @@ def load_pretrain_weight(model, pretrain_weight):
 
     weights_path = path + '.pdparams'
     param_state_dict = paddle.load(weights_path)
-    lack_backbone_weights_cnt = 0
-    lack_modules = set()
-    for name, weight in model_dict.items():
-        if name in param_state_dict.keys():
-            if weight.shape != list(param_state_dict[name].shape):
+    ignore_weights = set()
+
+    for name, weight in param_state_dict.items():
+        if name in model_dict.keys():
+            if list(weight.shape) != list(model_dict[name].shape):
                 logger.info(
                     '{} not used, shape {} unmatched with {} in model.'.format(
-                        name, list(param_state_dict[name].shape), weight.shape))
-                param_state_dict.pop(name, None)
+                        name, weight.shape, list(model_dict[name].shape)))
+                ignore_weights.add(name)
         else:
-            lack_modules.add(name.split('.')[0])
-            if name.find('backbone') >= 0:
-                logger.info('Lack backbone weights: {}'.format(name))
-                lack_backbone_weights_cnt += 1
-
-    if lack_backbone_weights_cnt > 0:
-        logger.info('Lack {} weights in backbone.'.format(
-            lack_backbone_weights_cnt))
-
-    if len(lack_modules) > 0:
-        logger.info('Lack weights of modules: {}'.format(', '.join(
-            list(lack_modules))))
+            logger.info('Redundant weight {} and ignore it.'.format(name))
+            ignore_weights.add(name)
+
+    for weight in ignore_weights:
+        param_state_dict.pop(weight, None)
 
     model.set_dict(param_state_dict)
     logger.info('Finish loading model weights: {}'.format(weights_path))