Merge branch 'PaddlePaddle:release/2.4' into release/2.4

doc/doc_ch/detection.md

@@ -78,11 +78,11 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
 cd PaddleOCR/
 # 根据backbone的不同选择下载对应的预训练模型
 # 下载MobileNetV3的预训练模型
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams
 # 或，下载ResNet18_vd的预训练模型
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet18_vd_pretrained.pdparams
 # 或，下载ResNet50_vd的预训练模型
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams
 ```
 
 <a name="2-----"></a>

doc/doc_en/detection_en.md

@@ -67,11 +67,11 @@ And the responding download link of backbone pretrain weights can be found in (h
 ```shell
 cd PaddleOCR/
 # Download the pre-trained model of MobileNetV3
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams
 # or, download the pre-trained model of ResNet18_vd
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet18_vd_pretrained.pdparams
 # or, download the pre-trained model of ResNet50_vd
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams
 
 ```
 

ppocr/utils/save_load.py

@@ -111,13 +111,16 @@ def load_pretrained_params(model, path):
     params = paddle.load(path + '.pdparams')
     state_dict = model.state_dict()
     new_state_dict = {}
-    for k1, k2 in zip(state_dict.keys(), params.keys()):
-        if list(state_dict[k1].shape) == list(params[k2].shape):
-            new_state_dict[k1] = params[k2]
+    for k1 in params.keys():
+        if k1 not in state_dict.keys():
+            logger.warning("The pretrained params {} not in model".format(k1))
         else:
-            logger.warning(
-                "The shape of model params {} {} not matched with loaded params {} {} !".
-                format(k1, state_dict[k1].shape, k2, params[k2].shape))
+            if list(state_dict[k1].shape) == list(params[k1].shape):
+                new_state_dict[k1] = params[k1]
+            else:
+                logger.warning(
+                    "The shape of model params {} {} not matched with loaded params {} {} !".
+                    format(k1, state_dict[k1].shape, k1, params[k1].shape))
     model.set_state_dict(new_state_dict)
     logger.info("load pretrain successful from {}".format(path))
     return model