train_loss_func only used in train mode

ppcls/engine/engine.py

@@ -214,17 +214,17 @@ class Engine(object):
         if self.config["Global"]["pretrained_model"] is not None:
             if self.config["Global"]["pretrained_model"].startswith("http"):
                 load_dygraph_pretrain_from_url(
-                    [self.model, self.train_loss_func],
+                    [self.model, getattr(self, 'train_loss_func', None)],
                     self.config["Global"]["pretrained_model"])
             else:
                 load_dygraph_pretrain(
-                    [self.model, self.train_loss_func],
+                    [self.model, getattr(self, 'train_loss_func', None)],
                     self.config["Global"]["pretrained_model"])
 
         # build optimizer
         if self.mode == 'train':
             self.optimizer, self.lr_sch = build_optimizer(
-                self.config["Optimizer"], self.config["Global"]["epochs"],
+                self.config, self.config["Global"]["epochs"],
                 len(self.train_dataloader),
                 [self.model, self.train_loss_func])
 
@@ -259,7 +259,8 @@ class Engine(object):
         if self.config["Global"]["distributed"]:
             dist.init_parallel_env()
             self.model = paddle.DataParallel(self.model)
-            if len(self.train_loss_func.parameters()) > 0:
+            if self.mode == 'train' and len(self.train_loss_func.parameters(
+            )) > 0:
                 self.train_loss_func = paddle.DataParallel(
                     self.train_loss_func)
         # build postprocess for infer

ppcls/optimizer/__init__.py

@@ -45,19 +45,20 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch):
 # model_list is None in static graph
 def build_optimizer(config, epochs, step_each_epoch, model_list=None):
     config = copy.deepcopy(config)
-    if isinstance(config, dict):
-        # convert to [{optim_name1: {scope: xxx, **optim_cfg}}, {optim_name2: {scope: xxx, **optim_cfg}}, ...]
-        optim_name = config.Optimizer.pop('name')
-        config: List[Dict[str, Dict]] = [{
+    optim_config = config["Optimizer"]
+    if isinstance(optim_config, dict):
+        # convert {'name': xxx, **optim_cfg} to [{name: {scope: xxx, **optim_cfg}}]
+        optim_name = optim_config.pop("name")
+        optim_config: List[Dict[str, Dict]] = [{
             optim_name: {
-                'scope': config.Arch.name,
+                'scope': config["Arch"].get("name"),
                 **
-                config.Optimizer
+                optim_config
             }
         }]
     optim_list = []
     lr_list = []
-    for optim_item in config:
+    for optim_item in optim_config:
         # optim_cfg = {optim_name1: {scope: xxx, **optim_cfg}}
         # step1 build lr
         optim_name = optim_item.keys()[0]  # get optim_name1

ppcls/utils/save_load.py

@@ -49,7 +49,8 @@ def load_dygraph_pretrain(model, path=None):
     param_state_dict = paddle.load(path + ".pdparams")
     if isinstance(model, list):
         for m in model:
-            m.set_dict(param_state_dict)
+            if hasattr(m, 'set_dict'):
+                m.set_dict(param_state_dict)
     else:
         model.set_dict(param_state_dict)
     return