fix: support static graph

ppcls/optimizer/__init__.py

@@ -41,7 +41,8 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch):
     return lr
 
 
-def build_optimizer(config, epochs, step_each_epoch, model_list):
+# model_list is None in static graph
+def build_optimizer(config, epochs, step_each_epoch, model_list=None):
     config = copy.deepcopy(config)
     # step1 build lr
     lr = build_lr_scheduler(config.pop('lr'), epochs, step_each_epoch)

ppcls/optimizer/optimizer.py

@@ -43,7 +43,9 @@ class Momentum(object):
         self.multi_precision = multi_precision
 
     def __call__(self, model_list):
-        parameters = sum([m.parameters() for m in model_list], [])
+        # model_list is None in static graph
+        parameters = sum([m.parameters() for m in model_list],
+                         []) if model_list else None
         opt = optim.Momentum(
             learning_rate=self.learning_rate,
             momentum=self.momentum,
@@ -79,7 +81,9 @@ class Adam(object):
         self.multi_precision = multi_precision
 
     def __call__(self, model_list):
-        parameters = sum([m.parameters() for m in model_list], [])
+        # model_list is None in static graph
+        parameters = sum([m.parameters() for m in model_list],
+                         []) if model_list else None
         opt = optim.Adam(
             learning_rate=self.learning_rate,
             beta1=self.beta1,
@@ -123,7 +127,9 @@ class RMSProp(object):
         self.grad_clip = grad_clip
 
     def __call__(self, model_list):
-        parameters = sum([m.parameters() for m in model_list], [])
+        # model_list is None in static graph
+        parameters = sum([m.parameters() for m in model_list],
+                         []) if model_list else None
         opt = optim.RMSProp(
             learning_rate=self.learning_rate,
             momentum=self.momentum,
@@ -160,18 +166,21 @@ class AdamW(object):
         self.one_dim_param_no_weight_decay = one_dim_param_no_weight_decay
 
     def __call__(self, model_list):
-        parameters = sum([m.parameters() for m in model_list], [])
+        # model_list is None in static graph
+        parameters = sum([m.parameters() for m in model_list],
+                         []) if model_list else None
 
+        # TODO(gaotingquan): model_list is None when in static graph, "no_weight_decay" not work.
         self.no_weight_decay_param_name_list = [
             p.name for model in model_list for n, p in model.named_parameters()
             if any(nd in n for nd in self.no_weight_decay_name_list)
-        ]
+        ] if model_list else []
 
         if self.one_dim_param_no_weight_decay:
             self.no_weight_decay_param_name_list += [
                 p.name for model in model_list
                 for n, p in model.named_parameters() if len(p.shape) == 1
-            ]
+            ] if model_list else []
 
         opt = optim.AdamW(
             learning_rate=self.learning_rate,