remove bare except and limit the try clause

deepmd/infer/deep_eval.py

@@ -57,12 +57,13 @@ def model_version(self) -> str:
         if not self._model_version:
             try:
                 t_mt = self._get_tensor("model_attr/model_version:0")
-                sess = tf.Session(graph=self.graph, config=default_tf_session_config)
-                [mt] = run_sess(sess, [t_mt], feed_dict={})
-                self._model_version = mt.decode("utf-8")
             except KeyError:
                 # For deepmd-kit version 0.x - 1.x, set model version to 0.0
                 self._model_version = "0.0"
+            else:
+                sess = tf.Session(graph=self.graph, config=default_tf_session_config)
+                [mt] = run_sess(sess, [t_mt], feed_dict={})
+                self._model_version = mt.decode("utf-8")
         return self._model_version    
 
     def _graph_compatable(

deepmd/infer/deep_tensor.py

@@ -76,10 +76,11 @@ def __init__(
             # then put those into self.attrs
             for attr_name, tensor_name in optional_tensors.items():
                 self._get_tensor(tensor_name, attr_name)
-            self.tensors.update(optional_tensors)
-            self._support_gfv = True
         except KeyError:
             self._support_gfv = False
+        else:
+            self.tensors.update(optional_tensors)
+            self._support_gfv = True
 
         # start a tf session associated to the graph
         self.sess = tf.Session(graph=self.graph, config=default_tf_session_config)

deepmd/loss/tensor.py

@@ -11,10 +11,10 @@ class TensorLoss () :
     Loss function for tensorial properties.
     """
     def __init__ (self, jdata, **kwarg) :
-        try:
-            model = kwarg['model']
+        model = kwarg.get('model', None)
+        if model is not None:
             self.type_sel = model.get_sel_type()
-        except :
+        else:
             self.type_sel = None
         self.tensor_name = kwarg['tensor_name']
         self.tensor_size = kwarg['tensor_size']

deepmd/train/trainer.py

@@ -109,10 +109,7 @@ def _init_param(self, jdata):
             self.descrpt = DescrptHybrid(descrpt_list)
 
         # fitting net
-        try: 
-            fitting_type = fitting_param['type']
-        except:
-            fitting_type = 'ener'
+        fitting_type = fitting_param.get('type', 'ener')
         fitting_param.pop('type', None)
         fitting_param['descrpt'] = self.descrpt
         if fitting_type == 'ener':
@@ -198,10 +195,7 @@ def _init_param(self, jdata):
 
         # learning rate
         lr_param = j_must_have(jdata, 'learning_rate')
-        try: 
-            lr_type = lr_param['type']
-        except:
-            lr_type = 'exp'
+        lr_type = lr_param.get('type', 'exp')
         if lr_type == 'exp':
             self.lr = LearningRateExp(lr_param['start_lr'],
                                       lr_param['stop_lr'],
@@ -211,12 +205,8 @@ def _init_param(self, jdata):
 
         # loss
         # infer loss type by fitting_type
-        try :
-            loss_param = jdata['loss']
-            loss_type = loss_param.get('type', 'ener')
-        except:
-            loss_param = None
-            loss_type = 'ener'
+        loss_param = jdata.get('loss', None)
+        loss_type = loss_param.get('type', 'ener')
 
         if fitting_type == 'ener':
             loss_param.pop('type', None)