Update difpoint/inference.py

difpoint/inference.py

@@ -193,20 +193,14 @@ class Inferencer(object):
 
     def _norm(self, data_dict):
         for k in data_dict.keys():
-            if k in ['yaw', 'pitch', 'roll', 't', 'scale', 'c_lip', 'c_eye']:
+            if k in ['yaw', 'pitch', 'roll', 't', 'exp', 'scale', 'kp', 'c_lip', 'c_eye']:
                 v=data_dict[k]
                 data_dict[k] = (v - self.norm_info[k+'_mean'])/self.norm_info[k+'_std']
-            elif k in ['exp', 'kp']:
-                v=data_dict[k]
-                data_dict[k] = (v - self.norm_info[k+'_mean'].reshape(1,21,3))/self.norm_info[k+'_std'].reshape(1,21,3)
         return data_dict
 
     def _denorm(self, data_dict):
         for k in data_dict.keys():
-            if k in ['yaw', 'pitch', 'roll', 't', 'scale', 'c_lip', 'c_eye']:
-                v=data_dict[k]
-                data_dict[k] = v * self.norm_info[k+'_std'] + self.norm_info[k+'_mean']
-            elif k in ['exp', 'kp']:
+            if k in ['yaw', 'pitch', 'roll', 't', 'exp', 'scale', 'kp', 'c_lip', 'c_eye']:
                 v=data_dict[k]
                 data_dict[k] = v * self.norm_info[k+'_std'] + self.norm_info[k+'_mean']
         return data_dict