SAE: changed default style power to 10.0 . Now style power is floating number in valid range 0.0 to 100.0

models/Model_SAE/Model.py

@@ -35,17 +35,19 @@ class SAEModel(ModelBase):
             self.options['archi'] = self.options.get('archi', default_archi)
             self.options['lighter_encoder'] = self.options.get('lighter_encoder', False)
 
+        default_face_style_power = 10.0
         if is_first_run or ask_override:
-            default_style_power = 100 if is_first_run else self.options.get('face_style_power', 100)
+            default_face_style_power = default_face_style_power if is_first_run else self.options.get('face_style_power', default_face_style_power)
-            self.options['face_style_power'] = np.clip ( input_int("Face style power (0..100 ?:help skip:%d) : " % (default_style_power), default_style_power, help_message="How fast NN will learn dst face style during generalization of src and dst faces."), 0, 100 )            
+            self.options['face_style_power'] = np.clip ( input_number("Face style power ( 0.0 .. 100.0 ?:help skip:%.1f) : " % (default_face_style_power), default_face_style_power, help_message="How fast NN will learn dst face style during generalization of src and dst faces."), 0.0, 100.0 )            
         else:
-            self.options['face_style_power'] = self.options.get('face_style_power', 100)
+            self.options['face_style_power'] = self.options.get('face_style_power', default_face_style_power)
         
+        default_bg_style_power = 10.0        
         if is_first_run or ask_override: 
-            default_style_power = 100 if is_first_run else self.options.get('bg_style_power', 100)
+            default_bg_style_power = default_bg_style_power if is_first_run else self.options.get('bg_style_power', default_bg_style_power)
-            self.options['bg_style_power'] = np.clip ( input_int("Background style power (0..100 ?:help skip:%d) : " % (default_style_power), default_style_power, help_message="How fast NN will learn dst background style during generalization of src and dst faces."), 0, 100 )            
+            self.options['bg_style_power'] = np.clip ( input_number("Background style power ( 0.0 .. 100.0 ?:help skip:%.1f) : " % (default_bg_style_power), default_bg_style_power, help_message="How fast NN will learn dst background style during generalization of src and dst faces."), 0.0, 100.0 )            
         else:
-            self.options['bg_style_power'] = self.options.get('bg_style_power', 100)
+            self.options['bg_style_power'] = self.options.get('bg_style_power', default_bg_style_power)
             
         default_ae_dims = 256 if self.options['archi'] == 'liae' else 512
         default_ed_ch_dims = 42
@@ -187,6 +189,9 @@ class SAEModel(ModelBase):
   
         src_loss = K.mean( 100*K.square(tf_dssim(2.0)( target_src_masked, pred_src_src_masked )) ) 
         
+        def optimizer():
+            return Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
+
         if self.options['face_style_power'] != 0:
             face_style_power = self.options['face_style_power'] / 100.0
             src_loss += tf_style_loss(gaussian_blur_radius=resolution // 8, loss_weight=0.2*face_style_power)(psd_target_dst_masked, target_dst_masked) 
@@ -200,7 +205,7 @@ class SAEModel(ModelBase):
         else:
             src_train_weights = self.encoder.trainable_weights + self.decoder_src.trainable_weights
         self.src_train = K.function ([warped_src, target_src, target_srcm, warped_dst, target_dst, target_dstm ],[src_loss],
-                                    Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src_loss, src_train_weights) )
+                                    optimizer().get_updates(src_loss, src_train_weights) )
 
         dst_loss = K.mean( 100*K.square(tf_dssim(2.0)( target_dst_masked, pred_dst_dst_masked )) )        
         
@@ -209,7 +214,7 @@ class SAEModel(ModelBase):
         else:
             dst_train_weights = self.encoder.trainable_weights + self.decoder_dst.trainable_weights
         self.dst_train = K.function ([warped_dst, target_dst, target_dstm],[dst_loss],
-                                    Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(dst_loss, dst_train_weights) )
+                                    optimizer().get_updates(dst_loss, dst_train_weights) )
   
         src_mask_loss = K.mean(K.square(target_srcm-pred_src_srcm))    
 
@@ -219,7 +224,7 @@ class SAEModel(ModelBase):
             src_mask_train_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights
             
         self.src_mask_train = K.function ([warped_src, target_srcm],[src_mask_loss],
-                                    Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src_mask_loss, src_mask_train_weights ) )
+                                    optimizer().get_updates(src_mask_loss, src_mask_train_weights ) )
         
         dst_mask_loss = K.mean(K.square(target_dstm-pred_dst_dstm))   
 
@@ -229,7 +234,7 @@ class SAEModel(ModelBase):
             dst_mask_train_weights = self.encoder.trainable_weights + self.decoder_dstm.trainable_weights
             
         self.dst_mask_train = K.function ([warped_dst, target_dstm],[dst_mask_loss],
-                                    Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(dst_mask_loss, dst_mask_train_weights) )
+                                    optimizer().get_updates(dst_mask_loss, dst_mask_train_weights) )
                                     
         self.AE_view = K.function ([warped_src, warped_dst],[pred_src_src, pred_src_srcm, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm])
         self.AE_convert = K.function ([warped_dst],[pred_src_dst, pred_src_dstm])

nnlib/nnlib.py

@@ -109,6 +109,7 @@ AddUniformNoise = nnlib.AddUniformNoise
 keras_contrib = nnlib.keras_contrib
 GroupNormalization = keras_contrib.layers.GroupNormalization
 InstanceNormalization = keras_contrib.layers.InstanceNormalization
+Padam = keras_contrib.optimizers.Padam
 """
     code_import_dlib_string = \
 """

utils/console_utils.py

@@ -3,6 +3,25 @@ import sys
 import time
 import multiprocessing
 
+def input_number(s, default_value, valid_list=None, help_message=None):
+    while True:
+        try:
+            inp = input(s)
+            if len(inp) == 0:
+                raise ValueError("")
+                
+            if help_message is not None and inp == '?':
+                print (help_message)
+                continue
+            
+            i = float(inp)
+            if (valid_list is not None) and (i not in valid_list):
+                return default_value
+            return i
+        except:
+            print (default_value)
+            return default_value
+            
 def input_int(s, default_value, valid_list=None, help_message=None):
     while True:
         try: