Upgraded to TF version 1.13.2

Removed the wait at first launch for most graphics cards.

Increased speed of training by 10-20%, but you have to retrain all models from scratch.

SAEHD:

added option 'use float16'
	Experimental option. Reduces the model size by half.
	Increases the speed of training.
	Decreases the accuracy of the model.
	The model may collapse or not train.
	Model may not learn the mask in large resolutions.

true_face_training option is replaced by
"True face power". 0.0000 .. 1.0
Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination.
Comparison - https://i.imgur.com/czScS9q.png

mainscripts/Extractor.py

@@ -47,7 +47,7 @@ class ExtractSubprocessor(Subprocessor):
             self.max_faces_from_image = client_dict['max_faces_from_image']
             self.device_idx           = client_dict['device_idx']
             self.cpu_only             = client_dict['device_type'] == 'CPU'
-            self.final_output_path    = client_dict['final_output_path']            
+            self.final_output_path    = client_dict['final_output_path']
             self.output_debug_path    = client_dict['output_debug_path']
 
             #transfer and set stdin in order to work code.interact in debug subprocess
@@ -64,9 +64,9 @@ class ExtractSubprocessor(Subprocessor):
 
             if self.type == 'all' or 'rects' in self.type or 'landmarks' in self.type:
                 nn.initialize (device_config)
-                
+
             self.log_info (f"Running on {client_dict['device_name'] }")
-                
+
             if self.type == 'all' or self.type == 'rects-s3fd' or 'landmarks' in self.type:
                 self.rects_extractor = facelib.S3FDExtractor(place_model_on_cpu=place_model_on_cpu)
 
@@ -79,8 +79,8 @@ class ExtractSubprocessor(Subprocessor):
         def process_data(self, data):
             if 'landmarks' in self.type and len(data.rects) == 0:
                 return data
-                
-            filepath = data.filepath                
+
+            filepath = data.filepath
             cached_filepath, image = self.cached_image
             if cached_filepath != filepath:
                 image = cv2_imread( filepath )
@@ -93,7 +93,7 @@ class ExtractSubprocessor(Subprocessor):
 
             h, w, c = image.shape
             extract_from_dflimg = (h == w and DFLIMG.load (filepath) is not None)
-            
+
             if 'rects' in self.type or self.type == 'all':
                 data = ExtractSubprocessor.Cli.rects_stage (data=data,
                                                             image=image,
@@ -119,7 +119,7 @@ class ExtractSubprocessor(Subprocessor):
                                                            final_output_path=self.final_output_path,
                                                            )
             return data
-            
+
         @staticmethod
         def rects_stage(data,
                         image,
@@ -157,7 +157,7 @@ class ExtractSubprocessor(Subprocessor):
                             rects_extractor,
                             ):
             h, w, ch = image.shape
-            
+
             if data.rects_rotation == 0:
                 rotated_image = image
             elif data.rects_rotation == 90:
@@ -323,7 +323,7 @@ class ExtractSubprocessor(Subprocessor):
         self.manual_window_size = manual_window_size
         self.max_faces_from_image = max_faces_from_image
         self.result = []
-        
+
         self.devices = ExtractSubprocessor.get_devices_for_config(self.type, device_config)
 
         super().__init__('Extractor', ExtractSubprocessor.Cli,
@@ -731,21 +731,21 @@ def main(detector=None,
         if detector == 'manual':
             io.log_info ('Performing manual extract...')
             data = ExtractSubprocessor ([ ExtractSubprocessor.Data(Path(filename)) for filename in input_path_image_paths ], 'landmarks-manual', image_size, face_type, output_debug_path if output_debug else None, manual_window_size=manual_window_size, device_config=device_config).run()
-        
+
             io.log_info ('Performing 3rd pass...')
             data = ExtractSubprocessor (data, 'final', image_size, face_type, output_debug_path if output_debug else None, final_output_path=output_path, device_config=device_config).run()
-        
+
         else:
             io.log_info ('Extracting faces...')
-            data = ExtractSubprocessor ([ ExtractSubprocessor.Data(Path(filename)) for filename in input_path_image_paths ], 
-                                         'all', 
-                                         image_size, 
-                                         face_type, 
-                                         output_debug_path if output_debug else None, 
-                                         max_faces_from_image=max_faces_from_image, 
+            data = ExtractSubprocessor ([ ExtractSubprocessor.Data(Path(filename)) for filename in input_path_image_paths ],
+                                         'all',
+                                         image_size,
+                                         face_type,
+                                         output_debug_path if output_debug else None,
+                                         max_faces_from_image=max_faces_from_image,
                                          final_output_path=output_path,
                                          device_config=device_config).run()
-            
+
         faces_detected += sum([d.faces_detected for d in data])
 
         if manual_fix: