SAEHD: added option Enable random warp of samples, default is on

Random warp is required to generalize facial expressions of both faces. When the face is trained enough, you can disable it to get extra sharpness for less amount of iterations.

models/ModelBase.py

@@ -226,6 +226,12 @@ class ModelBase(object):
                     io.destroy_window(wnd_name)
                 else:
                     self.sample_for_preview = self.generate_next_sample()
+                    
+            try:
+                self.get_static_preview()
+            except:
+                self.sample_for_preview = self.generate_next_sample()
+                
             self.last_sample = self.sample_for_preview
             
         ###Generate text summary of model hyperparameters

models/Model_SAEHD/Model.py

@@ -63,6 +63,9 @@ class SAEv2Model(ModelBase):
         default_face_style_power = 0.0
         default_bg_style_power = 0.0
         if is_first_run or ask_override:
+            default_random_warp = self.options.get('random_warp', True)
+            self.options['random_warp'] = io.input_str (f"Enable random warp of samples? ( y/n, ?:help skip:{yn_str[default_random_warp]}) : ", default_random_warp, help_message="Random warp is required to generalize facial expressions of both faces. When the face is trained enough, you can disable it to get extra sharpness for less amount of iterations.")
+
             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 ( io.input_number("Face style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_face_style_power), default_face_style_power,
                                                                                help_message="Learn to transfer face style details such as light and color conditions. Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style. Start from 0.1 value and check history changes. Enabling this option increases the chance of model collapse."), 0.0, 100.0 )
@@ -84,6 +87,7 @@ class SAEv2Model(ModelBase):
                 self.options['clipgrad'] = False
 
         else:
+            self.options['random_warp'] = self.options.get('random_warp', True)
             self.options['face_style_power'] = self.options.get('face_style_power', default_face_style_power)
             self.options['bg_style_power'] = self.options.get('bg_style_power', default_bg_style_power)
             self.options['apply_random_ct'] = self.options.get('apply_random_ct', False)
@@ -529,19 +533,21 @@ class SAEv2Model(ModelBase):
                 training_data_dst_path = self.pretraining_data_path
                 sort_by_yaw = False
                 
+            t_img_warped = t.IMG_WARPED_TRANSFORMED if self.options['random_warp'] else t.IMG_TRANSFORMED 
+
             self.set_training_data_generators ([
                     SampleGeneratorFace(training_data_src_path, sort_by_yaw_target_samples_path=training_data_dst_path if sort_by_yaw else None,
                                                                 random_ct_samples_path=training_data_dst_path if apply_random_ct else None,
                                                                 debug=self.is_debug(), batch_size=self.batch_size,
                         sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
-                        output_sample_types = [ {'types' : (t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr), 'resolution':resolution, 'apply_ct': apply_random_ct},
+                        output_sample_types = [ {'types' : (t_img_warped, face_type, t_mode_bgr), 'resolution':resolution, 'apply_ct': apply_random_ct},
                                                 {'types' : (t.IMG_TRANSFORMED, face_type, t_mode_bgr), 'resolution': resolution, 'apply_ct': apply_random_ct },
                                                 {'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'resolution': resolution } ]
                                               ),
 
                     SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
                         sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
-                        output_sample_types = [ {'types' : (t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr), 'resolution':resolution},
+                        output_sample_types = [ {'types' : (t_img_warped, face_type, t_mode_bgr), 'resolution':resolution},
                                                 {'types' : (t.IMG_TRANSFORMED, face_type, t_mode_bgr), 'resolution': resolution},
                                                 {'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'resolution': resolution} ])
                              ])

nnlib/nnlib.py

@@ -631,26 +631,8 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
 
                 reduction_axes = list(range(len(input_shape)))
                 del reduction_axes[self.axis]
-
-                #broadcast_shape = [1] * len(input_shape)
-                #broadcast_shape[self.axis] = input_shape[self.axis]
-                #normed = x# (x - K.reshape(self.moving_mean,broadcast_shape) ) / ( K.sqrt( K.reshape(self.moving_variance,broadcast_shape)) +self.epsilon)
-                #normed *= K.reshape(gamma,[-1]+broadcast_shape[1:] )
-                #normed += K.reshape(beta, [-1]+broadcast_shape[1:] )
-                #mean = K.mean(x, axis=reduction_axes)
-                #self.moving_mean = self.add_weight(shape=(units,), name='moving_mean', initializer='zeros',trainable=False)
-                #self.moving_variance = self.add_weight(shape=(units,), name='moving_variance',initializer='ones', trainable=False)
-
-                #variance = K.var(x, axis=reduction_axes)
-                #sample_size = K.prod([ K.shape(x)[axis] for axis in reduction_axes ])
-                #sample_size = K.cast(sample_size, dtype=K.dtype(x))
-                #variance *= sample_size / (sample_size - (1.0 + self.epsilon))
-
-                #self.add_update([K.moving_average_update(self.moving_mean, mean, self.momentum),
-                #                 K.moving_average_update(self.moving_variance, variance, self.momentum)], None)
-                #return normed
-
                 del reduction_axes[0]
+                
                 broadcast_shape = [1] * len(input_shape)
                 broadcast_shape[self.axis] = input_shape[self.axis]
                 mean = K.mean(x, reduction_axes, keepdims=True)

samplelib/SampleProcessor.py

@@ -72,6 +72,7 @@ class SampleProcessor(object):
         MODE_GGG                   = 42  #3xGrayscale
         MODE_M                     = 43  #mask only
         MODE_BGR_SHUFFLE           = 44  #BGR shuffle
+        MODE_BGR_RANDOM_HUE_SHIFT  = 45
         MODE_END = 50
 
     class Options(object):
@@ -257,6 +258,15 @@ class SampleProcessor(object):
                 elif mode_type == SPTF.MODE_BGR_SHUFFLE:
                     rnd_state = np.random.RandomState (sample_rnd_seed)
                     img = np.take (img_bgr, rnd_state.permutation(img_bgr.shape[-1]), axis=-1)
+                elif mode_type == SPTF.MODE_BGR_RANDOM_HUE_SHIFT:
+                    rnd_state = np.random.RandomState (sample_rnd_seed)
+                    hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)
+                    h, s, v = cv2.split(hsv)
+                    
+                    h = (h + rnd_state.randint(360) ) % 360
+                    hsv = cv2.merge([h, s, v])
+                    
+                    img = np.clip( cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) , 0, 1 )
                 elif mode_type == SPTF.MODE_G:
                     img = np.concatenate ( (np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1),img_mask) , -1 )
                 elif mode_type == SPTF.MODE_GGG: