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Colombo 2019-12-20 11:04:26 +04:00
parent cf27558e88
commit 951942821d
1 changed files with 7 additions and 11 deletions
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18
models/Model_SAEHD/Model.py
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@ -64,10 +64,7 @@ class SAEHDModel(ModelBase):
default_face_style_power = self.options.get('face_style_power', 0.0) default_face_style_power = self.options.get('face_style_power', 0.0)
default_bg_style_power = self.options.get('bg_style_power', 0.0) default_bg_style_power = self.options.get('bg_style_power', 0.0)
if is_first_run or ask_override: if is_first_run or ask_override:
default_lr_dropout = self.options.get('lr_dropout', False)
self.options['lr_dropout'] = io.input_bool ( f"Use learning rate dropout? (y/n, ?:help skip:{yn_str[default_lr_dropout]} ) : ", default_lr_dropout, help_message="When the face is trained enough, you can enable this option to get extra sharpness for less amount of iterations.")
default_random_warp = self.options.get('random_warp', True) default_random_warp = self.options.get('random_warp', True)
self.options['random_warp'] = io.input_bool (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.") self.options['random_warp'] = io.input_bool (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.")
@ -87,8 +84,8 @@ class SAEHDModel(ModelBase):
self.options['clipgrad'] = io.input_bool (f"Enable gradient clipping? (y/n, ?:help skip:{yn_str[default_clipgrad]}) : ", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.") self.options['clipgrad'] = io.input_bool (f"Enable gradient clipping? (y/n, ?:help skip:{yn_str[default_clipgrad]}) : ", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
else: else:
self.options['clipgrad'] = False self.options['clipgrad'] = False
else: else:
self.options['lr_dropout'] = self.options.get('lr_dropout', default_lr_dropout)
self.options['random_warp'] = self.options.get('random_warp', True) self.options['random_warp'] = self.options.get('random_warp', True)
self.options['true_face_training'] = self.options.get('true_face_training', default_true_face_training) self.options['true_face_training'] = self.options.get('true_face_training', default_true_face_training)
self.options['face_style_power'] = self.options.get('face_style_power', default_face_style_power) self.options['face_style_power'] = self.options.get('face_style_power', default_face_style_power)
@ -455,10 +452,9 @@ class SAEHDModel(ModelBase):
psd_target_dst_anti_masked = self.model.pred_src_dst*(1.0 - target_dstm) psd_target_dst_anti_masked = self.model.pred_src_dst*(1.0 - target_dstm)
if self.is_training_mode: if self.is_training_mode:
lr_dropout = 0.3 if self.options['lr_dropout'] else 0.0 self.src_dst_opt = RMSprop(lr=5e-5, lr_dropout=0.3, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
self.src_dst_opt = RMSprop(lr=5e-5, lr_dropout=lr_dropout, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1) self.src_dst_mask_opt = RMSprop(lr=5e-5, lr_dropout=0.3, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
self.src_dst_mask_opt = RMSprop(lr=5e-5, lr_dropout=lr_dropout, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1) self.D_opt = RMSprop(lr=5e-5, lr_dropout=0.3, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
self.D_opt = RMSprop(lr=5e-5, lr_dropout=lr_dropout, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
src_loss = K.mean ( 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_opt, pred_src_src_masked_opt) ) src_loss = K.mean ( 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_opt, pred_src_src_masked_opt) )
src_loss += K.mean ( 10*K.square( target_src_masked_opt - pred_src_src_masked_opt ) ) src_loss += K.mean ( 10*K.square( target_src_masked_opt - pred_src_src_masked_opt ) )
@ -539,7 +535,7 @@ class SAEHDModel(ModelBase):
t_img_warped = t.IMG_WARPED_TRANSFORMED if self.options['random_warp'] else t.IMG_TRANSFORMED t_img_warped = t.IMG_WARPED_TRANSFORMED if self.options['random_warp'] else t.IMG_TRANSFORMED
self.set_training_data_generators ([ 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, SampleGeneratorFace(training_data_src_path, sort_by_yaw_target_samples_path=training_data_dst_path if sort_by_yaw else None, use_caching=False,
random_ct_samples_path=training_data_dst_path if self.options['ct_mode'] != 'none' else None, random_ct_samples_path=training_data_dst_path if self.options['ct_mode'] != 'none' else None,
debug=self.is_debug(), batch_size=self.batch_size, 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 ), sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
@ -548,7 +544,7 @@ class SAEHDModel(ModelBase):
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'resolution': resolution } ] {'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, SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, use_caching=False,
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ), sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
output_sample_types = [ {'types' : (t_img_warped, 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_bgr), 'resolution': resolution},