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SAEHD: lr_dropout now as an option, and disabled by default
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Colombo
parent
951942821d
commit
021bb6d128
1 changed files with 18 additions and 14 deletions
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@ -20,7 +20,7 @@ class SAEHDModel(ModelBase):
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default_resolution = 128
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default_archi = 'df'
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default_face_type = 'f'
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if is_first_run:
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resolution = io.input_int("Resolution ( 64-256 ?:help skip:128) : ", default_resolution, help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
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@ -38,7 +38,7 @@ class SAEHDModel(ModelBase):
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self.options['learn_mask'] = io.input_bool ( f"Learn mask? (y/n, ?:help skip:{yn_str[default_learn_mask]} ) : " , default_learn_mask, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case converter forced to use 'not predicted mask' that is not smooth as predicted.")
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else:
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self.options['learn_mask'] = self.options.get('learn_mask', default_learn_mask)
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if (is_first_run or ask_override) and 'tensorflow' in self.device_config.backend:
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def_optimizer_mode = self.options.get('optimizer_mode', 1)
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self.options['optimizer_mode'] = io.input_int ("Optimizer mode? ( 1,2,3 ?:help skip:%d) : " % (def_optimizer_mode), def_optimizer_mode, help_message="1 - no changes. 2 - allows you to train x2 bigger network consuming RAM. 3 - allows you to train x3 bigger network consuming huge amount of RAM and slower, depends on CPU power.")
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@ -63,8 +63,11 @@ class SAEHDModel(ModelBase):
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default_true_face_training = self.options.get('true_face_training', False)
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default_face_style_power = self.options.get('face_style_power', 0.0)
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default_bg_style_power = self.options.get('bg_style_power', 0.0)
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if is_first_run or ask_override:
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default_lr_dropout = self.options.get('lr_dropout', False)
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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.")
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default_random_warp = self.options.get('random_warp', True)
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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.")
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@ -78,14 +81,14 @@ class SAEHDModel(ModelBase):
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default_ct_mode = self.options.get('ct_mode', 'none')
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self.options['ct_mode'] = io.input_str (f"Color transfer mode apply to src faceset. ( none/rct/lct/mkl/idt/sot, ?:help skip:{default_ct_mode}) : ", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
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if nnlib.device.backend != 'plaidML': # todo https://github.com/plaidml/plaidml/issues/301
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default_clipgrad = False if is_first_run else self.options.get('clipgrad', False)
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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.")
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else:
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self.options['clipgrad'] = False
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else:
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self.options['lr_dropout'] = self.options.get('lr_dropout', default_lr_dropout)
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self.options['random_warp'] = self.options.get('random_warp', True)
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self.options['true_face_training'] = self.options.get('true_face_training', default_true_face_training)
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self.options['face_style_power'] = self.options.get('face_style_power', default_face_style_power)
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@ -452,10 +455,11 @@ class SAEHDModel(ModelBase):
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psd_target_dst_anti_masked = self.model.pred_src_dst*(1.0 - target_dstm)
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if self.is_training_mode:
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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)
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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)
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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)
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lr_dropout = 0.3 if self.options['lr_dropout'] else 0.0
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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)
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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)
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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)
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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) )
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src_loss += K.mean ( 10*K.square( target_src_masked_opt - pred_src_src_masked_opt ) )
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@ -520,7 +524,7 @@ class SAEHDModel(ModelBase):
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face_type = t.FACE_TYPE_MID_FULL
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elif self.options['face_type'] == 'f':
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face_type = t.FACE_TYPE_FULL
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t_mode_bgr = t.MODE_BGR if not self.pretrain else t.MODE_BGR_SHUFFLE
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training_data_src_path = self.training_data_src_path
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@ -531,11 +535,11 @@ class SAEHDModel(ModelBase):
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training_data_src_path = self.pretraining_data_path
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training_data_dst_path = self.pretraining_data_path
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sort_by_yaw = False
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t_img_warped = t.IMG_WARPED_TRANSFORMED if self.options['random_warp'] else t.IMG_TRANSFORMED
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t_img_warped = t.IMG_WARPED_TRANSFORMED if self.options['random_warp'] else t.IMG_TRANSFORMED
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self.set_training_data_generators ([
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SampleGeneratorFace(training_data_src_path, sort_by_yaw_target_samples_path=training_data_dst_path if sort_by_yaw else None, use_caching=False,
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SampleGeneratorFace(training_data_src_path, sort_by_yaw_target_samples_path=training_data_dst_path if sort_by_yaw else None,
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random_ct_samples_path=training_data_dst_path if self.options['ct_mode'] != 'none' else None,
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debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
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@ -544,7 +548,7 @@ class SAEHDModel(ModelBase):
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{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'resolution': resolution } ]
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),
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SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, use_caching=False,
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SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
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output_sample_types = [ {'types' : (t_img_warped, face_type, t_mode_bgr), 'resolution':resolution},
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{'types' : (t.IMG_TRANSFORMED, face_type, t_mode_bgr), 'resolution': resolution},
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