mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 13:02:15 -07:00
SAEHD: lr_dropout now can be ‘n’, ‘y’, ‘cpu’. ‘n’ and ’y’ are the same as before.
‘cpu’ mean enabled on CPU. This allows not to use extra VRAM, sacrificing 20% time of iteration. SAEHD: resolution >= 256 now has second dssim loss function
This commit is contained in:
Colombo
parent
4acaf08ebf
commit
af98407f06
2 changed files with 30 additions and 10 deletions
|
|
@ -38,7 +38,8 @@ class SAEHDModel(ModelBase):
|
|||
default_d_mask_dims = self.options['d_mask_dims'] = self.options.get('d_mask_dims', None)
|
||||
default_masked_training = self.options['masked_training'] = self.load_or_def_option('masked_training', True)
|
||||
default_eyes_prio = self.options['eyes_prio'] = self.load_or_def_option('eyes_prio', False)
|
||||
default_lr_dropout = self.options['lr_dropout'] = self.load_or_def_option('lr_dropout', False)
|
||||
default_lr_dropout = self.options['lr_dropout'] = self.load_or_def_option('lr_dropout', 'n')
|
||||
default_lr_dropout = {True:'y', False:'n'}.get(default_lr_dropout, default_lr_dropout) #backward comp
|
||||
default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
|
||||
default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
|
||||
default_true_face_power = self.options['true_face_power'] = self.load_or_def_option('true_face_power', 0.0)
|
||||
|
|
@ -61,7 +62,13 @@ class SAEHDModel(ModelBase):
|
|||
resolution = np.clip ( (resolution // 16) * 16, 64, 512)
|
||||
self.options['resolution'] = resolution
|
||||
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head'], help_message="Half / mid face / full face / whole face / head. Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face. 'Whole face' covers full area of face include forehead. 'head' covers full head, but requires XSeg for src and dst faceset.").lower()
|
||||
self.options['archi'] = io.input_str ("AE architecture", default_archi, ['df','liae','dfhd','liaehd','dfuhd','liaeuhd'], help_message="'df' keeps faces more natural.\n'liae' can fix overly different face shapes.\n'hd' are experimental versions.").lower()
|
||||
self.options['archi'] = io.input_str ("AE architecture", default_archi, ['df','liae','dfhd','liaehd','dfuhd','liaeuhd'], help_message=\
|
||||
"""
|
||||
'df' keeps faces more natural.
|
||||
'liae' can fix overly different face shapes.
|
||||
'hd' experimental versions.
|
||||
'uhd' increased likeness of the face.
|
||||
""").lower()
|
||||
|
||||
default_d_dims = 48 if self.options['archi'] == 'dfhd' else 64
|
||||
default_d_dims = self.options['d_dims'] = self.load_or_def_option('d_dims', default_d_dims)
|
||||
|
|
@ -92,7 +99,8 @@ class SAEHDModel(ModelBase):
|
|||
if self.is_first_run() or ask_override:
|
||||
self.options['models_opt_on_gpu'] = io.input_bool ("Place models and optimizer on GPU", default_models_opt_on_gpu, help_message="When you train on one GPU, by default model and optimizer weights are placed on GPU to accelerate the process. You can place they on CPU to free up extra VRAM, thus set bigger dimensions.")
|
||||
|
||||
self.options['lr_dropout'] = io.input_bool ("Use learning rate dropout", default_lr_dropout, help_message="When the face is trained enough, you can enable this option to get extra sharpness and reduce subpixel shake for less amount of iterations.")
|
||||
self.options['lr_dropout'] = io.input_str (f"Use learning rate dropout", default_lr_dropout, ['n','y','cpu'], help_message="When the face is trained enough, you can enable this option to get extra sharpness and reduce subpixel shake for less amount of iterations.\nn - disabled.\ny - enabled\ncpu - enabled on CPU. This allows not to use extra VRAM, sacrificing 20% time of iteration.")
|
||||
|
||||
self.options['random_warp'] = io.input_bool ("Enable random warp of samples", 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 and reduce subpixel shake for less amount of iterations.")
|
||||
|
||||
self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 10.0", help_message="Train the network in Generative Adversarial manner. Accelerates the speed of training. Forces the neural network to learn small details of the face. You can enable/disable this option at any time. Typical value is 1.0"), 0.0, 10.0 )
|
||||
|
|
@ -218,7 +226,7 @@ class SAEHDModel(ModelBase):
|
|||
|
||||
# Initialize optimizers
|
||||
lr=5e-5
|
||||
lr_dropout = 0.3 if self.options['lr_dropout'] and not self.pretrain else 1.0
|
||||
lr_dropout = 0.3 if self.options['lr_dropout'] in ['y','cpu'] and not self.pretrain else 1.0
|
||||
clipnorm = 1.0 if self.options['clipgrad'] else 0.0
|
||||
self.src_dst_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
|
||||
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
|
||||
|
|
@ -227,16 +235,16 @@ class SAEHDModel(ModelBase):
|
|||
elif 'liae' in archi:
|
||||
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
|
||||
|
||||
self.src_dst_opt.initialize_variables (self.src_dst_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu)
|
||||
self.src_dst_opt.initialize_variables (self.src_dst_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
|
||||
|
||||
if self.options['true_face_power'] != 0:
|
||||
self.D_code_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_code_opt')
|
||||
self.D_code_opt.initialize_variables ( self.code_discriminator.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
|
||||
self.D_code_opt.initialize_variables ( self.code_discriminator.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
|
||||
self.model_filename_list += [ (self.D_code_opt, 'D_code_opt.npy') ]
|
||||
|
||||
if gan_power != 0:
|
||||
self.D_src_dst_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_src_dst_opt')
|
||||
self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights()+self.D_dst.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
|
||||
self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights()+self.D_dst.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
|
||||
self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_dst_opt.npy') ]
|
||||
|
||||
if self.is_training:
|
||||
|
|
@ -323,7 +331,11 @@ class SAEHDModel(ModelBase):
|
|||
gpu_psd_target_dst_masked = gpu_pred_src_dst*gpu_target_dstm_blur
|
||||
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_blur)
|
||||
|
||||
gpu_src_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
|
||||
if resolution < 256:
|
||||
gpu_src_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
|
||||
else:
|
||||
gpu_src_loss = tf.reduce_mean ( 5*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
|
||||
gpu_src_loss += tf.reduce_mean ( 5*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
|
||||
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
|
||||
|
||||
if eyes_prio:
|
||||
|
|
@ -340,8 +352,13 @@ class SAEHDModel(ModelBase):
|
|||
gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*nn.dssim(gpu_psd_target_dst_anti_masked, gpu_target_dst_anti_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
|
||||
gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*tf.square( gpu_psd_target_dst_anti_masked - gpu_target_dst_anti_masked), axis=[1,2,3] )
|
||||
|
||||
gpu_dst_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
|
||||
if resolution < 256:
|
||||
gpu_dst_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
|
||||
else:
|
||||
gpu_dst_loss = tf.reduce_mean ( 5*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
|
||||
gpu_dst_loss += tf.reduce_mean ( 5*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
|
||||
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
|
||||
|
||||
|
||||
if eyes_prio:
|
||||
gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_eyes - gpu_pred_dst_dst*gpu_target_dstm_eyes ), axis=[1,2,3])
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue