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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 13:02:15 -07:00
Eyes priority is replaced with Eyes and mouth priority,
Helps to fix eye problems during training like "alien eyes" and wrong eyes direction. Also makes the detail of the teeth higher. New default values with new model: Archi : ‘liae-ud’ AdaBelief : enabled
This commit is contained in:
iperov
parent
dd037d2dea
commit
db83a21244
3 changed files with 102 additions and 63 deletions
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@ -34,7 +34,7 @@ class SAEHDModel(ModelBase):
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default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'f')
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default_models_opt_on_gpu = self.options['models_opt_on_gpu'] = self.load_or_def_option('models_opt_on_gpu', True)
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archi = self.load_or_def_option('archi', 'df')
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archi = self.load_or_def_option('archi', 'liae-ud')
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archi = {'dfuhd':'df-u','liaeuhd':'liae-u'}.get(archi, archi) #backward comp
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default_archi = self.options['archi'] = archi
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@ -43,14 +43,14 @@ class SAEHDModel(ModelBase):
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default_d_dims = self.options['d_dims'] = self.options.get('d_dims', None)
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default_d_mask_dims = self.options['d_mask_dims'] = self.options.get('d_mask_dims', None)
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default_masked_training = self.options['masked_training'] = self.load_or_def_option('masked_training', True)
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default_eyes_prio = self.options['eyes_prio'] = self.load_or_def_option('eyes_prio', False)
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default_eyes_mouth_prio = self.options['eyes_mouth_prio'] = self.load_or_def_option('eyes_mouth_prio', False)
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default_uniform_yaw = self.options['uniform_yaw'] = self.load_or_def_option('uniform_yaw', False)
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default_adabelief = self.options['adabelief'] = self.load_or_def_option('adabelief', False)
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default_adabelief = self.options['adabelief'] = self.load_or_def_option('adabelief', True)
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lr_dropout = self.load_or_def_option('lr_dropout', 'n')
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lr_dropout = {True:'y', False:'n'}.get(lr_dropout, lr_dropout) #backward comp
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default_lr_dropout = self.options['lr_dropout'] = lr_dropout
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default_lr_dropout = self.options['lr_dropout'] = lr_dropout
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default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
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default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
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@ -131,7 +131,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if self.options['face_type'] == 'wf' or self.options['face_type'] == 'head':
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self.options['masked_training'] = io.input_bool ("Masked training", default_masked_training, help_message="This option is available only for 'whole_face' or 'head' type. Masked training clips training area to full_face mask or XSeg mask, thus network will train the faces properly.")
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self.options['eyes_prio'] = io.input_bool ("Eyes priority", default_eyes_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction ( especially on HD architectures ) by forcing the neural network to train eyes with higher priority. before/after https://i.imgur.com/YQHOuSR.jpg ')
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self.options['eyes_mouth_prio'] = io.input_bool ("Eyes and mouth priority", default_eyes_mouth_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction. Also makes the detail of the teeth higher.')
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self.options['uniform_yaw'] = io.input_bool ("Uniform yaw distribution of samples", default_uniform_yaw, help_message='Helps to fix blurry side faces due to small amount of them in the faceset.')
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if self.is_first_run() or ask_override:
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@ -178,7 +178,10 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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'wf' : FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD}[ self.options['face_type'] ]
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eyes_prio = self.options['eyes_prio']
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if 'eyes_prio' in self.options:
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self.options.pop('eyes_prio')
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eyes_mouth_prio = self.options['eyes_mouth_prio']
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archi_split = self.options['archi'].split('-')
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@ -196,8 +199,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.set_iter(0)
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adabelief = self.options['adabelief']
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self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
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self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
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random_warp = False if self.pretrain else self.options['random_warp']
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if self.pretrain:
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@ -230,8 +233,10 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.target_src = tf.placeholder (nn.floatx, bgr_shape)
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self.target_dst = tf.placeholder (nn.floatx, bgr_shape)
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self.target_srcm_all = tf.placeholder (nn.floatx, mask_shape)
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self.target_dstm_all = tf.placeholder (nn.floatx, mask_shape)
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self.target_srcm = tf.placeholder (nn.floatx, mask_shape)
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self.target_srcm_em = tf.placeholder (nn.floatx, mask_shape)
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self.target_dstm = tf.placeholder (nn.floatx, mask_shape)
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self.target_dstm_em = tf.placeholder (nn.floatx, mask_shape)
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# Initializing model classes
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model_archi = nn.DeepFakeArchi(resolution, opts=archi_opts)
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@ -264,7 +269,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.inter_AB = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, name='inter_AB')
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self.inter_B = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, name='inter_B')
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inter_out_ch = self.inter_AB.get_out_ch()
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inter_out_ch = self.inter_AB.get_out_ch()
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inters_out_ch = inter_out_ch*2
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self.decoder = model_archi.Decoder(in_ch=inters_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder')
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@ -288,8 +293,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
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elif 'liae' in archi_type:
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self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
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self.src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
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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')
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@ -335,8 +340,10 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
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gpu_target_src = self.target_src [batch_slice,:,:,:]
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gpu_target_dst = self.target_dst [batch_slice,:,:,:]
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gpu_target_srcm_all = self.target_srcm_all[batch_slice,:,:,:]
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gpu_target_dstm_all = self.target_dstm_all[batch_slice,:,:,:]
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gpu_target_srcm = self.target_srcm[batch_slice,:,:,:]
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gpu_target_srcm_em = self.target_srcm_em[batch_slice,:,:,:]
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gpu_target_dstm = self.target_dstm[batch_slice,:,:,:]
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gpu_target_dstm_em = self.target_dstm_em[batch_slice,:,:,:]
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# process model tensors
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if 'df' in archi_type:
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@ -368,12 +375,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
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gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
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# unpack masks from one combined mask
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gpu_target_srcm = tf.clip_by_value (gpu_target_srcm_all, 0, 1)
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gpu_target_dstm = tf.clip_by_value (gpu_target_dstm_all, 0, 1)
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gpu_target_srcm_eyes = tf.clip_by_value (gpu_target_srcm_all-1, 0, 1)
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gpu_target_dstm_eyes = tf.clip_by_value (gpu_target_dstm_all-1, 0, 1)
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gpu_target_srcm_blur = nn.gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
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gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_blur, 0, 0.5) * 2
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@ -401,8 +402,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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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])
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gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
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if eyes_prio:
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gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_eyes - gpu_pred_src_src*gpu_target_srcm_eyes ), axis=[1,2,3])
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if eyes_mouth_prio:
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gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_em - gpu_pred_src_src*gpu_target_srcm_em ), axis=[1,2,3])
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gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
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@ -423,8 +424,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
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if eyes_prio:
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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])
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if eyes_mouth_prio:
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gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_em - gpu_pred_dst_dst*gpu_target_dstm_em ), axis=[1,2,3])
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gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
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@ -487,7 +488,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
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pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
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pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
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with tf.device (models_opt_device):
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src_loss = tf.concat(gpu_src_losses, 0)
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dst_loss = tf.concat(gpu_dst_losses, 0)
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@ -501,15 +502,17 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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# Initializing training and view functions
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def src_dst_train(warped_src, target_src, target_srcm_all, \
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warped_dst, target_dst, target_dstm_all):
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def src_dst_train(warped_src, target_src, target_srcm, target_srcm_em, \
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warped_dst, target_dst, target_dstm, target_dstm_em, ):
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s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
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feed_dict={self.warped_src :warped_src,
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self.target_src :target_src,
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self.target_srcm_all:target_srcm_all,
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self.target_srcm:target_srcm,
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self.target_srcm_em:target_srcm_em,
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self.warped_dst :warped_dst,
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self.target_dst :target_dst,
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self.target_dstm_all:target_dstm_all,
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self.target_dstm:target_dstm,
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self.target_dstm_em:target_dstm_em,
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})
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return s, d
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self.src_dst_train = src_dst_train
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@ -520,14 +523,16 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.D_train = D_train
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if gan_power != 0:
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def D_src_dst_train(warped_src, target_src, target_srcm_all, \
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warped_dst, target_dst, target_dstm_all):
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def D_src_dst_train(warped_src, target_src, target_srcm, target_srcm_em, \
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warped_dst, target_dst, target_dstm, target_dstm_em, ):
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nn.tf_sess.run ([src_D_src_dst_loss_gv_op], feed_dict={self.warped_src :warped_src,
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self.target_src :target_src,
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self.target_srcm_all:target_srcm_all,
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self.target_srcm:target_srcm,
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self.target_srcm_em:target_srcm_em,
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self.warped_dst :warped_dst,
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self.target_dst :target_dst,
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self.target_dstm_all:target_dstm_all})
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self.target_dstm:target_dstm,
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self.target_dstm_em:target_dstm_em})
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self.D_src_dst_train = D_src_dst_train
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@ -597,7 +602,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE_EYES, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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uniform_yaw_distribution=self.options['uniform_yaw'] or self.pretrain,
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generators_count=src_generators_count ),
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@ -606,7 +612,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE_EYES, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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uniform_yaw_distribution=self.options['uniform_yaw'] or self.pretrain,
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generators_count=dst_generators_count )
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@ -639,26 +646,28 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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bs = self.get_batch_size()
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( (warped_src, target_src, target_srcm_all), \
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(warped_dst, target_dst, target_dstm_all) ) = self.generate_next_samples()
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( (warped_src, target_src, target_srcm, target_srcm_em), \
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(warped_dst, target_dst, target_dstm, target_dstm_em) ) = self.generate_next_samples()
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src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm_all, warped_dst, target_dst, target_dstm_all)
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src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
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for i in range(bs):
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self.last_src_samples_loss.append ( (target_src[i], target_srcm_all[i], src_loss[i] ) )
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self.last_dst_samples_loss.append ( (target_dst[i], target_dstm_all[i], dst_loss[i] ) )
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self.last_src_samples_loss.append ( (target_src[i], target_srcm[i], target_srcm_em[i], src_loss[i] ) )
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self.last_dst_samples_loss.append ( (target_dst[i], target_dstm[i], target_dstm_em[i], dst_loss[i] ) )
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if len(self.last_src_samples_loss) >= bs*16:
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src_samples_loss = sorted(self.last_src_samples_loss, key=operator.itemgetter(2), reverse=True)
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dst_samples_loss = sorted(self.last_dst_samples_loss, key=operator.itemgetter(2), reverse=True)
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src_samples_loss = sorted(self.last_src_samples_loss, key=operator.itemgetter(3), reverse=True)
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dst_samples_loss = sorted(self.last_dst_samples_loss, key=operator.itemgetter(3), reverse=True)
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target_src = np.stack( [ x[0] for x in src_samples_loss[:bs] ] )
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target_srcm_all = np.stack( [ x[1] for x in src_samples_loss[:bs] ] )
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target_src = np.stack( [ x[0] for x in src_samples_loss[:bs] ] )
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target_srcm = np.stack( [ x[1] for x in src_samples_loss[:bs] ] )
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target_srcm_em = np.stack( [ x[2] for x in src_samples_loss[:bs] ] )
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target_dst = np.stack( [ x[0] for x in dst_samples_loss[:bs] ] )
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target_dstm_all = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )
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target_dst = np.stack( [ x[0] for x in dst_samples_loss[:bs] ] )
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target_dstm = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )
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target_dstm_em = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
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src_loss, dst_loss = self.src_dst_train (target_src, target_src, target_srcm_all, target_dst, target_dst, target_dstm_all)
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src_loss, dst_loss = self.src_dst_train (target_src, target_src, target_srcm, target_srcm_em, target_dst, target_dst, target_dstm, target_dstm_em)
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self.last_src_samples_loss = []
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self.last_dst_samples_loss = []
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@ -666,22 +675,19 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.D_train (warped_src, warped_dst)
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if self.gan_power != 0:
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self.D_src_dst_train (warped_src, target_src, target_srcm_all, warped_dst, target_dst, target_dstm_all)
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self.D_src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
|
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|
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return ( ('src_loss', np.mean(src_loss) ), ('dst_loss', np.mean(dst_loss) ), )
|
||||
|
||||
#override
|
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def onGetPreview(self, samples):
|
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( (warped_src, target_src, target_srcm_all,),
|
||||
(warped_dst, target_dst, target_dstm_all,) ) = samples
|
||||
( (warped_src, target_src, target_srcm, target_srcm_em),
|
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(warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
|
||||
|
||||
S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
|
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DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
|
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|
||||
target_srcm_all, target_dstm_all = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm_all, target_dstm_all] )]
|
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|
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target_srcm = np.clip(target_srcm_all, 0, 1)
|
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target_dstm = np.clip(target_dstm_all, 0, 1)
|
||||
target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )]
|
||||
|
||||
n_samples = min(4, self.get_batch_size(), 800 // self.resolution )
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue