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old gan
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sinofis
parent
97295c2246
commit
7233d54324
1 changed files with 64 additions and 21 deletions
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@ -53,6 +53,8 @@ class SAEHDModel(ModelBase):
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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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default_gan_old = self.options['gan_old'] = self.load_or_def_option('gan_old', False)
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default_true_face_power = self.options['true_face_power'] = self.load_or_def_option('true_face_power', 0.0)
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default_face_style_power = self.options['face_style_power'] = self.load_or_def_option('face_style_power', 0.0)
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default_bg_style_power = self.options['bg_style_power'] = self.load_or_def_option('bg_style_power', 0.0)
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@ -144,6 +146,9 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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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. Forces the neural network to learn small details of the face. Enable it only when the face is trained enough and don't disable. Typical value is 0.1"), 0.0, 10.0 )
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if (self.options['gan_power'] != 0):
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self.options['gan_old'] = io.input_bool ("Use old GAN version", default_gan_old, help_message="Use older version of GAN." )
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if 'df' in self.options['archi']:
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self.options['true_face_power'] = np.clip ( io.input_number ("'True face' power.", default_true_face_power, add_info="0.0000 .. 1.0", help_message="Experimental option. Discriminates result face to be more like src face. Higher value - stronger discrimination. Typical value is 0.01 . Comparison - https://i.imgur.com/czScS9q.png"), 0.0, 1.0 )
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else:
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@ -196,6 +201,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.set_iter(0)
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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_old = gan_old = self.options['gan_old']
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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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@ -274,8 +281,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if self.is_training:
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if gan_power != 0:
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self.D_src = nn.UNetPatchDiscriminator(patch_size=resolution//16, in_ch=input_ch, name="D_src")
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self.model_filename_list += [ [self.D_src, 'D_src_v2.npy'] ]
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if gan_old:
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self.D_src = nn.PatchDiscriminator(patch_size=resolution//16, in_ch=input_ch, name="D_src")
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self.D_src_x2 = nn.PatchDiscriminator(patch_size=resolution//32, in_ch=input_ch, name="D_src_x2")
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self.model_filename_list += [ [self.D_src, 'D_src.npy'] ]
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self.model_filename_list += [ [self.D_src_x2, 'D_src_x2.npy'] ]
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elif:
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self.D_src = nn.UNetPatchDiscriminator(patch_size=resolution//16, in_ch=input_ch, name="D_src")
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self.model_filename_list += [ [self.D_src, 'D_src_v2.npy'] ]
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# Initialize optimizers
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lr=5e-5
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@ -298,8 +311,13 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if gan_power != 0:
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self.D_src_dst_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_src_dst_opt')
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self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')#+self.D_src_x2.get_weights()
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self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_v2_opt.npy') ]
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if gan_old:
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self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights()+self.D_src_x2.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
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self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_dst_opt.npy') ]
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elif:
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self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')#+self.D_src_x2.get_weights()
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self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_v2_opt.npy') ]
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if self.is_training:
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# Adjust batch size for multiple GPU
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@ -465,30 +483,55 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_D_code_loss_gvs += [ nn.gradients (gpu_D_code_loss, self.code_discriminator.get_weights() ) ]
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if gan_power != 0:
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gpu_pred_src_src_d, \
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gpu_pred_src_src_d2 = self.D_src(gpu_pred_src_src_masked_opt)
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if gan_old:
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gpu_pred_src_src_d = self.D_src(gpu_pred_src_src_masked_opt)
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gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d) gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d)
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gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d) gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d)
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gpu_target_src_d = self.D_src(gpu_target_src_masked_opt)
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gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d)
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gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d)
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gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d)
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gpu_pred_src_src_x2_d = self.D_src_x2(gpu_pred_src_src_masked_opt) gpu_pred_src_src_d2_ones = tf.ones_like (gpu_pred_src_src_d2)
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gpu_pred_src_src_x2_d_ones = tf.ones_like (gpu_pred_src_src_x2_d) gpu_pred_src_src_d2_zeros = tf.zeros_like(gpu_pred_src_src_d2)
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gpu_pred_src_src_x2_d_zeros = tf.zeros_like(gpu_pred_src_src_x2_d)
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gpu_target_src_x2_d = self.D_src_x2(gpu_target_src_masked_opt) gpu_target_src_d, \
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gpu_target_src_x2_d_ones = tf.ones_like(gpu_target_src_x2_d)
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gpu_pred_src_src_d2_ones = tf.ones_like (gpu_pred_src_src_d2)
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gpu_pred_src_src_d2_zeros = tf.zeros_like(gpu_pred_src_src_d2)
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gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
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DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \
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(DLoss(gpu_target_src_x2_d_ones , gpu_target_src_x2_d) + \
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DLoss(gpu_pred_src_src_x2_d_zeros, gpu_pred_src_src_x2_d) ) * 0.5
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gpu_target_src_d, \
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gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
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gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss,
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self.D_src.get_weights()+self.D_src_x2.get_weights() ) ]
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gpu_G_loss += 0.5*gan_power*( DLoss(gpu_pred_src_src_d_ones,
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gpu_pred_src_src_d) + DLoss(gpu_pred_src_src_x2_d_ones, gpu_pred_src_src_x2_d))
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gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d)
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gpu_target_src_d2_ones = tf.ones_like(gpu_target_src_d2)
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elif:
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gpu_pred_src_src_d, \
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gpu_pred_src_src_d2 = self.D_src(gpu_pred_src_src_masked_opt)
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gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
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DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \
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(DLoss(gpu_target_src_d2_ones , gpu_target_src_d2) + \
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DLoss(gpu_pred_src_src_d2_zeros , gpu_pred_src_src_d2) ) * 0.5
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gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d)
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gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d)
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gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.D_src.get_weights() ) ]#+self.D_src_x2.get_weights()
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gpu_pred_src_src_d2_ones = tf.ones_like (gpu_pred_src_src_d2)
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gpu_pred_src_src_d2_zeros = tf.zeros_like(gpu_pred_src_src_d2)
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gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + \
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DLoss(gpu_pred_src_src_d2_ones, gpu_pred_src_src_d2))
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gpu_target_src_d, \
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gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
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gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d)
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gpu_target_src_d2_ones = tf.ones_like(gpu_target_src_d2)
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gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
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DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \
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(DLoss(gpu_target_src_d2_ones , gpu_target_src_d2) + \
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DLoss(gpu_pred_src_src_d2_zeros , gpu_pred_src_src_d2) ) * 0.5
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gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.D_src.get_weights() ) ]#+self.D_src_x2.get_weights()
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gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + \
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DLoss(gpu_pred_src_src_d2_ones, gpu_pred_src_src_d2))
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gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ]
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