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model.py - removed references to old gan

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sinofis 2021-01-13 00:18:42 +01:00
commit 9b80652ed8
1 changed files with 16 additions and 37 deletions
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53
models/Model_SAEHD/Model.py
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@ -54,9 +54,6 @@ class SAEHDModel(ModelBase):
default_lr_dropout = self.options['lr_dropout'] = lr_dropout
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_gan_old = self.options['gan_old'] = self.load_or_def_option('gan_old', False)
default_true_face_power = self.options['true_face_power'] = self.load_or_def_option('true_face_power', 0.0)
default_face_style_power = self.options['face_style_power'] = self.load_or_def_option('face_style_power', 0.0)
default_bg_style_power = self.options['bg_style_power'] = self.load_or_def_option('bg_style_power', 0.0)
@ -218,8 +215,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
adabelief = self.options['adabelief']
self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
self.gan_old = gan_old = self.options['gan_old']
random_warp = False if self.pretrain else self.options['random_warp']
if self.pretrain:
@ -497,47 +492,31 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
gpu_D_code_loss_gvs += [ nn.gradients (gpu_D_code_loss, self.code_discriminator.get_weights() ) ]
if gan_power != 0:
if gan_old:
gpu_pred_src_src_d = self.D_src(gpu_pred_src_src_masked_opt)
gpu_pred_src_src_d, \
gpu_pred_src_src_d2 = self.D_src(gpu_pred_src_src_masked_opt)
gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d)
gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d)
gpu_target_src_d = self.D_src(gpu_target_src_masked_opt)
gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d)
gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d)
gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d)
gpu_pred_src_src_x2_d = self.D_src_x2(gpu_pred_src_src_masked_opt)
gpu_pred_src_src_x2_d_ones = tf.ones_like (gpu_pred_src_src_x2_d)
gpu_pred_src_src_x2_d_zeros = tf.zeros_like(gpu_pred_src_src_x2_d)
gpu_target_src_x2_d = self.D_src_x2(gpu_target_src_masked_opt)
gpu_target_src_x2_d_ones = tf.ones_like(gpu_target_src_x2_d)
gpu_pred_src_src_d2_ones = tf.ones_like (gpu_pred_src_src_d2)
gpu_pred_src_src_d2_zeros = tf.zeros_like(gpu_pred_src_src_d2)
gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \
(DLoss(gpu_target_src_x2_d_ones , gpu_target_src_x2_d) + \
DLoss(gpu_pred_src_src_x2_d_zeros, gpu_pred_src_src_x2_d) ) * 0.5
gpu_target_src_d, \
gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss,
self.D_src.get_weights()+self.D_src_x2.get_weights() ) ]
gpu_G_loss += 0.5*gan_power*( DLoss(gpu_pred_src_src_d_ones,
gpu_pred_src_src_d) + DLoss(gpu_pred_src_src_x2_d_ones, gpu_pred_src_src_x2_d))
gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d)
gpu_target_src_d2_ones = tf.ones_like(gpu_target_src_d2)
else:
gpu_pred_src_src_d, \
gpu_pred_src_src_d2 = self.D_src(gpu_pred_src_src_masked_opt)
gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \
(DLoss(gpu_target_src_d2_ones , gpu_target_src_d2) + \
DLoss(gpu_pred_src_src_d2_zeros , gpu_pred_src_src_d2) ) * 0.5
gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d)
gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d)
gpu_pred_src_src_d2_ones = tf.ones_like (gpu_pred_src_src_d2)
gpu_pred_src_src_d2_zeros = tf.zeros_like(gpu_pred_src_src_d2)
gpu_target_src_d, \
gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.D_src.get_weights() ) ]#+self.D_src_x2.get_weights()
gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + \
DLoss(gpu_pred_src_src_d2_ones, gpu_pred_src_src_d2))
if masked_training:
# Minimal src-src-bg rec with total_variation_mse to suppress random bright dots from gan