From 9b80652ed8acbb7a294f1974a600968eebbbfe06 Mon Sep 17 00:00:00 2001 From: sinofis Date: Wed, 13 Jan 2021 00:18:42 +0100 Subject: [PATCH] model.py - removed references to old gan --- models/Model_SAEHD/Model.py | 53 +++++++++++-------------------------- 1 file changed, 16 insertions(+), 37 deletions(-) diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py index ac455d9..02dbddf 100644 --- a/models/Model_SAEHD/Model.py +++ b/models/Model_SAEHD/Model.py @@ -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