From 30711658833e8a5924af234ebf00806a7545d4d2 Mon Sep 17 00:00:00 2001 From: jh Date: Wed, 24 Mar 2021 09:19:03 -0700 Subject: [PATCH] fix after merge --- models/Model_SAEHD/Model.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py index dc8ac68..f5799a1 100644 --- a/models/Model_SAEHD/Model.py +++ b/models/Model_SAEHD/Model.py @@ -455,13 +455,13 @@ Examples: df, liae, df-d, df-ud, liae-ud, ... if self.options['background_power'] > 0: bg_factor = self.options['background_power'] if self.options['ms_ssim_loss']: - gpu_src_loss = 10 * nn.MsSsim(resolution)(gpu_target_src_anti_masked, gpu_pred_src_src_anti_masked, max_val=1.0) + gpu_src_loss = 10 * nn.MsSsim(resolution)(gpu_target_src, gpu_pred_src_src, max_val=1.0) else: if resolution < 256: - gpu_src_loss += bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1]) + gpu_src_loss += bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1]) else: - gpu_src_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1]) - gpu_src_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1]) + gpu_src_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1]) + gpu_src_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1]) gpu_src_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_src - gpu_pred_src_src ), axis=[1,2,3]) face_style_power = self.options['face_style_power'] / 100.0 @@ -497,13 +497,13 @@ Examples: df, liae, df-d, df-ud, liae-ud, ... if self.options['background_power'] > 0: bg_factor = self.options['background_power'] if self.options['ms_ssim_loss']: - gpu_src_loss = 10 * nn.MsSsim(resolution)(gpu_target_dst_anti_masked, gpu_pred_dst_dst_anti_masked, max_val=1.0) + gpu_src_loss = 10 * nn.MsSsim(resolution)(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0) else: if resolution < 256: - gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1]) + gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1]) else: - gpu_dst_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1]) - gpu_dst_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1]) + gpu_dst_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1]) + gpu_dst_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1]) gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_dst - gpu_pred_dst_dst ), axis=[1,2,3]) gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )