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fix: Apply background source to the entire images (not just the area outside the mask)

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jh 2021-03-24 09:14:29 -07:00
commit 1829f16379
1 changed files with 8 additions and 11 deletions
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19
models/Model_SAEHD/Model.py
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@ -416,15 +416,12 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
gpu_target_dst_style_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_style_blur)
gpu_target_src_anti_masked = gpu_target_src*(1.0-gpu_target_srcm_blur)
gpu_target_dst_anti_masked = gpu_target_dst_style_anti_masked
gpu_target_src_masked_opt = gpu_target_src*gpu_target_srcm_blur if masked_training else gpu_target_src
gpu_target_dst_masked_opt = gpu_target_dst_masked if masked_training else gpu_target_dst
gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
gpu_pred_src_src_anti_masked = gpu_pred_src_src*(1.0-gpu_target_srcm_blur)
gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
gpu_pred_dst_dst_anti_masked = gpu_pred_dst_dst*(1.0-gpu_target_dstm_blur)
gpu_psd_target_dst_style_masked = gpu_pred_src_dst*gpu_target_dstm_style_blur
gpu_psd_target_dst_style_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_style_blur)
@ -451,11 +448,11 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
if self.options['background_power'] > 0:
bg_factor = self.options['background_power']
if resolution < 256:
gpu_src_loss += bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_src_anti_masked, gpu_pred_src_src_anti_masked, 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_anti_masked, gpu_pred_src_src_anti_masked, 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_anti_masked, gpu_pred_src_src_anti_masked, 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_anti_masked - gpu_pred_src_src_anti_masked ), axis=[1,2,3])
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
if face_style_power != 0 and not self.pretrain:
@ -487,11 +484,11 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
if self.options['background_power'] > 0:
bg_factor = self.options['background_power']
if resolution < 256:
gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_dst_anti_masked, gpu_pred_dst_dst_anti_masked, 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_anti_masked, gpu_pred_dst_dst_anti_masked, 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_anti_masked, gpu_pred_dst_dst_anti_masked, 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_anti_masked - gpu_pred_dst_dst_anti_masked ), axis=[1,2,3])
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] )