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fix mask for style losses

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This commit is contained in:
Colombo 2020-07-17 17:34:07 +04:00
parent 0efebe6bc5
commit b9b97861e1
1 changed files with 7 additions and 6 deletions
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13
models/Model_SAEHD/Model.py
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@ -361,10 +361,11 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_blur, 0, 0.5) * 2
gpu_target_dstm_blur = nn.gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
gpu_target_dstm_style_blur = gpu_target_dstm_blur #default style mask is 0.5 on boundary
gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
gpu_target_dst_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_blur)
gpu_target_dst_style_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_style_blur)
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
@ -372,8 +373,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
gpu_psd_target_dst_masked = gpu_pred_src_dst*gpu_target_dstm_blur
gpu_psd_target_dst_anti_masked = gpu_pred_src_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)
if resolution < 256:
gpu_src_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
@ -389,12 +390,12 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
face_style_power = self.options['face_style_power'] / 100.0
if face_style_power != 0 and not self.pretrain:
gpu_src_loss += nn.style_loss(gpu_psd_target_dst_masked, gpu_target_dst_masked, gaussian_blur_radius=resolution//16, loss_weight=10000*face_style_power)
gpu_src_loss += nn.style_loss(gpu_psd_target_dst_style_masked, gpu_target_dst_masked, gaussian_blur_radius=resolution//16, loss_weight=10000*face_style_power)
bg_style_power = self.options['bg_style_power'] / 100.0
if bg_style_power != 0 and not self.pretrain:
gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*nn.dssim(gpu_psd_target_dst_anti_masked, gpu_target_dst_anti_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*tf.square( gpu_psd_target_dst_anti_masked - gpu_target_dst_anti_masked), axis=[1,2,3] )
gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*nn.dssim( gpu_psd_target_dst_style_anti_masked, gpu_target_dst_style_anti_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*tf.square(gpu_psd_target_dst_style_anti_masked - gpu_target_dst_style_anti_masked), axis=[1,2,3] )
if resolution < 256:
gpu_dst_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])