github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-08-21 22:13:20 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

test

Browse source
This commit is contained in:
jh 2021-03-17 12:16:03 -07:00
commit 4e08864531
1 changed files with 6 additions and 7 deletions
Download patch file Download diff file Expand all files Collapse all files

13
models/Model_SAEHD/Model.py
View file

@ -465,7 +465,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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] ) 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 self.options['ms_ssim_loss']: if self.options['ms_ssim_loss']:
gpu_dst_loss = tf.reduce_mean ( 10*nn.MsSsim(resolution)(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0)) gpu_dst_loss = 10*nn.MsSsim(resolution)(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0)
else: else:
if resolution < 256: 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]) 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])
@ -484,15 +484,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
gpu_target_part_mask = gpu_target_dstm_mouth gpu_target_part_mask = gpu_target_dstm_mouth
if self.options['ms_ssim_loss']: if self.options['ms_ssim_loss']:
gpu_dst_loss += tf.reduce_mean ( 300*nn.MsSsim(resolution, kernel_size=5)(gpu_target_dst*gpu_target_part_mask, gpu_pred_dst_dst*gpu_target_part_mask, max_val=1.0)) gpu_dst_loss += 300 * nn.MsSsim(resolution, kernel_size=5)(gpu_target_dst*gpu_target_part_mask, gpu_pred_dst_dst*gpu_target_part_mask, max_val=1.0)
else: else:
gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_part_mask - gpu_pred_dst_dst*gpu_target_part_mask ), axis=[1,2,3]) gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_part_mask - gpu_pred_dst_dst*gpu_target_part_mask ), axis=[1,2,3])
# FIXME if self.options['ms_ssim_loss']:
# if self.options['ms_ssim_loss']: gpu_dst_loss += 10 * nn.MsSsim(resolution)(gpu_target_dstm, gpu_pred_dst_dstm, max_val=1.0)
# gpu_dst_loss += tf.reduce_mean ( 10*nn.MsSsim(resolution)(gpu_target_dstm, gpu_pred_dst_dstm, max_val=1.0)) else:
# else: gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
gpu_src_losses += [gpu_src_loss] gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss] gpu_dst_losses += [gpu_dst_loss]