github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-08-22 06:23:20 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

test

Browse source
This commit is contained in:
jh 2021-03-17 12:13:28 -07:00
commit 40924b5f74
1 changed files with 5 additions and 19 deletions
Download patch file Download diff file Expand all files Collapse all files

24
models/Model_SAEHD/Model.py
View file

@ -446,28 +446,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
gpu_target_part_mask = gpu_target_srcm_mouth gpu_target_part_mask = gpu_target_srcm_mouth
if self.options['ms_ssim_loss']: if self.options['ms_ssim_loss']:
# FIXME gpu_src_loss += 300 * nn.MsSsim(resolution, kernel_size=5)(gpu_target_src*gpu_target_part_mask, gpu_pred_src_src*gpu_target_part_mask, max_val=1.0)
ms_ssim_loss = nn.MsSsim(resolution, kernel_size=5)(gpu_target_src*gpu_target_part_mask, gpu_pred_src_src*gpu_target_part_mask, max_val=1.0)
print('ms_ssim_loss.shape: ', ms_ssim_loss.shape)
ms_ssim_mean_loss = tf.reduce_mean(300 * ms_ssim_loss)
print('ms_ssim_mean_loss.shape: ', ms_ssim_mean_loss.shape)
abs_loss = tf.abs ( gpu_target_src*gpu_target_part_mask - gpu_pred_src_src*gpu_target_part_mask )
print('abs_loss.shape: ', abs_loss.shape)
abs_mean_loss = tf.reduce_mean(300 * abs_loss, axis=[1,2,3])
print('abs_mean_loss.shape: ', abs_mean_loss.shape)
gpu_src_loss += 300*nn.MsSsim(resolution, kernel_size=5)(gpu_target_src*gpu_target_part_mask, gpu_pred_src_src*gpu_target_part_mask, max_val=1.0)
# gpu_src_loss += tf.reduce_mean ( 300*nn.MsSsim(resolution, kernel_size=5)(gpu_target_src*gpu_target_part_mask, gpu_pred_src_src*gpu_target_part_mask, max_val=1.0))
else: else:
gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_part_mask - gpu_pred_src_src*gpu_target_part_mask ), axis=[1,2,3]) gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_part_mask - gpu_pred_src_src*gpu_target_part_mask ), axis=[1,2,3])
# FIXME if self.options['ms_ssim_loss']:
# if self.options['ms_ssim_loss']: gpu_src_loss += 10 * nn.MsSsim(resolution)(gpu_target_srcm, gpu_pred_src_srcm, max_val=1.0)
# gpu_src_loss += tf.reduce_mean ( 10*nn.MsSsim(resolution)(gpu_target_srcm, gpu_pred_src_srcm, max_val=1.0)) else:
# else: gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
print('gpu_target_srcm.shape:', gpu_target_srcm.shape)
print('gpu_pred_src_srcm.shape:', gpu_pred_src_srcm.shape)
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
face_style_power = self.options['face_style_power'] / 100.0 face_style_power = self.options['face_style_power'] / 100.0
if face_style_power != 0 and not self.pretrain: if face_style_power != 0 and not self.pretrain: