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Merge pull request #130 from faceshiftlabs/fix/background-power-whole-image

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Fix/background power whole image
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Jeremy Hummel 2021-03-24 13:12:12 -07:00 • committed by GitHub
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1 changed files with 10 additions and 13 deletions
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models/Model_SAEHD/Model.py
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@ -441,15 +441,12 @@ 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_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_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_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_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_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_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_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_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) gpu_psd_target_dst_style_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_style_blur)
@ -479,14 +476,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
if self.options['background_power'] > 0: if self.options['background_power'] > 0:
bg_factor = self.options['background_power'] bg_factor = self.options['background_power']
if self.options['ms_ssim_loss']: 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: else:
if resolution < 256: 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: 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, 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_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 ( 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_anti_masked - gpu_pred_src_src_anti_masked ), axis=[1,2,3]) 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 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:
@ -521,14 +518,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
if self.options['background_power'] > 0: if self.options['background_power'] > 0:
bg_factor = self.options['background_power'] bg_factor = self.options['background_power']
if self.options['ms_ssim_loss']: 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: else:
if resolution < 256: 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: 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, 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_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 ( 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_anti_masked - gpu_pred_dst_dst_anti_masked ), axis=[1,2,3]) 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] ) gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )