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SAE: finally collapses are fixed

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iperov 2019-04-24 09:34:41 +04:00
commit 29fbb9f19f
1 changed files with 7 additions and 9 deletions
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models/Model_SAE/Model.py
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@ -248,9 +248,7 @@ class SAEModel(ModelBase):
psd_target_dst_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))] psd_target_dst_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))]
psd_target_dst_anti_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))] psd_target_dst_anti_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))]
alpha_rec = 10
if self.is_training_mode: if self.is_training_mode:
self.src_dst_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1) self.src_dst_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
self.src_dst_mask_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1) self.src_dst_mask_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
@ -265,9 +263,9 @@ class SAEModel(ModelBase):
src_dst_mask_loss_train_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights + self.decoder_dstm.trainable_weights src_dst_mask_loss_train_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights + self.decoder_dstm.trainable_weights
if not self.options['pixel_loss']: if not self.options['pixel_loss']:
src_loss_batch = sum([ alpha_rec*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_ar_opt[i], pred_src_src_masked_ar_opt[i]) for i in range(len(target_src_masked_ar_opt)) ]) src_loss_batch = sum([ 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_ar_opt[i], pred_src_src_masked_ar_opt[i]) for i in range(len(target_src_masked_ar_opt)) ])
else: else:
src_loss_batch = sum([ K.mean ( alpha_rec*K.square( target_src_masked_ar_opt[i] - pred_src_src_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_src_masked_ar_opt)) ]) src_loss_batch = sum([ K.mean ( 50*K.square( target_src_masked_ar_opt[i] - pred_src_src_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_src_masked_ar_opt)) ])
src_loss = K.mean(src_loss_batch) src_loss = K.mean(src_loss_batch)
@ -279,15 +277,15 @@ class SAEModel(ModelBase):
bg_style_power = self.options['bg_style_power'] / 100.0 bg_style_power = self.options['bg_style_power'] / 100.0
if bg_style_power != 0: if bg_style_power != 0:
if not self.options['pixel_loss']: if not self.options['pixel_loss']:
bg_loss = K.mean( (alpha_rec*bg_style_power)*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] )) bg_loss = K.mean( (10*bg_style_power)*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] ))
else: else:
bg_loss = K.mean( (alpha_rec*bg_style_power)*K.square( psd_target_dst_anti_masked_ar[-1] - target_dst_anti_masked_ar[-1] )) bg_loss = K.mean( (50*bg_style_power)*K.square( psd_target_dst_anti_masked_ar[-1] - target_dst_anti_masked_ar[-1] ))
src_loss += bg_loss src_loss += bg_loss
if not self.options['pixel_loss']: if not self.options['pixel_loss']:
dst_loss_batch = sum([ alpha_rec*dssim(kernel_size=int(resolution/11.6),max_value=1.0)(target_dst_masked_ar_opt[i], pred_dst_dst_masked_ar_opt[i]) for i in range(len(target_dst_masked_ar_opt)) ]) dst_loss_batch = sum([ 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)(target_dst_masked_ar_opt[i], pred_dst_dst_masked_ar_opt[i]) for i in range(len(target_dst_masked_ar_opt)) ])
else: else:
dst_loss_batch = sum([ K.mean ( alpha_rec*K.square( target_dst_masked_ar_opt[i] - pred_dst_dst_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_dst_masked_ar_opt)) ]) dst_loss_batch = sum([ K.mean ( 50*K.square( target_dst_masked_ar_opt[i] - pred_dst_dst_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_dst_masked_ar_opt)) ])
dst_loss = K.mean(dst_loss_batch) dst_loss = K.mean(dst_loss_batch)