diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py index 394b010..bf17d85 100644 --- a/models/Model_SAEHD/Model.py +++ b/models/Model_SAEHD/Model.py @@ -229,7 +229,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ... random_src_flip = self.random_src_flip if not self.pretrain else True random_dst_flip = self.random_dst_flip if not self.pretrain else True blur_out_mask = self.options['blur_out_mask'] - learn_dst_bg = False#True if self.pretrain: self.options_show_override['gan_power'] = 0.0 @@ -409,7 +408,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ... gpu_pred_src_src, gpu_pred_src_srcm = self.decoder(gpu_src_code) gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder(gpu_dst_code) - gpu_pred_dst_dst_no_code_grad, _ = self.decoder(tf.stop_gradient(gpu_dst_code)) gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code) gpu_pred_src_dst_no_code_grad, _ = self.decoder(tf.stop_gradient(gpu_src_dst_code)) @@ -426,28 +424,22 @@ Examples: df, liae, df-d, df-ud, liae-ud, ... gpu_target_srcm_anti_blur = 1.0-gpu_target_srcm_blur 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_style_anti_blur = 1.0 - gpu_target_dstm_style_blur gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2 - gpu_target_dstm_anti_blur = 1.0-gpu_target_dstm_blur - - gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur - gpu_target_dst_style_masked = gpu_target_dst*gpu_target_dstm_style_blur - gpu_target_dst_style_anti_masked = gpu_target_dst*gpu_target_dstm_style_anti_blur + + gpu_style_mask_blur = nn.gaussian_blur(gpu_pred_src_dstm*gpu_pred_dst_dstm, max(1, resolution // 32) ) + gpu_style_mask_blur = tf.stop_gradient(tf.clip_by_value(gpu_target_srcm_blur, 0, 1.0)) + gpu_style_mask_anti_blur = 1.0 - gpu_style_mask_blur + + gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur gpu_target_src_anti_masked = gpu_target_src*gpu_target_srcm_anti_blur - gpu_target_dst_anti_masked = gpu_target_dst*gpu_target_dstm_anti_blur gpu_pred_src_src_anti_masked = gpu_pred_src_src*gpu_target_srcm_anti_blur - gpu_pred_dst_dst_anti_masked = gpu_pred_dst_dst*gpu_target_dstm_anti_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 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_style_masked = gpu_pred_src_dst*gpu_target_dstm_style_blur - gpu_psd_target_dst_style_anti_masked = gpu_pred_src_dst*gpu_target_dstm_style_anti_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]) else: @@ -463,12 +455,14 @@ 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_pred_src_dst_no_code_grad*tf.stop_gradient(gpu_pred_src_dstm), tf.stop_gradient(gpu_pred_dst_dst*gpu_pred_dst_dstm), gaussian_blur_radius=resolution//8, loss_weight=10000*face_style_power) - #gpu_src_loss += nn.style_loss(gpu_psd_target_dst_style_masked, gpu_target_dst_style_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_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] ) + gpu_target_dst_style_anti_masked = gpu_target_dst*gpu_style_mask_anti_blur + gpu_psd_style_anti_masked = gpu_pred_src_dst*gpu_style_mask_anti_blur + + gpu_src_loss += tf.reduce_mean( (10*bg_style_power)*nn.dssim( gpu_psd_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_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]) @@ -487,9 +481,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ... gpu_G_loss = gpu_src_loss + gpu_dst_loss - if learn_dst_bg and masked_training and 'liae' in archi_type: - gpu_G_loss += tf.reduce_mean( tf.square(gpu_pred_dst_dst_no_code_grad*gpu_target_dstm_anti_blur-gpu_target_dst_anti_masked),axis=[1,2,3] ) - def DLoss(labels,logits): return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits), axis=[1,2,3])