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SAEHD: removed 'dst_denoise' option. Added -t arhi option.
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iperov
parent
01f1a084b4
commit
6e094d873d
2 changed files with 136 additions and 73 deletions
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@ -35,9 +35,7 @@ class SAEHDModel(ModelBase):
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default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'f')
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default_models_opt_on_gpu = self.options['models_opt_on_gpu'] = self.load_or_def_option('models_opt_on_gpu', True)
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archi = self.load_or_def_option('archi', 'liae-ud')
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archi = {'dfuhd':'df-u','liaeuhd':'liae-u'}.get(archi, archi) #backward comp
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default_archi = self.options['archi'] = archi
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default_archi = self.options['archi'] = self.load_or_def_option('archi', 'liae-ud')
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default_ae_dims = self.options['ae_dims'] = self.load_or_def_option('ae_dims', 256)
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default_e_dims = self.options['e_dims'] = self.load_or_def_option('e_dims', 64)
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@ -47,7 +45,6 @@ class SAEHDModel(ModelBase):
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default_eyes_mouth_prio = self.options['eyes_mouth_prio'] = self.load_or_def_option('eyes_mouth_prio', False)
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default_uniform_yaw = self.options['uniform_yaw'] = self.load_or_def_option('uniform_yaw', False)
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default_blur_out_mask = self.options['blur_out_mask'] = self.load_or_def_option('blur_out_mask', False)
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default_dst_denoise = self.options['dst_denoise'] = self.load_or_def_option('dst_denoise', False)
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default_adabelief = self.options['adabelief'] = self.load_or_def_option('adabelief', True)
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@ -107,7 +104,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if archi_opts is not None:
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if len(archi_opts) == 0:
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continue
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if len([ 1 for opt in archi_opts if opt not in ['u','d'] ]) != 0:
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if len([ 1 for opt in archi_opts if opt not in ['u','d','t'] ]) != 0:
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continue
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if 'd' in archi_opts:
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@ -141,7 +138,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.options['eyes_mouth_prio'] = io.input_bool ("Eyes and mouth priority", default_eyes_mouth_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction. Also makes the detail of the teeth higher.')
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self.options['uniform_yaw'] = io.input_bool ("Uniform yaw distribution of samples", default_uniform_yaw, help_message='Helps to fix blurry side faces due to small amount of them in the faceset.')
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self.options['blur_out_mask'] = io.input_bool ("Blur out mask", default_blur_out_mask, help_message='Blurs nearby area outside of applied face mask of training samples. The result is the background near the face is smoothed and less noticeable on swapped face. The exact xseg mask in src and dst faceset is required.')
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self.options['dst_denoise'] = io.input_bool ("Denoise DST faceset.", default_dst_denoise, help_message='Used in RTM(ReadyToMerge) training with RTM DST faceset. Removes high frequency noise keeping edges. Result is better face syncronization with any face. Can be enabled at any time.')
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default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
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default_gan_patch_size = self.options['gan_patch_size'] = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8)
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@ -233,7 +229,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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random_src_flip = self.random_src_flip if not self.pretrain else True
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random_dst_flip = self.random_dst_flip if not self.pretrain else True
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blur_out_mask = self.options['blur_out_mask']
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dst_denoise = self.options['dst_denoise']
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learn_dst_bg = False#True
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if self.pretrain:
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self.options_show_override['gan_power'] = 0.0
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@ -327,8 +323,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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elif 'liae' in archi_type:
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self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
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self.src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
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self.src_dst_opt.initialize_variables (self.src_dst_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
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self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
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@ -413,6 +407,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_pred_src_src, gpu_pred_src_srcm = self.decoder(gpu_src_code)
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gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
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gpu_pred_dst_dst_no_code_grad, _ = self.decoder(tf.stop_gradient(gpu_dst_code))
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gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
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gpu_pred_src_src_list.append(gpu_pred_src_src)
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@ -425,25 +420,30 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_target_srcm_blur = nn.gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
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gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_blur, 0, 0.5) * 2
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gpu_target_srcm_anti_blur = 1.0-gpu_target_srcm_blur
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gpu_target_dstm_blur = nn.gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
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gpu_target_dstm_style_blur = gpu_target_dstm_blur #default style mask is 0.5 on boundary
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gpu_target_dstm_style_anti_blur = 1.0 - gpu_target_dstm_style_blur
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gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
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gpu_target_dstm_anti_blur = 1.0-gpu_target_dstm_blur
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gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
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gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
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gpu_target_dst_style_masked = gpu_target_dst*gpu_target_dstm_style_blur
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gpu_target_dst_style_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_style_blur)
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gpu_target_dst_style_anti_masked = gpu_target_dst*gpu_target_dstm_style_anti_blur
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gpu_target_src_anti_masked = gpu_target_src*gpu_target_srcm_anti_blur
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gpu_target_dst_anti_masked = gpu_target_dst*gpu_target_dstm_anti_blur
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gpu_pred_src_src_anti_masked = gpu_pred_src_src*gpu_target_srcm_anti_blur
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gpu_pred_dst_dst_anti_masked = gpu_pred_dst_dst*gpu_target_dstm_anti_blur
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gpu_target_src_anti_masked = gpu_target_src*(1.0-gpu_target_srcm_blur)
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gpu_target_src_masked_opt = gpu_target_src*gpu_target_srcm_blur if masked_training else gpu_target_src
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gpu_target_dst_masked_opt = gpu_target_dst_masked if masked_training else gpu_target_dst
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gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
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gpu_pred_src_src_anti_masked = gpu_pred_src_src*(1.0-gpu_target_srcm_blur)
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gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
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gpu_psd_target_dst_style_masked = gpu_pred_src_dst*gpu_target_dstm_style_blur
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gpu_psd_target_dst_style_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_style_blur)
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gpu_psd_target_dst_style_anti_masked = gpu_pred_src_dst*gpu_target_dstm_style_anti_blur
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if resolution < 256:
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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])
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@ -483,6 +483,9 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_G_loss = gpu_src_loss + gpu_dst_loss
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if learn_dst_bg and masked_training and 'liae' in archi_type:
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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] )
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def DLoss(labels,logits):
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return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits), axis=[1,2,3])
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@ -526,14 +529,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + \
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DLoss(gpu_pred_src_src_d2_ones, gpu_pred_src_src_d2))
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if masked_training:
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# Minimal src-src-bg rec with total_variation_mse to suppress random bright dots from gan
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gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_src_src)
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gpu_G_loss += 0.02*tf.reduce_mean(tf.square(gpu_pred_src_src_anti_masked-gpu_target_src_anti_masked),axis=[1,2,3] )
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gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ]
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gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.src_dst_trainable_weights )]
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# Average losses and gradients, and create optimizer update ops
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@ -560,7 +563,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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# Initializing training and view functions
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def src_dst_train(warped_src, target_src, target_srcm, target_srcm_em, \
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warped_dst, target_dst, target_dstm, target_dstm_em, ):
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s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
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s, d = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
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feed_dict={self.warped_src :warped_src,
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self.target_src :target_src,
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self.target_srcm:target_srcm,
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@ -569,7 +572,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.target_dst :target_dst,
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self.target_dstm:target_dstm,
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self.target_dstm_em:target_dstm_em,
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})
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})[:2]
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return s, d
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self.src_dst_train = src_dst_train
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@ -674,7 +677,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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sample_process_options=SampleProcessor.Options(random_flip=random_dst_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'denoise_filter' : dst_denoise, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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@ -762,13 +764,13 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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bs = self.get_batch_size()
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( (warped_src, target_src, target_srcm, target_srcm_em), \
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(warped_dst, target_dst, target_dst_train, target_dstm, target_dstm_em) ) = self.generate_next_samples()
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(warped_dst, target_dst, target_dstm, target_dstm_em) ) = self.generate_next_samples()
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src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst_train, target_dstm, target_dstm_em)
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src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
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for i in range(bs):
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self.last_src_samples_loss.append ( (src_loss[i], target_src[i], target_srcm[i], target_srcm_em[i],) )
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self.last_dst_samples_loss.append ( (dst_loss[i], target_dst[i], target_dst_train[i], target_dstm[i], target_dstm_em[i],) )
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self.last_dst_samples_loss.append ( (dst_loss[i], target_dst[i], target_dstm[i], target_dstm_em[i],) )
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if len(self.last_src_samples_loss) >= bs*16:
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src_samples_loss = sorted(self.last_src_samples_loss, key=operator.itemgetter(0), reverse=True)
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@ -779,11 +781,10 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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target_srcm_em = np.stack( [ x[3] for x in src_samples_loss[:bs] ] )
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target_dst = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )
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target_dst_train = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
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target_dstm = np.stack( [ x[3] for x in dst_samples_loss[:bs] ] )
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target_dstm_em = np.stack( [ x[4] for x in dst_samples_loss[:bs] ] )
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target_dstm = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
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target_dstm_em = np.stack( [ x[3] for x in dst_samples_loss[:bs] ] )
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src_loss, dst_loss = self.src_dst_train (target_src, target_src, target_srcm, target_srcm_em, target_dst, target_dst_train, target_dstm, target_dstm_em)
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src_loss, dst_loss = self.src_dst_train (target_src, target_src, target_srcm, target_srcm_em, target_dst, target_dst, target_dstm, target_dstm_em)
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self.last_src_samples_loss = []
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self.last_dst_samples_loss = []
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@ -791,14 +792,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.D_train (warped_src, warped_dst)
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if self.gan_power != 0:
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self.D_src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst_train, target_dstm, target_dstm_em)
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self.D_src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
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return ( ('src_loss', np.mean(src_loss) ), ('dst_loss', np.mean(dst_loss) ), )
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#override
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def onGetPreview(self, samples, for_history=False):
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( (warped_src, target_src, target_srcm, target_srcm_em),
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(warped_dst, target_dst, target_dst_train, target_dstm, target_dstm_em) ) = samples
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(warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
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S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
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DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
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