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SAEHD: random scale increased to -0.15+0.15. Improved lr_dropout capability to reach lower value of the loss.
When random_warp is off, inter_AB network is no longer trained to keep the face more src-like. added option Random hue/saturation/light intensity applied to the src face set only at the input of the neural network. Stabilizes color perturbations during face swapping. Reduces the quality of the color transfer by selecting the closest one in the src faceset. Thus the src faceset must be diverse enough. Typical fine value is 0.05. AMP: random scale increased to -0.15+0.15. Improved lr_dropout capability to reach lower value of the loss.
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iperov
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f71a9e6bbd
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@ -292,20 +292,21 @@ class AMPModel(ModelBase):
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if self.is_training:
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# Initialize optimizers
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clipnorm = 1.0 if self.options['clipgrad'] else 0.0
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lr_dropout = 0.3 if self.options['lr_dropout'] in ['y','cpu'] else 1.0
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if self.options['lr_dropout'] in ['y','cpu'] and not self.pretrain:
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lr_cos = 500
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lr_dropout = 0.3
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else:
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lr_cos = 0
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lr_dropout = 1.0
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self.G_weights = self.encoder.get_weights() + self.decoder.get_weights()
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#if random_warp:
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# self.G_weights += self.inter_src.get_weights() + self.inter_dst.get_weights()
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self.src_dst_opt = nn.AdaBelief(lr=5e-5, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
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self.src_dst_opt = nn.AdaBelief(lr=5e-5, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='src_dst_opt')
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self.src_dst_opt.initialize_variables (self.G_weights, vars_on_cpu=optimizer_vars_on_cpu)
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self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
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if gan_power != 0:
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self.GAN = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="GAN")
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self.GAN_opt = nn.AdaBelief(lr=5e-5, lr_dropout=lr_dropout, clipnorm=clipnorm, name='GAN_opt')
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self.GAN_opt = nn.AdaBelief(lr=5e-5, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='GAN_opt')
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self.GAN_opt.initialize_variables ( self.GAN.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
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self.model_filename_list += [ [self.GAN, 'GAN.npy'],
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[self.GAN_opt, 'GAN_opt.npy'] ]
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@ -391,15 +392,15 @@ class AMPModel(ModelBase):
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if blur_out_mask:
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sigma = resolution / 128
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x = nn.gaussian_blur(gpu_target_src*gpu_target_srcm_anti, sigma)
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y = 1-nn.gaussian_blur(gpu_target_srcm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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y = 1-nn.gaussian_blur(gpu_target_srcm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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gpu_target_src = gpu_target_src*gpu_target_srcm + (x/y)*gpu_target_srcm_anti
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x = nn.gaussian_blur(gpu_target_dst*gpu_target_dstm_anti, sigma)
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y = 1-nn.gaussian_blur(gpu_target_dstm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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y = 1-nn.gaussian_blur(gpu_target_dstm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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gpu_target_dst = gpu_target_dst*gpu_target_dstm + (x/y)*gpu_target_dstm_anti
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gpu_target_src_masked = gpu_target_src*gpu_target_srcm_blur
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@ -561,7 +562,7 @@ class AMPModel(ModelBase):
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self.set_training_data_generators ([
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SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(scale_range=[-0.125, 0.125], random_flip=self.random_src_flip),
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sample_process_options=SampleProcessor.Options(scale_range=[-0.15, 0.15], random_flip=self.random_src_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':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, 'ct_mode': ct_mode, 'face_type':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':face_type, 'data_format':nn.data_format, 'resolution': resolution},
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@ -571,7 +572,7 @@ class AMPModel(ModelBase):
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generators_count=src_generators_count ),
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SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(scale_range=[-0.125, 0.125], random_flip=self.random_dst_flip),
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sample_process_options=SampleProcessor.Options(scale_range=[-0.15, 0.15], random_flip=self.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':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':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':face_type, 'data_format':nn.data_format, 'resolution': resolution},
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@ -53,6 +53,7 @@ class SAEHDModel(ModelBase):
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default_lr_dropout = self.options['lr_dropout'] = lr_dropout
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default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
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default_random_hsv_power = self.options['random_hsv_power'] = self.load_or_def_option('random_hsv_power', 0.0)
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default_true_face_power = self.options['true_face_power'] = self.load_or_def_option('true_face_power', 0.0)
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default_face_style_power = self.options['face_style_power'] = self.load_or_def_option('face_style_power', 0.0)
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default_bg_style_power = self.options['bg_style_power'] = self.load_or_def_option('bg_style_power', 0.0)
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@ -152,6 +153,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.options['random_warp'] = io.input_bool ("Enable random warp of samples", default_random_warp, help_message="Random warp is required to generalize facial expressions of both faces. When the face is trained enough, you can disable it to get extra sharpness and reduce subpixel shake for less amount of iterations.")
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self.options['random_hsv_power'] = np.clip ( io.input_number ("Random hue/saturation/light intensity", default_random_hsv_power, add_info="0.0 .. 0.3", help_message="Random hue/saturation/light intensity applied to the src face set only at the input of the neural network. Stabilizes color perturbations during face swapping. Reduces the quality of the color transfer by selecting the closest one in the src faceset. Thus the src faceset must be diverse enough. Typical fine value is 0.05"), 0.0, 0.3 )
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self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 5.0", help_message="Forces the neural network to learn small details of the face. Enable it only when the face is trained enough with lr_dropout(on) and random_warp(off), and don't disable. The higher the value, the higher the chances of artifacts. Typical fine value is 0.1"), 0.0, 5.0 )
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if self.options['gan_power'] != 0.0:
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@ -228,12 +231,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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random_warp = False if self.pretrain else self.options['random_warp']
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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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random_hsv_power = self.options['random_hsv_power'] if not self.pretrain else 0.0
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blur_out_mask = self.options['blur_out_mask']
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if self.pretrain:
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self.options_show_override['gan_power'] = 0.0
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self.options_show_override['random_warp'] = False
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self.options_show_override['lr_dropout'] = 'n'
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self.options_show_override['random_warp'] = False
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self.options_show_override['gan_power'] = 0.0
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self.options_show_override['random_hsv_power'] = 0.0
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self.options_show_override['face_style_power'] = 0.0
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self.options_show_override['bg_style_power'] = 0.0
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self.options_show_override['uniform_yaw'] = True
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@ -313,26 +318,36 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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# Initialize optimizers
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lr=5e-5
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lr_dropout = 0.3 if self.options['lr_dropout'] in ['y','cpu'] and not self.pretrain else 1.0
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if self.options['lr_dropout'] in ['y','cpu'] and not self.pretrain:
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lr_cos = 500
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lr_dropout = 0.3
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else:
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lr_cos = 0
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lr_dropout = 1.0
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OptimizerClass = nn.AdaBelief if adabelief else nn.RMSprop
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clipnorm = 1.0 if self.options['clipgrad'] else 0.0
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if 'df' in archi_type:
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self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
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self.src_dst_saveable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
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self.src_dst_trainable_weights = self.src_dst_saveable_weights
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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_saveable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
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if random_warp:
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self.src_dst_trainable_weights = self.src_dst_saveable_weights
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else:
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self.src_dst_trainable_weights = self.encoder.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.src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='src_dst_opt')
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self.src_dst_opt.initialize_variables (self.src_dst_saveable_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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if self.options['true_face_power'] != 0:
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self.D_code_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_code_opt')
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self.D_code_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='D_code_opt')
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self.D_code_opt.initialize_variables ( self.code_discriminator.get_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.D_code_opt, 'D_code_opt.npy') ]
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if gan_power != 0:
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self.D_src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='GAN_opt')
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self.D_src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='GAN_opt')
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self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')#+self.D_src_x2.get_weights()
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self.model_filename_list += [ (self.D_src_dst_opt, 'GAN_opt.npy') ]
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@ -375,18 +390,18 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if blur_out_mask:
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sigma = resolution / 128
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x = nn.gaussian_blur(gpu_target_src*gpu_target_srcm_anti, sigma)
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y = 1-nn.gaussian_blur(gpu_target_srcm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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y = 1-nn.gaussian_blur(gpu_target_srcm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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gpu_target_src = gpu_target_src*gpu_target_srcm + (x/y)*gpu_target_srcm_anti
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x = nn.gaussian_blur(gpu_target_dst*gpu_target_dstm_anti, sigma)
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y = 1-nn.gaussian_blur(gpu_target_dstm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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y = 1-nn.gaussian_blur(gpu_target_dstm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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gpu_target_dst = gpu_target_dst*gpu_target_dstm + (x/y)*gpu_target_dstm_anti
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# process model tensors
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if 'df' in archi_type:
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gpu_src_code = self.inter(self.encoder(gpu_warped_src))
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@ -395,7 +410,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
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gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
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gpu_pred_src_dst_no_code_grad, _ = self.decoder_src(tf.stop_gradient(gpu_dst_code))
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elif 'liae' in archi_type:
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gpu_src_code = self.encoder (gpu_warped_src)
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gpu_src_inter_AB_code = self.inter_AB (gpu_src_code)
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@ -425,11 +440,11 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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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_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
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gpu_style_mask_blur = nn.gaussian_blur(gpu_pred_src_dstm*gpu_pred_dst_dstm, max(1, resolution // 32) )
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gpu_style_mask_blur = tf.stop_gradient(tf.clip_by_value(gpu_target_srcm_blur, 0, 1.0))
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gpu_style_mask_anti_blur = 1.0 - gpu_style_mask_blur
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gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
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gpu_target_src_anti_masked = gpu_target_src*gpu_target_srcm_anti_blur
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@ -460,7 +475,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if bg_style_power != 0 and not self.pretrain:
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gpu_target_dst_style_anti_masked = gpu_target_dst*gpu_style_mask_anti_blur
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gpu_psd_style_anti_masked = gpu_pred_src_dst*gpu_style_mask_anti_blur
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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])
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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] )
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@ -659,8 +674,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.set_training_data_generators ([
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SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(scale_range=[-0.125, 0.125], random_flip=random_src_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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sample_process_options=SampleProcessor.Options(scale_range=[-0.15, 0.15], random_flip=random_src_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'random_hsv_shift_amount' : random_hsv_power, '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, 'ct_mode': ct_mode, '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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@ -669,7 +684,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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generators_count=src_generators_count ),
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SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(scale_range=[-0.125, 0.125], random_flip=random_dst_flip),
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sample_process_options=SampleProcessor.Options(scale_range=[-0.15, 0.15], 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_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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