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sinofis 2020-10-14 15:59:15 +02:00
commit 7233d54324
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models/Model_SAEHD/Model.py
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@ -53,6 +53,8 @@ class SAEHDModel(ModelBase):
default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True) default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0) default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
default_gan_old = self.options['gan_old'] = self.load_or_def_option('gan_old', False)
default_true_face_power = self.options['true_face_power'] = self.load_or_def_option('true_face_power', 0.0) default_true_face_power = self.options['true_face_power'] = self.load_or_def_option('true_face_power', 0.0)
default_face_style_power = self.options['face_style_power'] = self.load_or_def_option('face_style_power', 0.0) default_face_style_power = self.options['face_style_power'] = self.load_or_def_option('face_style_power', 0.0)
default_bg_style_power = self.options['bg_style_power'] = self.load_or_def_option('bg_style_power', 0.0) default_bg_style_power = self.options['bg_style_power'] = self.load_or_def_option('bg_style_power', 0.0)
@ -144,6 +146,9 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 10.0", help_message="Train the network in Generative Adversarial manner. Forces the neural network to learn small details of the face. Enable it only when the face is trained enough and don't disable. Typical value is 0.1"), 0.0, 10.0 ) self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 10.0", help_message="Train the network in Generative Adversarial manner. Forces the neural network to learn small details of the face. Enable it only when the face is trained enough and don't disable. Typical value is 0.1"), 0.0, 10.0 )
if (self.options['gan_power'] != 0):
self.options['gan_old'] = io.input_bool ("Use old GAN version", default_gan_old, help_message="Use older version of GAN." )
if 'df' in self.options['archi']: if 'df' in self.options['archi']:
self.options['true_face_power'] = np.clip ( io.input_number ("'True face' power.", default_true_face_power, add_info="0.0000 .. 1.0", help_message="Experimental option. Discriminates result face to be more like src face. Higher value - stronger discrimination. Typical value is 0.01 . Comparison - https://i.imgur.com/czScS9q.png"), 0.0, 1.0 ) self.options['true_face_power'] = np.clip ( io.input_number ("'True face' power.", default_true_face_power, add_info="0.0000 .. 1.0", help_message="Experimental option. Discriminates result face to be more like src face. Higher value - stronger discrimination. Typical value is 0.01 . Comparison - https://i.imgur.com/czScS9q.png"), 0.0, 1.0 )
else: else:
@ -196,6 +201,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
self.set_iter(0) self.set_iter(0)
self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power'] self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
self.gan_old = gan_old = self.options['gan_old']
random_warp = False if self.pretrain else self.options['random_warp'] random_warp = False if self.pretrain else self.options['random_warp']
if self.pretrain: if self.pretrain:
@ -274,8 +281,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
if self.is_training: if self.is_training:
if gan_power != 0: if gan_power != 0:
self.D_src = nn.UNetPatchDiscriminator(patch_size=resolution//16, in_ch=input_ch, name="D_src") if gan_old:
self.model_filename_list += [ [self.D_src, 'D_src_v2.npy'] ] self.D_src = nn.PatchDiscriminator(patch_size=resolution//16, in_ch=input_ch, name="D_src")
self.D_src_x2 = nn.PatchDiscriminator(patch_size=resolution//32, in_ch=input_ch, name="D_src_x2")
self.model_filename_list += [ [self.D_src, 'D_src.npy'] ]
self.model_filename_list += [ [self.D_src_x2, 'D_src_x2.npy'] ]
elif:
self.D_src = nn.UNetPatchDiscriminator(patch_size=resolution//16, in_ch=input_ch, name="D_src")
self.model_filename_list += [ [self.D_src, 'D_src_v2.npy'] ]
# Initialize optimizers # Initialize optimizers
lr=5e-5 lr=5e-5
@ -298,8 +311,13 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
if gan_power != 0: if gan_power != 0:
self.D_src_dst_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_src_dst_opt') self.D_src_dst_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_src_dst_opt')
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()
self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_v2_opt.npy') ] if gan_old:
self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights()+self.D_src_x2.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_dst_opt.npy') ]
elif:
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()
self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_v2_opt.npy') ]
if self.is_training: if self.is_training:
# Adjust batch size for multiple GPU # Adjust batch size for multiple GPU
@ -465,30 +483,55 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
gpu_D_code_loss_gvs += [ nn.gradients (gpu_D_code_loss, self.code_discriminator.get_weights() ) ] gpu_D_code_loss_gvs += [ nn.gradients (gpu_D_code_loss, self.code_discriminator.get_weights() ) ]
if gan_power != 0: if gan_power != 0:
gpu_pred_src_src_d, \ if gan_old:
gpu_pred_src_src_d2 = self.D_src(gpu_pred_src_src_masked_opt) gpu_pred_src_src_d = self.D_src(gpu_pred_src_src_masked_opt)
gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d) gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d) gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d)
gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d) gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d) gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d)
gpu_target_src_d = self.D_src(gpu_target_src_masked_opt)
gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d)
gpu_pred_src_src_d2_ones = tf.ones_like (gpu_pred_src_src_d2) gpu_pred_src_src_x2_d = self.D_src_x2(gpu_pred_src_src_masked_opt) gpu_pred_src_src_d2_ones = tf.ones_like (gpu_pred_src_src_d2)
gpu_pred_src_src_d2_zeros = tf.zeros_like(gpu_pred_src_src_d2) gpu_pred_src_src_x2_d_ones = tf.ones_like (gpu_pred_src_src_x2_d) gpu_pred_src_src_d2_zeros = tf.zeros_like(gpu_pred_src_src_d2)
gpu_pred_src_src_x2_d_zeros = tf.zeros_like(gpu_pred_src_src_x2_d)
gpu_target_src_x2_d = self.D_src_x2(gpu_target_src_masked_opt) gpu_target_src_d, \
gpu_target_src_x2_d_ones = tf.ones_like(gpu_target_src_x2_d)
gpu_target_src_d, \ gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt) DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \
(DLoss(gpu_target_src_x2_d_ones , gpu_target_src_x2_d) + \
DLoss(gpu_pred_src_src_x2_d_zeros, gpu_pred_src_src_x2_d) ) * 0.5
gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d) gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss,
gpu_target_src_d2_ones = tf.ones_like(gpu_target_src_d2) self.D_src.get_weights()+self.D_src_x2.get_weights() ) ]
gpu_G_loss += 0.5*gan_power*( DLoss(gpu_pred_src_src_d_ones,
gpu_pred_src_src_d) + DLoss(gpu_pred_src_src_x2_d_ones, gpu_pred_src_src_x2_d))
gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \ elif:
DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \ gpu_pred_src_src_d, \
(DLoss(gpu_target_src_d2_ones , gpu_target_src_d2) + \ gpu_pred_src_src_d2 = self.D_src(gpu_pred_src_src_masked_opt)
DLoss(gpu_pred_src_src_d2_zeros , gpu_pred_src_src_d2) ) * 0.5
gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.D_src.get_weights() ) ]#+self.D_src_x2.get_weights() gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d)
gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d)
gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + \ gpu_pred_src_src_d2_ones = tf.ones_like (gpu_pred_src_src_d2)
DLoss(gpu_pred_src_src_d2_ones, gpu_pred_src_src_d2)) gpu_pred_src_src_d2_zeros = tf.zeros_like(gpu_pred_src_src_d2)
gpu_target_src_d, \
gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d)
gpu_target_src_d2_ones = tf.ones_like(gpu_target_src_d2)
gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \
(DLoss(gpu_target_src_d2_ones , gpu_target_src_d2) + \
DLoss(gpu_pred_src_src_d2_zeros , gpu_pred_src_src_d2) ) * 0.5
gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.D_src.get_weights() ) ]#+self.D_src_x2.get_weights()
gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + \
DLoss(gpu_pred_src_src_d2_ones, gpu_pred_src_src_d2))
gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ] gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ]