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added most SAEHD option to amp

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Jan 2021-11-23 19:54:43 +01:00
commit af712cc69d
1 changed files with 128 additions and 53 deletions
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181
models/Model_AMP/Model.py
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@ -18,7 +18,7 @@ class AMPModel(ModelBase):
default_retraining_samples = self.options['retraining_samples'] = self.load_or_def_option('retraining_samples', False)
# default_usefp16 = self.options['use_fp16'] = self.load_or_def_option('use_fp16', False)
default_resolution = self.options['resolution'] = self.load_or_def_option('resolution', 224)
default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'wf')
default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'f')
default_models_opt_on_gpu = self.options['models_opt_on_gpu'] = self.load_or_def_option('models_opt_on_gpu', True)
default_ae_dims = self.options['ae_dims'] = self.load_or_def_option('ae_dims', 256)
@ -27,12 +27,13 @@ class AMPModel(ModelBase):
default_e_dims = self.options['e_dims'] = self.load_or_def_option('e_dims', 64)
default_d_dims = self.options['d_dims'] = self.options.get('d_dims', None)
default_d_mask_dims = self.options['d_mask_dims'] = self.options.get('d_mask_dims', None)
default_morph_factor = self.options['morph_factor'] = self.options.get('morph_factor', 0.5)
default_morph_factor = self.options['morph_factor'] = self.load_or_def_option('morph_factor', 0.5)
default_masked_training = self.options['masked_training'] = self.load_or_def_option('masked_training', True)
default_eyes_mouth_prio = self.options['eyes_mouth_prio'] = self.load_or_def_option('eyes_mouth_prio', False)
default_uniform_yaw = self.options['uniform_yaw'] = self.load_or_def_option('uniform_yaw', False)
# Uncomment it just if you want to impelement other loss functions
#default_loss_function = self.options['loss_function'] = self.load_or_def_option('loss_function', 'SSIM')
default_loss_function = self.options['loss_function'] = self.load_or_def_option('loss_function', 'SSIM')
default_blur_out_mask = self.options['blur_out_mask'] = self.load_or_def_option('blur_out_mask', False)
@ -41,6 +42,7 @@ class AMPModel(ModelBase):
default_lr_dropout = self.options['lr_dropout'] = self.load_or_def_option('lr_dropout', 'n')
default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
default_random_hsv_power = self.options['random_hsv_power'] = self.load_or_def_option('random_hsv_power', 0.0)
default_random_downsample = self.options['random_downsample'] = self.load_or_def_option('random_downsample', False)
default_random_noise = self.options['random_noise'] = self.load_or_def_option('random_noise', False)
default_random_blur = self.options['random_blur'] = self.load_or_def_option('random_blur', False)
@ -69,7 +71,7 @@ class AMPModel(ModelBase):
resolution = io.input_int("Resolution", default_resolution, add_info="64-640", help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 32 .")
resolution = np.clip ( (resolution // 32) * 32, 64, 640)
self.options['resolution'] = resolution
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['f','wf','head'], help_message="whole face / head").lower()
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head', 'custom'], help_message="Half / mid face / full face / whole face / head / custom. Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face. 'Whole face' covers full area of face include forehead. 'head' covers full head, but requires XSeg for src and dst faceset.").lower()
default_d_dims = self.options['d_dims'] = self.load_or_def_option('d_dims', 64)
@ -91,21 +93,29 @@ class AMPModel(ModelBase):
d_mask_dims = np.clip ( io.input_int("Decoder mask dimensions", default_d_mask_dims, add_info="16-256", help_message="Typical mask dimensions = decoder dimensions / 3. If you manually cut out obstacles from the dst mask, you can increase this parameter to achieve better quality." ), 16, 256 )
self.options['d_mask_dims'] = d_mask_dims + d_mask_dims % 2
if self.is_first_run() or ask_override:
morph_factor = np.clip ( io.input_number ("Morph factor.", default_morph_factor, add_info="0.1 .. 0.5", help_message="Typical fine value is 0.5"), 0.1, 0.5 )
self.options['morph_factor'] = morph_factor
if self.options['face_type'] == 'wf' or self.options['face_type'] == 'head':
self.options['masked_training'] = io.input_bool ("Masked training", default_masked_training, help_message="This option is available only for 'whole_face' or 'head' type. Masked training clips training area to full_face mask or XSeg mask, thus network will train the faces properly.")
if self.is_first_run() or ask_override:
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.')
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.')
if self.options['masked_training']:
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.')
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.')
self.options['loss_function'] = io.input_str(f"Loss function", default_loss_function, ['SSIM', 'MS-SSIM', 'MS-SSIM+L1'], help_message="Change loss function used for image quality assessment.")
self.options['lr_dropout'] = io.input_str (f"Use learning rate dropout", default_lr_dropout, ['n','y','cpu'], help_message="When the face is trained enough, you can enable this option to get extra sharpness and reduce subpixel shake for less amount of iterations. Enabled it before `disable random warp` and before GAN. \nn - disabled.\ny - enabled\ncpu - enabled on CPU. This allows not to use extra VRAM, sacrificing 20% time of iteration.")
default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
default_gan_version = self.options['gan_version'] = self.load_or_def_option('gan_version', 2)
default_gan_patch_size = self.options['gan_patch_size'] = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8)
default_gan_dims = self.options['gan_dims'] = self.load_or_def_option('gan_dims', 16)
default_gan_smoothing = self.options['gan_smoothing'] = self.load_or_def_option('gan_smoothing', 0.1)
default_gan_noise = self.options['gan_noise'] = self.load_or_def_option('gan_noise', 0.0)
if self.is_first_run() or ask_override:
self.options['models_opt_on_gpu'] = io.input_bool ("Place models and optimizer on GPU", default_models_opt_on_gpu, help_message="When you train on one GPU, by default model and optimizer weights are placed on GPU to accelerate the process. You can place they on CPU to free up extra VRAM, thus set bigger dimensions.")
@ -116,15 +126,25 @@ class AMPModel(ModelBase):
self.options['random_noise'] = io.input_bool("Enable random noise added to samples", default_random_noise, help_message="")
self.options['random_blur'] = io.input_bool("Enable random blur of samples", default_random_blur, help_message="")
self.options['random_jpeg'] = io.input_bool("Enable random jpeg compression of samples", default_random_jpeg, help_message="")
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 )
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 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 )
if self.options['gan_power'] != 0.0:
gan_patch_size = np.clip ( io.input_int("GAN patch size", default_gan_patch_size, add_info="3-640", help_message="The higher patch size, the higher the quality, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is resolution / 8." ), 3, 640 )
self.options['gan_patch_size'] = gan_patch_size
self.options['gan_version'] = np.clip (io.input_int("GAN version", default_gan_version, add_info="2 or 3", help_message="Choose GAN version (v2: 7/16/2020, v3: 1/3/2021):"), 2, 3)
if self.options['gan_version'] == 3:
gan_patch_size = np.clip ( io.input_int("GAN patch size", default_gan_patch_size, add_info="3-640", help_message="The higher patch size, the higher the quality, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is resolution / 8." ), 3, 640 )
self.options['gan_patch_size'] = gan_patch_size
gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="4-64", help_message="The dimensions of the GAN network. The higher dimensions, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is 16." ), 4, 64 )
self.options['gan_dims'] = gan_dims
self.options['gan_smoothing'] = np.clip ( io.input_number("GAN label smoothing", default_gan_smoothing, add_info="0 - 0.5", help_message="Uses soft labels with values slightly off from 0/1 for GAN, has a regularizing effect"), 0, 0.5)
self.options['gan_noise'] = np.clip ( io.input_number("GAN noisy labels", default_gan_noise, add_info="0 - 0.5", help_message="Marks some images with the wrong label, helps prevent collapse"), 0, 0.5)
gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="4-512", help_message="The dimensions of the GAN network. The higher dimensions, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is 16." ), 4, 512 )
self.options['gan_dims'] = gan_dims
#self.options['background_power'] = np.clip ( io.input_number("Background power", default_background_power, add_info="0.0..1.0", help_message="Learn the area outside of the mask. Helps smooth out area near the mask boundaries. Can be used at any time"), 0.0, 1.0 )
@ -151,16 +171,20 @@ class AMPModel(ModelBase):
inter_res = self.inter_res = resolution // 32
d_dims = self.options['d_dims']
d_mask_dims = self.options['d_mask_dims']
face_type = self.face_type = {'f' : FaceType.FULL,
'wf' : FaceType.WHOLE_FACE,
'head' : FaceType.HEAD}[ self.options['face_type'] ]
self.face_type = {'h' : FaceType.HALF,
'mf' : FaceType.MID_FULL,
'f' : FaceType.FULL,
'wf' : FaceType.WHOLE_FACE,
'custom' : FaceType.CUSTOM,
'head' : FaceType.HEAD}[ self.options['face_type'] ]
morph_factor = self.options['morph_factor']
gan_power = self.gan_power = self.options['gan_power']
random_warp = self.options['random_warp']
random_hsv_power = self.options['random_hsv_power']
eyes_mouth_prio = self.options['eyes_mouth_prio']
blur_out_mask = self.options['blur_out_mask']
masked_training = self.options['masked_training']
blur_out_mask = self.options['blur_out_mask'] if masked_training else False
ct_mode = self.options['ct_mode']
if ct_mode == 'none':
@ -329,6 +353,14 @@ class AMPModel(ModelBase):
[self.decoder , 'decoder.npy'] ]
if self.is_training:
if gan_power != 0:
if self.options['gan_version'] == 2:
self.GAN = nn.UNetPatchDiscriminatorV2(patch_size=resolution//16, in_ch=input_ch, name="D_src", use_fp16=self.options['use_fp16'])
self.model_filename_list += [ [self.GAN, 'D_src_v2.npy'] ]
else:
self.GAN = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], use_fp16=self.options['use_fp16'], name="D_src")
self.model_filename_list += [ [self.GAN, 'GAN.npy'] ]
# Initialize optimizers
clipnorm = 1.0 if self.options['clipgrad'] else 0.0
if self.options['lr_dropout'] in ['y','cpu']:
@ -340,17 +372,19 @@ class AMPModel(ModelBase):
self.G_weights = self.encoder.get_weights() + self.decoder.get_weights()
OptimizerClass = nn.AdaBelief if adabelief else nn.RMSprop
self.src_dst_opt = OptimizerClass(lr=5e-5, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='src_dst_opt')
self.src_dst_opt = OptimizerClass(lr=5e-5, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
self.src_dst_opt.initialize_variables (self.G_weights, vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
if gan_power != 0:
self.GAN = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], use_fp16=use_fp16, name="GAN")
self.GAN_opt = OptimizerClass(lr=5e-5, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='GAN_opt')
self.GAN_opt.initialize_variables ( self.GAN.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [ [self.GAN, 'GAN.npy'],
[self.GAN_opt, 'GAN_opt.npy'] ]
if self.options['gan_version'] == 2:
self.GAN_opt = OptimizerClass(lr=5e-5, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='D_src_dst_opt')
self.GAN_opt.initialize_variables ( self.GAN.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.GAN_opt, 'D_src_v2_opt.npy') ]
else:
self.GAN_opt = OptimizerClass(lr=5e-5, lr_dropout=lr_dropout, lr_cos=lr_cos, clipnorm=clipnorm, name='GAN_opt')
self.GAN_opt.initialize_variables ( self.GAN.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.GAN_opt, 'GAN_opt.npy') ]
if self.is_training:
# Adjust batch size for multiple GPU
@ -371,6 +405,9 @@ class AMPModel(ModelBase):
gpu_G_loss_gradients = []
gpu_GAN_loss_gradients = []
def DLoss(labels,logits):
return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits), axis=[1,2,3])
def DLossOnes(logits):
return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(logits), logits=logits), axis=[1,2,3])
@ -444,26 +481,39 @@ class AMPModel(ModelBase):
y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
gpu_target_dst = gpu_target_dst*gpu_target_dstm + (x/y)*gpu_target_dstm_anti
gpu_target_src_masked = gpu_target_src*gpu_target_srcm_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_target_src_masked = gpu_target_src*gpu_target_srcm_blur if masked_training else gpu_target_src
gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur if masked_training else gpu_target_dst
gpu_target_src_anti_masked = gpu_target_src*gpu_target_srcm_anti_blur if masked_training else gpu_pred_src_src
gpu_target_dst_anti_masked = gpu_target_dst*gpu_target_dstm_anti_blur if masked_training else gpu_pred_dst_dst
gpu_pred_src_src_masked = gpu_pred_src_src*gpu_target_srcm_blur
gpu_pred_dst_dst_masked = gpu_pred_dst_dst*gpu_target_dstm_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
# Structural loss
gpu_src_loss = tf.reduce_mean (5*nn.dssim(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += tf.reduce_mean (5*nn.dssim(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
gpu_dst_loss = tf.reduce_mean (5*nn.dssim(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
gpu_dst_loss += tf.reduce_mean (5*nn.dssim(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
# Pixel loss
gpu_src_loss += tf.reduce_mean (10*tf.square(gpu_target_src_masked-gpu_pred_src_src_masked), axis=[1,2,3])
gpu_dst_loss += tf.reduce_mean (10*tf.square(gpu_target_dst_masked-gpu_pred_dst_dst_masked), axis=[1,2,3])
if self.options['loss_function'] == 'MS-SSIM':
gpu_src_loss = 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution)(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0)
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked - gpu_pred_src_src_masked ), axis=[1,2,3])
elif self.options['loss_function'] == 'MS-SSIM+L1':
gpu_src_loss = 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0)
else:
gpu_src_loss = tf.reduce_mean (5*nn.dssim(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += tf.reduce_mean (5*nn.dssim(gpu_target_src_masked, gpu_pred_src_src_masked, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
# Pixel loss
gpu_src_loss += tf.reduce_mean (10*tf.square(gpu_target_src_masked-gpu_pred_src_src_masked), axis=[1,2,3])
if self.options['loss_function'] == 'MS-SSIM':
gpu_dst_loss = 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution)(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0)
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_dst_masked - gpu_pred_dst_dst_masked ), axis=[1,2,3])
elif self.options['loss_function'] == 'MS-SSIM+L1':
gpu_dst_loss = 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0)
else:
gpu_dst_loss = tf.reduce_mean (5*nn.dssim(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
gpu_dst_loss += tf.reduce_mean (5*nn.dssim(gpu_target_dst_masked, gpu_pred_dst_dst_masked, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
# Pixel loss
gpu_dst_loss += tf.reduce_mean (10*tf.square(gpu_target_dst_masked-gpu_pred_dst_dst_masked), axis=[1,2,3])
# Eyes+mouth prio loss
if eyes_mouth_prio:
gpu_src_loss += tf.reduce_mean (300*tf.abs (gpu_target_src*gpu_target_srcm_em-gpu_pred_src_src*gpu_target_srcm_em), axis=[1,2,3])
@ -482,26 +532,50 @@ class AMPModel(ModelBase):
if gan_power != 0:
gpu_pred_src_src_d, gpu_pred_src_src_d2 = self.GAN(gpu_pred_src_src_masked)
gpu_pred_dst_dst_d, gpu_pred_dst_dst_d2 = self.GAN(gpu_pred_dst_dst_masked)
gpu_target_src_d, gpu_target_src_d2 = self.GAN(gpu_target_src_masked)
gpu_target_dst_d, gpu_target_dst_d2 = self.GAN(gpu_target_dst_masked)
gpu_GAN_loss = (DLossOnes (gpu_target_src_d) + DLossOnes (gpu_target_src_d2) + \
DLossZeros(gpu_pred_src_src_d) + DLossZeros(gpu_pred_src_src_d2) + \
DLossOnes (gpu_target_dst_d) + DLossOnes (gpu_target_dst_d2) + \
DLossZeros(gpu_pred_dst_dst_d) + DLossZeros(gpu_pred_dst_dst_d2)
) * (1.0 / 8)
def get_smooth_noisy_labels(label, tensor, smoothing=0.1, noise=0.05):
num_labels = self.batch_size
for d in tensor.get_shape().as_list()[1:]:
num_labels *= d
probs = tf.math.log([[noise, 1-noise]]) if label == 1 else tf.math.log([[1-noise, noise]])
x = tf.random.categorical(probs, num_labels)
x = tf.cast(x, tf.float32)
x = tf.math.scalar_mul(1-smoothing, x)
# x = x + (smoothing/num_labels)
x = tf.reshape(x, (self.batch_size,) + tuple(tensor.get_shape().as_list()[1:]))
return x
smoothing = self.options['gan_smoothing']
noise = self.options['gan_noise']
gpu_pred_src_src_d_ones = tf.ones_like(gpu_pred_src_src_d)
gpu_pred_src_src_d2_ones = tf.ones_like(gpu_pred_src_src_d2)
gpu_pred_src_src_d_smooth_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d, smoothing=smoothing, noise=noise)
gpu_pred_src_src_d2_smooth_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d2, smoothing=smoothing, noise=noise)
gpu_target_src_d, gpu_target_src_d2 = self.GAN(gpu_target_src_masked)
gpu_target_src_d_smooth_ones = get_smooth_noisy_labels(1, gpu_target_src_d, smoothing=smoothing, noise=noise)
gpu_target_src_d2_smooth_ones = get_smooth_noisy_labels(1, gpu_target_src_d2, smoothing=smoothing, noise=noise)
gpu_GAN_loss = DLoss(gpu_target_src_d_smooth_ones, gpu_target_src_d) \
+ DLoss(gpu_pred_src_src_d_smooth_zeros, gpu_pred_src_src_d) \
+ DLoss(gpu_target_src_d2_smooth_ones, gpu_target_src_d2) \
+ DLoss(gpu_pred_src_src_d2_smooth_zeros, gpu_pred_src_src_d2)
gpu_GAN_loss_gradients += [ nn.gradients (gpu_GAN_loss, self.GAN.get_weights() ) ]
gpu_G_loss += (DLossOnes(gpu_pred_src_src_d) + DLossOnes(gpu_pred_src_src_d2) + \
DLossOnes(gpu_pred_dst_dst_d) + DLossOnes(gpu_pred_dst_dst_d2)
) * gan_power
# Minimal src-src-bg rec with total_variation_mse to suppress random bright dots from gan
gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_src_src)
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] )
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))
if masked_training:
# Minimal src-src-bg rec with total_variation_mse to suppress random bright dots from gan
gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_src_src)
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] )
gpu_G_loss_gradients += [ nn.gradients ( gpu_G_loss, self.G_weights ) ]
@ -615,6 +689,7 @@ class AMPModel(ModelBase):
'random_blur': self.options['random_blur'],
'random_jpeg': self.options['random_jpeg'],
'transform':True, 'channel_type' : channel_type, 'ct_mode': ct_mode,
'random_hsv_shift_amount' : random_hsv_power,
'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False,
'transform':True, 'channel_type' : channel_type, 'ct_mode': ct_mode,