def fanseg_extract_func(face_type, *args, **kwargs):
fanseg = self.fanseg_by_face_type.get(face_type, None)
if self.fanseg_by_face_type.get(face_type, None) is None:
cpu_only = len(nn.getCurrentDeviceConfig().devices) == 0
with nn.tf.device('/CPU:0' if cpu_only else '/GPU:0'):
fanseg = TernausNet("FANSeg", self.fanseg_input_size , FaceType.toString( face_type ), place_model_on_cpu=True )
self.fanseg_by_face_type[face_type] = fanseg
return fanseg.extract(*args, **kwargs)
def on_initialize_options(self):
device_config = nn.getCurrentDeviceConfig()
lowest_vram = 2
if len(device_config.devices) != 0:
lowest_vram = device_config.devices.get_worst_device().total_mem_gb
if lowest_vram >= 4:
suggest_batch_size = 8
else:
suggest_batch_size = 4
yn_str = {True: 'y', False: 'n'}
ask_override = self.ask_override()
resolution = default_resolution = self.options[
'resolution'] = self.load_or_def_option('resolution', 512)
if self.is_first_run() or ask_override:
self.ask_batch_size(suggest_batch_size)
resolution = io.input_int("Resolution",
default_resolution,
add_info="64-1024")
self.stage_max = stage_max = np.clip(
mathlib.get_power_of_two(resolution), 6, 10) - 2
self.options['resolution'] = resolution = 2**(stage_max + 2)
default_stage = self.load_or_def_option('stage', 0)
default_target_stage_iter = self.load_or_def_option(
'target_stage_iter', self.iter + 100000)
if (self.is_first_run() or ask_override):
new_stage = np.clip(
io.input_int("Stage", default_stage,
add_info=f"0-{stage_max}"), 0, stage_max)
if new_stage != default_stage:
self.options['start_stage_iter'] = self.iter
default_target_stage_iter = self.iter + 100000
self.options['stage'] = new_stage
else:
self.options['stage'] = default_stage
if self.options['stage'] == 0:
if 'start_stage_iter' in self.options:
self.options.pop('start_stage_iter')
if 'target_stage_iter' in self.options:
self.options.pop('target_stage_iter')
else:
if (self.is_first_run() or ask_override):
self.options['target_stage_iter'] = io.input_int(
"Target stage iteration", default_target_stage_iter)
else:
self.options['target_stage_iter'] = default_target_stage_iter
def on_initialize_options(self):
device_config = nn.getCurrentDeviceConfig()
yn_str = {True:'y',False:'n'}
#default_resolution = 256
ask_override = self.ask_override()
if self.is_first_run() or ask_override:
self.ask_autobackup_hour()
self.ask_target_iter()
self.ask_batch_size(24)
def depth_to_space(x, size):
if nn.data_format == "NHWC":
# match NCHW version in order to switch data_format without problems
b, h, w, c = x.shape.as_list()
oh, ow = h * size, w * size
oc = c // (size * size)
x = tf.reshape(x, (
-1,
h,
w,
size,
size,
oc,
))
x = tf.transpose(x, (0, 1, 3, 2, 4, 5))
x = tf.reshape(x, (
-1,
oh,
ow,
oc,
))
return x
else:
cfg = nn.getCurrentDeviceConfig()
if not cfg.cpu_only:
return tf.depth_to_space(x, size, data_format=nn.data_format)
b, c, h, w = x.shape.as_list()
oh, ow = h * size, w * size
oc = c // (size * size)
x = tf.reshape(x, (
-1,
size,
size,
oc,
h,
w,
))
x = tf.transpose(x, (0, 3, 4, 1, 5, 2))
x = tf.reshape(x, (-1, oc, oh, ow))
return x
def on_initialize_options(self):
device_config = nn.getCurrentDeviceConfig()
yn_str = {True: 'y', False: 'n'}
ask_override = self.ask_override()
if self.is_first_run() or ask_override:
self.ask_autobackup_hour()
self.ask_target_iter()
self.ask_batch_size(24)
default_lr_dropout = self.options[
'lr_dropout'] = self.load_or_def_option('lr_dropout', False)
if self.is_first_run() or ask_override:
self.options['lr_dropout'] = io.input_bool(
"Use learning rate dropout",
default_lr_dropout,
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."
)
def on_initialize_options(self):
device_config = nn.getCurrentDeviceConfig()
lowest_vram = 2
if len(device_config.devices) != 0:
lowest_vram = device_config.devices.get_worst_device().total_mem_gb
if lowest_vram >= 4:
suggest_batch_size = 8
else:
suggest_batch_size = 4
yn_str = {True:'y',False:'n'}
ask_override = self.ask_override()
if self.is_first_run() or ask_override:
self.ask_autobackup_hour()
self.ask_write_preview_history()
self.ask_target_iter()
self.ask_random_flip()
self.ask_batch_size(suggest_batch_size)
default_resolution = self.options['resolution'] = self.load_or_def_option('resolution', 128)
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_archi = self.options['archi'] = self.load_or_def_option('archi', 'dfhd')
default_ae_dims = self.options['ae_dims'] = self.load_or_def_option('ae_dims', 256)
default_e_dims = self.options['e_dims'] = self.load_or_def_option('e_dims', 64)
default_d_dims = self.options['d_dims'] = self.load_or_def_option('d_dims', 64)
default_d_mask_dims = default_d_dims // 3
default_d_mask_dims += default_d_mask_dims % 2
default_d_mask_dims = self.options['d_mask_dims'] = self.load_or_def_option('d_mask_dims', default_d_mask_dims)
default_learn_mask = self.options['learn_mask'] = self.load_or_def_option('learn_mask', True)
default_lr_dropout = self.options['lr_dropout'] = self.load_or_def_option('lr_dropout', False)
default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
default_true_face_training = self.options['true_face_training'] = self.load_or_def_option('true_face_training', False)
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_ct_mode = self.options['ct_mode'] = self.load_or_def_option('ct_mode', 'none')
default_clipgrad = self.options['clipgrad'] = self.load_or_def_option('clipgrad', False)
default_pretrain = self.options['pretrain'] = self.load_or_def_option('pretrain', False)
if self.is_first_run():
resolution = io.input_int("Resolution", default_resolution, add_info="64-256", help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
resolution = np.clip ( (resolution // 16) * 16, 64, 256)
self.options['resolution'] = resolution
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f'], help_message="Half / mid face / full face. Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face.").lower()
if (self.is_first_run() or ask_override) and len(device_config.devices) == 1:
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.")
if self.is_first_run():
self.options['archi'] = io.input_str ("AE architecture", default_archi, ['dfhd','liaehd','df','liae'], help_message="'df' keeps faces more natural. 'liae' can fix overly different face shapes. 'hd' is heavyweight version for the best quality.").lower() #-s version is slower, but has decreased change to collapse.
self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dimensions", default_ae_dims, add_info="32-1024", help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
e_dims = np.clip ( io.input_int("Encoder dimensions", default_e_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['e_dims'] = e_dims + e_dims % 2
d_dims = np.clip ( io.input_int("Decoder dimensions", default_d_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['d_dims'] = d_dims + d_dims % 2
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:
self.options['learn_mask'] = io.input_bool ("Learn mask", default_learn_mask, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case merger forced to use 'not predicted mask' that is not smooth as predicted.")
self.options['lr_dropout'] = io.input_bool ("Use learning rate dropout", default_lr_dropout, help_message="When the face is trained enough, you can enable this option to get extra sharpness for less amount of iterations.")
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 for less amount of iterations.")
if 'df' in self.options['archi']:
self.options['true_face_training'] = io.input_bool ("Enable 'true face' training", default_true_face_training, help_message="The result face will be more like src and will get extra sharpness. Enable it for last 10-20k iterations before conversion.")
else:
self.options['true_face_training'] = False
self.options['face_style_power'] = np.clip ( io.input_number("Face style power", default_face_style_power, add_info="0.0..100.0", help_message="Learn to transfer face style details such as light and color conditions. Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style. Start from 0.1 value and check history changes. Enabling this option increases the chance of model collapse."), 0.0, 100.0 )
self.options['bg_style_power'] = np.clip ( io.input_number("Background style power", default_bg_style_power, add_info="0.0..100.0", help_message="Learn to transfer background around face. This can make face more like dst. Enabling this option increases the chance of model collapse."), 0.0, 100.0 )
self.options['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain, help_message="Pretrain the model with large amount of various faces. After that, model can be used to train the fakes more quickly.")
if self.options['pretrain'] and self.get_pretraining_data_path() is None:
raise Exception("pretraining_data_path is not defined")
self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
if self.pretrain_just_disabled:
self.set_iter(1)
def on_initialize(self):
nn.initialize()
tf = nn.tf
nn.set_floatx(tf.float32)
conv_kernel_initializer = nn.initializers.ca
class Downscale(nn.ModelBase):
def __init__(self, in_ch, out_ch, kernel_size=5, dilations=1, subpixel=True, use_activator=True, *kwargs ):
self.in_ch = in_ch
self.out_ch = out_ch
self.kernel_size = kernel_size
self.dilations = dilations
self.subpixel = subpixel
self.use_activator = use_activator
super().__init__(*kwargs)
def on_build(self, *args, **kwargs ):
self.conv1 = nn.Conv2D( self.in_ch,
self.out_ch // (4 if self.subpixel else 1),
kernel_size=self.kernel_size,
strides=1 if self.subpixel else 2,
padding='SAME', dilations=self.dilations, kernel_initializer=conv_kernel_initializer )
def forward(self, x):
x = self.conv1(x)
if self.subpixel:
x = tf.nn.space_to_depth(x, 2)
if self.use_activator:
x = tf.nn.leaky_relu(x, 0.1)
return x
def get_out_ch(self):
return (self.out_ch // 4) * 4
class DownscaleBlock(nn.ModelBase):
def on_build(self, in_ch, ch, n_downscales, kernel_size, dilations=1, subpixel=True):
self.downs = []
last_ch = in_ch
for i in range(n_downscales):
cur_ch = ch*( min(2**i, 8) )
self.downs.append ( Downscale(last_ch, cur_ch, kernel_size=kernel_size, dilations=dilations, subpixel=subpixel) )
last_ch = self.downs[-1].get_out_ch()
def forward(self, inp):
x = inp
for down in self.downs:
x = down(x)
return x
class Upscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=3 ):
self.conv1 = nn.Conv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME', kernel_initializer=conv_kernel_initializer)
def forward(self, x):
x = self.conv1(x)
x = tf.nn.leaky_relu(x, 0.1)
x = tf.nn.depth_to_space(x, 2)
return x
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3 ):
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', kernel_initializer=conv_kernel_initializer)
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', kernel_initializer=conv_kernel_initializer)
def forward(self, inp):
x = self.conv1(inp)
x = tf.nn.leaky_relu(x, 0.2)
x = self.conv2(x)
x = tf.nn.leaky_relu(inp + x, 0.2)
return x
class UpdownResidualBlock(nn.ModelBase):
def on_build(self, ch, inner_ch, kernel_size=3 ):
self.up = Upscale (ch, inner_ch, kernel_size=kernel_size)
self.res = ResidualBlock (inner_ch, kernel_size=kernel_size)
self.down = Downscale (inner_ch, ch, kernel_size=kernel_size, use_activator=False)
def forward(self, inp):
x = self.up(inp)
x = upx = self.res(x)
x = self.down(x)
x = x + inp
x = tf.nn.leaky_relu(x, 0.2)
return x, upx
class Encoder(nn.ModelBase):
def on_build(self, in_ch, e_ch, is_hd):
self.is_hd=is_hd
if self.is_hd:
self.down1 = DownscaleBlock(in_ch, e_ch*2, n_downscales=4, kernel_size=3, dilations=1)
self.down2 = DownscaleBlock(in_ch, e_ch*2, n_downscales=4, kernel_size=5, dilations=1)
self.down3 = DownscaleBlock(in_ch, e_ch//2, n_downscales=4, kernel_size=5, dilations=2)
self.down4 = DownscaleBlock(in_ch, e_ch//2, n_downscales=4, kernel_size=7, dilations=2)
else:
self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5, dilations=1, subpixel=False)
def forward(self, inp):
if self.is_hd:
x = tf.concat([ nn.tf_flatten(self.down1(inp)),
nn.tf_flatten(self.down2(inp)),
nn.tf_flatten(self.down3(inp)),
nn.tf_flatten(self.down4(inp)) ], -1 )
else:
x = nn.tf_flatten(self.down1(inp))
return x
class Inter(nn.ModelBase):
def __init__(self, in_ch, lowest_dense_res, ae_ch, ae_out_ch, **kwargs):
self.in_ch, self.lowest_dense_res, self.ae_ch, self.ae_out_ch = in_ch, lowest_dense_res, ae_ch, ae_out_ch
super().__init__(**kwargs)
def on_build(self):
in_ch, lowest_dense_res, ae_ch, ae_out_ch = self.in_ch, self.lowest_dense_res, self.ae_ch, self.ae_out_ch
self.dense1 = nn.Dense( in_ch, ae_ch, kernel_initializer=tf.initializers.orthogonal )
self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch, kernel_initializer=tf.initializers.orthogonal )
self.upscale1 = Upscale(ae_out_ch, ae_out_ch)
def forward(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
x = tf.reshape (x, (-1, lowest_dense_res, lowest_dense_res, self.ae_out_ch))
x = self.upscale1(x)
return x
def get_out_ch(self):
return self.ae_out_ch
class Decoder(nn.ModelBase):
def on_build(self, in_ch, d_ch, d_mask_ch, is_hd ):
self.is_hd = is_hd
self.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3)
self.upscale1 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
self.upscale2 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
if is_hd:
self.res0 = UpdownResidualBlock(in_ch, d_ch*8, kernel_size=3)
self.res1 = UpdownResidualBlock(d_ch*8, d_ch*4, kernel_size=3)
self.res2 = UpdownResidualBlock(d_ch*4, d_ch*2, kernel_size=3)
self.res3 = UpdownResidualBlock(d_ch*2, d_ch, kernel_size=3)
else:
self.res0 = ResidualBlock(d_ch*8, kernel_size=3)
self.res1 = ResidualBlock(d_ch*4, kernel_size=3)
self.res2 = ResidualBlock(d_ch*2, kernel_size=3)
self.out_conv = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME', kernel_initializer=conv_kernel_initializer)
self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3)
self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3)
self.upscalem2 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3)
self.out_convm = nn.Conv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME', kernel_initializer=conv_kernel_initializer)
def get_weights_ex(self, include_mask):
# Call internal get_weights in order to initialize inner logic
self.get_weights()
weights = self.upscale0.get_weights() + self.upscale1.get_weights() + self.upscale2.get_weights() \
+ self.res0.get_weights() + self.res1.get_weights() + self.res2.get_weights() + self.out_conv.get_weights()
if include_mask:
weights += self.upscalem0.get_weights() + self.upscalem1.get_weights() + self.upscalem2.get_weights() \
+ self.out_convm.get_weights()
return weights
def forward(self, inp):
z = inp
if self.is_hd:
x, upx = self.res0(z)
x = self.upscale0(x)
x = tf.nn.leaky_relu(x + upx, 0.2)
x, upx = self.res1(x)
x = self.upscale1(x)
x = tf.nn.leaky_relu(x + upx, 0.2)
x, upx = self.res2(x)
x = self.upscale2(x)
x = tf.nn.leaky_relu(x + upx, 0.2)
x, upx = self.res3(x)
else:
x = self.upscale0(z)
x = self.res0(x)
x = self.upscale1(x)
x = self.res1(x)
x = self.upscale2(x)
x = self.res2(x)
m = self.upscalem0(z)
m = self.upscalem1(m)
m = self.upscalem2(m)
return tf.nn.sigmoid(self.out_conv(x)), \
tf.nn.sigmoid(self.out_convm(m))
class CodeDiscriminator(nn.ModelBase):
def on_build(self, in_ch, code_res, ch=256):
n_downscales = 2 + code_res // 8
self.convs = []
prev_ch = in_ch
for i in range(n_downscales):
cur_ch = ch * min( (2**i), 8 )
self.convs.append ( nn.Conv2D( prev_ch, cur_ch, kernel_size=4 if i == 0 else 3, strides=2, padding='SAME', kernel_initializer=conv_kernel_initializer) )
prev_ch = cur_ch
self.out_conv = nn.Conv2D( prev_ch, 1, kernel_size=1, padding='VALID', kernel_initializer=conv_kernel_initializer)
def forward(self, x):
for conv in self.convs:
x = tf.nn.leaky_relu( conv(x), 0.1 )
return self.out_conv(x)
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
resolution = self.options['resolution']
learn_mask = self.options['learn_mask']
archi = self.options['archi']
ae_dims = self.options['ae_dims']
e_dims = self.options['e_dims']
d_dims = self.options['d_dims']
d_mask_dims = self.options['d_mask_dims']
self.pretrain = self.options['pretrain']
masked_training = True
models_opt_on_gpu = False if len(devices) != 1 else self.options['models_opt_on_gpu']
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
input_nc = 3
output_nc = 3
bgr_shape = (resolution, resolution, output_nc)
mask_shape = (resolution, resolution, 1)
lowest_dense_res = resolution // 16
self.model_filename_list = []
with tf.device ('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder (tf.float32, (None,)+bgr_shape)
self.warped_dst = tf.placeholder (tf.float32, (None,)+bgr_shape)
self.target_src = tf.placeholder (tf.float32, (None,)+bgr_shape)
self.target_dst = tf.placeholder (tf.float32, (None,)+bgr_shape)
self.target_srcm = tf.placeholder (tf.float32, (None,)+mask_shape)
self.target_dstm = tf.placeholder (tf.float32, (None,)+mask_shape)
self.target_dst_0 = tf.placeholder (tf.float32, (None,)+bgr_shape)
self.target_dst_1 = tf.placeholder (tf.float32, (None,)+bgr_shape)
self.target_dst_2 = tf.placeholder (tf.float32, (None,)+bgr_shape)
# Initializing model classes
with tf.device (models_opt_device):
if 'df' in archi:
self.encoder = Encoder(in_ch=input_nc, e_ch=e_dims, is_hd='hd' in archi, name='encoder')
encoder_out_ch = self.encoder.compute_output_shape ( (tf.float32, (None,resolution,resolution,input_nc)))[-1]
self.inter = Inter (in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims, name='inter')
inter_out_ch = self.inter.compute_output_shape ( (tf.float32, (None,encoder_out_ch)))[-1]
self.decoder_src = Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd='hd' in archi, name='decoder_src')
self.decoder_dst = Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd='hd' in archi, name='decoder_dst')
self.model_filename_list += [ [self.encoder, 'encoder.npy' ],
[self.inter, 'inter.npy' ],
[self.decoder_src, 'decoder_src.npy'],
[self.decoder_dst, 'decoder_dst.npy'] ]
if self.is_training:
if self.options['true_face_training']:
self.dis = CodeDiscriminator(ae_dims, code_res=lowest_dense_res*2, name='dis' )
self.model_filename_list += [ [self.dis, 'dis.npy'] ]
elif 'liae' in archi:
self.encoder = Encoder(in_ch=input_nc, e_ch=e_dims, is_hd='hd' in archi, name='encoder')
encoder_out_ch = self.encoder.compute_output_shape ( (tf.float32, (None,resolution,resolution,input_nc)))[-1]
self.inter_AB = Inter(in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims*2, name='inter_AB')
self.inter_B = Inter(in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims*2, name='inter_B')
inter_AB_out_ch = self.inter_AB.compute_output_shape ( (tf.float32, (None,encoder_out_ch)))[-1]
inter_B_out_ch = self.inter_B.compute_output_shape ( (tf.float32, (None,encoder_out_ch)))[-1]
inters_out_ch = inter_AB_out_ch+inter_B_out_ch
self.decoder = Decoder(in_ch=inters_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd='hd' in archi, name='decoder')
self.model_filename_list += [ [self.encoder, 'encoder.npy'],
[self.inter_AB, 'inter_AB.npy'],
[self.inter_B , 'inter_B.npy'],
[self.decoder , 'decoder.npy'] ]
if self.is_training:
# Initialize optimizers
lr=5e-5
lr_dropout = 0.3 if self.options['lr_dropout'] else 1.0
clipnorm = 1.0 if self.options['clipgrad'] else 0.0
self.src_dst_opt = nn.TFRMSpropOptimizer(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
if 'df' in archi:
self.src_dst_all_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights_ex(learn_mask) + self.decoder_dst.get_weights_ex(learn_mask)
self.src_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights_ex(False)
elif 'liae' in archi:
self.src_dst_all_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights_ex(learn_mask)
self.src_dst_opt.initialize_variables (self.src_dst_all_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu)
if self.options['true_face_training']:
self.D_opt = nn.TFRMSpropOptimizer(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_opt')
self.D_opt.initialize_variables ( self.dis.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [ (self.D_opt, 'D_opt.npy') ]
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices) )
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size( gpu_count*bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_src_dst_loss_gvs = []
gpu_D_loss_gvs = []
gpu_var_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
gpu_warped_src = self.warped_src [batch_slice,:,:,:]
gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
gpu_target_src = self.target_src [batch_slice,:,:,:]
gpu_target_dst = self.target_dst [batch_slice,:,:,:]
gpu_target_srcm = self.target_srcm[batch_slice,:,:,:]
gpu_target_dstm = self.target_dstm[batch_slice,:,:,:]
gpu_target_dst_0 = self.target_dst_0[batch_slice,:,:,:]
gpu_target_dst_1 = self.target_dst_1[batch_slice,:,:,:]
gpu_target_dst_2 = self.target_dst_2[batch_slice,:,:,:]
# process model tensors
if 'df' in archi:
gpu_src_code = self.inter(self.encoder(gpu_warped_src))
gpu_dst_code = self.inter(self.encoder(gpu_warped_dst))
gpu_dst_0_code = self.inter(self.encoder(gpu_target_dst_0))
gpu_dst_1_code = self.inter(self.encoder(gpu_target_dst_1))
gpu_dst_2_code = self.inter(self.encoder(gpu_target_dst_2))
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(gpu_src_code)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
gpu_pred_src_dst_0, _ = self.decoder_src(gpu_dst_0_code)
gpu_pred_src_dst_1, _ = self.decoder_src(gpu_dst_1_code)
gpu_pred_src_dst_2, _ = self.decoder_src(gpu_dst_2_code)
elif 'liae' in archi:
gpu_src_code = self.encoder (gpu_warped_src)
gpu_src_inter_AB_code = self.inter_AB (gpu_src_code)
gpu_src_code = tf.concat([gpu_src_inter_AB_code,gpu_src_inter_AB_code],-1)
gpu_dst_code = self.encoder (gpu_warped_dst)
gpu_dst_inter_B_code = self.inter_B (gpu_dst_code)
gpu_dst_inter_AB_code = self.inter_AB (gpu_dst_code)
gpu_dst_code = tf.concat([gpu_dst_inter_B_code,gpu_dst_inter_AB_code],-1)
gpu_src_dst_code = tf.concat([gpu_dst_inter_AB_code,gpu_dst_inter_AB_code],-1)
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_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
gpu_target_srcm_blur = nn.tf_gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
gpu_target_dstm_blur = nn.tf_gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
gpu_target_dst_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_blur)
gpu_target_srcmasked_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_masked = gpu_pred_src_dst*gpu_target_dstm_blur
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_blur)
gpu_src_loss = tf.reduce_mean ( 10*nn.tf_dssim(gpu_target_srcmasked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_srcmasked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
if learn_mask:
gpu_src_loss += tf.reduce_mean ( tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
face_style_power = self.options['face_style_power'] / 100.0
if face_style_power != 0 and not self.pretrain:
gpu_src_loss += nn.tf_style_loss(gpu_psd_target_dst_masked, gpu_target_dst_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.tf_dssim(gpu_psd_target_dst_anti_masked, gpu_target_dst_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_anti_masked - gpu_target_dst_anti_masked), axis=[1,2,3] )
gpu_dst_loss = tf.reduce_mean ( 10*nn.tf_dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
if learn_mask:
gpu_dst_loss += tf.reduce_mean ( tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_src_dst_loss = gpu_src_loss + gpu_dst_loss
if self.options['true_face_training']:
def DLoss(labels,logits):
return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits), axis=[1,2,3])
gpu_src_code_d = self.dis( gpu_src_code )
gpu_src_code_d_ones = tf.ones_like(gpu_src_code_d)
gpu_src_code_d_zeros = tf.zeros_like(gpu_src_code_d)
gpu_dst_code_d = self.dis( gpu_dst_code )
gpu_dst_code_d_ones = tf.ones_like(gpu_dst_code_d)
gpu_src_dst_loss += 0.01*DLoss(gpu_src_code_d_ones, gpu_src_code_d)
gpu_D_loss = (DLoss(gpu_src_code_d_ones , gpu_dst_code_d) + \
DLoss(gpu_src_code_d_zeros, gpu_src_code_d) ) * 0.5
gpu_D_loss_gvs += [ nn.tf_gradients (gpu_D_loss, self.dis.get_weights() ) ]
gpu_src_dst_loss_gvs += [ nn.tf_gradients ( gpu_src_dst_loss, self.src_dst_trainable_weights ) ]
gpu_var_loss = nn.tf_style_loss (gpu_pred_src_dst_1, gpu_pred_src_dst_0, gaussian_blur_radius=resolution//16, loss_weight=100)
gpu_var_loss += nn.tf_style_loss (gpu_pred_src_dst_1, gpu_pred_src_dst_2, gaussian_blur_radius=resolution//16, loss_weight=100)
gpu_var_loss_gvs += [ nn.tf_gradients ( gpu_var_loss, self.src_trainable_weights ) ]
# Average losses and gradients, and create optimizer update ops
with tf.device (models_opt_device):
if gpu_count == 1:
pred_src_src = gpu_pred_src_src_list[0]
pred_dst_dst = gpu_pred_dst_dst_list[0]
pred_src_dst = gpu_pred_src_dst_list[0]
pred_src_srcm = gpu_pred_src_srcm_list[0]
pred_dst_dstm = gpu_pred_dst_dstm_list[0]
pred_src_dstm = gpu_pred_src_dstm_list[0]
src_loss = gpu_src_losses[0]
dst_loss = gpu_dst_losses[0]
src_dst_loss_gv = gpu_src_dst_loss_gvs[0]
var_loss_gv = gpu_var_loss_gvs[0]
else:
pred_src_src = tf.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = tf.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = tf.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = tf.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = tf.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = tf.concat(gpu_pred_src_dstm_list, 0)
src_loss = nn.tf_average_tensor_list(gpu_src_losses)
dst_loss = nn.tf_average_tensor_list(gpu_dst_losses)
src_dst_loss_gv = nn.tf_average_gv_list (gpu_src_dst_loss_gvs)
var_loss_gv = nn.tf_average_gv_list (gpu_var_loss_gvs)
if self.options['true_face_training']:
D_loss_gv = nn.tf_average_gv_list(gpu_D_loss_gvs)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op (src_dst_loss_gv )
if self.options['true_face_training']:
D_loss_gv_op = self.D_opt.get_update_op (D_loss_gv )
var_loss_gv_op = self.src_dst_opt.get_update_op (var_loss_gv )
# Initializing training and view functions
def src_dst_train(warped_src, target_src, target_srcm, \
warped_dst, target_dst, target_dstm):
s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm:target_srcm,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm:target_dstm,
})
s = np.mean(s)
d = np.mean(d)
return s, d
self.src_dst_train = src_dst_train
def var_train(target_dst_0, target_dst_1, target_dst_2):
_ = nn.tf_sess.run ( [ var_loss_gv_op],
feed_dict={self.target_dst_0 :target_dst_0,
self.target_dst_1 :target_dst_1,
self.target_dst_2 :target_dst_2,
})
self.var_train = var_train
if self.options['true_face_training']:
def D_train(warped_src, warped_dst):
nn.tf_sess.run ([D_loss_gv_op], feed_dict={self.warped_src: warped_src, self.warped_dst: warped_dst})
self.D_train = D_train
if learn_mask:
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
feed_dict={self.warped_src:warped_src,
self.warped_dst:warped_dst})
else:
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_src_dst],
feed_dict={self.warped_src:warped_src,
self.warped_dst:warped_dst})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device( f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
if 'df' in archi:
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
elif 'liae' in archi:
gpu_dst_code = self.encoder (self.warped_dst)
gpu_dst_inter_B_code = self.inter_B (gpu_dst_code)
gpu_dst_inter_AB_code = self.inter_AB (gpu_dst_code)
gpu_dst_code = tf.concat([gpu_dst_inter_B_code,gpu_dst_inter_AB_code],-1)
gpu_src_dst_code = tf.concat([gpu_dst_inter_AB_code,gpu_dst_inter_AB_code],-1)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
if learn_mask:
def AE_merge( warped_dst):
return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
else:
def AE_merge( warped_dst):
return nn.tf_sess.run ( [gpu_pred_src_dst], feed_dict={self.warped_dst:warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
do_init = self.is_first_run()
if self.pretrain_just_disabled:
if 'df' in archi:
if model == self.inter:
do_init = True
elif 'liae' in archi:
if model == self.inter_AB:
do_init = True
if not do_init:
do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
t = SampleProcessor.Types
if self.options['face_type'] == 'h':
face_type = t.FACE_TYPE_HALF
elif self.options['face_type'] == 'mf':
face_type = t.FACE_TYPE_MID_FULL
elif self.options['face_type'] == 'f':
face_type = t.FACE_TYPE_FULL
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path()
random_ct_samples_path=training_data_dst_path if self.options['ct_mode'] != 'none' and not self.pretrain else None
t_img_warped = t.IMG_WARPED_TRANSFORMED if self.options['random_warp'] else t.IMG_TRANSFORMED
cpu_count = multiprocessing.cpu_count()
src_generators_count = cpu_count // 2
if self.options['ct_mode'] != 'none':
src_generators_count = int(src_generators_count * 1.5)
dst_generators_count = cpu_count - src_generators_count
self.set_training_data_generators ([
SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
output_sample_types = [ {'types' : (t_img_warped, face_type, t.MODE_BGR), 'resolution':resolution, 'ct_mode': self.options['ct_mode'] },
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution': resolution, 'ct_mode': self.options['ct_mode'] },
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_FACE_MASK_ALL_HULL), 'resolution': resolution } ],
generators_count=src_generators_count ),
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
output_sample_types = [ {'types' : (t_img_warped, face_type, t.MODE_BGR), 'resolution':resolution},
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution': resolution},
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_FACE_MASK_ALL_HULL), 'resolution': resolution} ],
generators_count=dst_generators_count ),
SampleGeneratorFaceTemporal(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=False),
output_sample_types = [{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution': resolution}],
generators_count=dst_generators_count )
])
def on_initialize_options(self):
device_config = nn.getCurrentDeviceConfig()
lowest_vram = 2
if len(device_config.devices) != 0:
lowest_vram = device_config.devices.get_worst_device().total_mem_gb
if lowest_vram >= 4:
suggest_batch_size = 8
else:
suggest_batch_size = 4
yn_str = {True:'y',False:'n'}
min_res = 64
max_res = 640
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_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)
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', 0.33)
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', True)
default_uniform_yaw = self.options['uniform_yaw'] = self.load_or_def_option('uniform_yaw', False)
lr_dropout = self.load_or_def_option('lr_dropout', 'n')
lr_dropout = {True:'y', False:'n'}.get(lr_dropout, lr_dropout) #backward comp
default_lr_dropout = self.options['lr_dropout'] = lr_dropout
default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
default_ct_mode = self.options['ct_mode'] = self.load_or_def_option('ct_mode', 'none')
default_clipgrad = self.options['clipgrad'] = self.load_or_def_option('clipgrad', False)
default_pretrain = self.options['pretrain'] = self.load_or_def_option('pretrain', False)
ask_override = self.ask_override()
if self.is_first_run() or ask_override:
self.ask_autobackup_hour()
self.ask_write_preview_history()
self.ask_target_iter()
self.ask_random_src_flip()
self.ask_random_dst_flip()
self.ask_batch_size(suggest_batch_size)
if self.is_first_run():
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, min_res, max_res)
self.options['resolution'] = resolution
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['wf','head'], help_message="whole face / head").lower()
default_d_dims = self.options['d_dims'] = self.load_or_def_option('d_dims', 64)
default_d_mask_dims = default_d_dims // 3
default_d_mask_dims += default_d_mask_dims % 2
default_d_mask_dims = self.options['d_mask_dims'] = self.load_or_def_option('d_mask_dims', default_d_mask_dims)
if self.is_first_run():
self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dimensions", default_ae_dims, add_info="32-1024", help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
e_dims = np.clip ( io.input_int("Encoder dimensions", default_e_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['e_dims'] = e_dims + e_dims % 2
d_dims = np.clip ( io.input_int("Decoder dimensions", default_d_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['d_dims'] = d_dims + d_dims % 2
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
morph_factor = np.clip ( io.input_number ("Morph factor.", default_morph_factor, add_info="0.1 .. 0.5", help_message="The smaller the value, the more src-like facial expressions will appear. The larger the value, the less space there is to train a large dst faceset in the neural network. Typical fine value is 0.33"), 0.1, 0.5 )
self.options['morph_factor'] = morph_factor
if self.is_first_run() or ask_override:
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.")
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.')
default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
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)
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.")
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.")
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.")
self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 1.0", 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, 1.0 )
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
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['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain, help_message="Pretrain the model with large amount of various faces. After that, model can be used to train the fakes more quickly. Forces random_warp=N, random_flips=Y, gan_power=0.0, lr_dropout=N, uniform_yaw=Y")
self.gan_model_changed = (default_gan_patch_size != self.options['gan_patch_size']) or (default_gan_dims != self.options['gan_dims'])
self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
self.model_data_format = "NCHW" if len(
device_config.devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.resolution = resolution = 256
self.face_type = FaceType.WHOLE_FACE
place_model_on_cpu = len(devices) == 0
models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
bgr_shape = nn.get4Dshape(resolution, resolution, 3)
mask_shape = nn.get4Dshape(resolution, resolution, 1)
# Initializing model classes
self.model = XSegNet(name=f'XSeg',
resolution=resolution,
load_weights=not self.is_first_run(),
weights_file_root=self.get_model_root_path(),
training=True,
place_model_on_cpu=place_model_on_cpu,
optimizer=nn.RMSprop(lr=0.0001,
lr_dropout=0.3,
name='opt'),
data_format=nn.data_format)
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices))
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size(gpu_count * bs_per_gpu)
# Compute losses per GPU
gpu_pred_list = []
gpu_losses = []
gpu_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device(
f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0'):
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
batch_slice = slice(gpu_id * bs_per_gpu,
(gpu_id + 1) * bs_per_gpu)
gpu_input_t = self.model.input_t[batch_slice, :, :, :]
gpu_target_t = self.model.target_t[
batch_slice, :, :, :]
# process model tensors
gpu_pred_logits_t, gpu_pred_t = self.model.flow(
gpu_input_t)
gpu_pred_list.append(gpu_pred_t)
gpu_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=gpu_target_t, logits=gpu_pred_logits_t),
axis=[1, 2, 3])
gpu_losses += [gpu_loss]
gpu_loss_gvs += [
nn.gradients(gpu_loss, self.model.get_weights())
]
# Average losses and gradients, and create optimizer update ops
with tf.device(models_opt_device):
pred = nn.concat(gpu_pred_list, 0)
loss = tf.reduce_mean(gpu_losses)
loss_gv_op = self.model.opt.get_update_op(
nn.average_gv_list(gpu_loss_gvs))
# Initializing training and view functions
def train(input_np, target_np):
l, _ = nn.tf_sess.run([loss, loss_gv_op],
feed_dict={
self.model.input_t: input_np,
self.model.target_t: target_np
})
return l
self.train = train
def view(input_np):
return nn.tf_sess.run([pred],
feed_dict={self.model.input_t: input_np})
self.view = view
# initializing sample generators
cpu_count = min(multiprocessing.cpu_count(), 8)
src_dst_generators_count = cpu_count // 2
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
srcdst_generator = SampleGeneratorFaceXSeg(
[self.training_data_src_path, self.training_data_dst_path],
debug=self.is_debug(),
batch_size=self.get_batch_size(),
resolution=resolution,
face_type=self.face_type,
generators_count=src_dst_generators_count,
data_format=nn.data_format)
src_generator = SampleGeneratorFace(
self.training_data_src_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=False),
output_sample_types=[
{
'sample_type': SampleProcessor.SampleType.FACE_IMAGE,
'warp': False,
'transform': False,
'channel_type': SampleProcessor.ChannelType.BGR,
'border_replicate': False,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution
},
],
generators_count=src_generators_count,
raise_on_no_data=False)
dst_generator = SampleGeneratorFace(
self.training_data_dst_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=False),
output_sample_types=[
{
'sample_type': SampleProcessor.SampleType.FACE_IMAGE,
'warp': False,
'transform': False,
'channel_type': SampleProcessor.ChannelType.BGR,
'border_replicate': False,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution
},
],
generators_count=dst_generators_count,
raise_on_no_data=False)
self.set_training_data_generators(
[srcdst_generator, src_generator, dst_generator])
def on_initialize_options(self):
device_config = nn.getCurrentDeviceConfig()
lowest_vram = 2
if len(device_config.devices) != 0:
lowest_vram = device_config.devices.get_worst_device().total_mem_gb
if lowest_vram >= 4:
suggest_batch_size = 8
else:
suggest_batch_size = 4
yn_str = {True:'y',False:'n'}
min_res = 64
max_res = 640
default_resolution = self.options['resolution'] = self.load_or_def_option('resolution', 128)
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)
archi = self.load_or_def_option('archi', 'df')
archi = {'dfuhd':'df-u','liaeuhd':'liae-u'}.get(archi, archi) #backward comp
default_archi = self.options['archi'] = archi
default_ae_dims = self.options['ae_dims'] = self.load_or_def_option('ae_dims', 256)
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_masked_training = self.options['masked_training'] = self.load_or_def_option('masked_training', True)
default_eyes_prio = self.options['eyes_prio'] = self.load_or_def_option('eyes_prio', False)
default_uniform_yaw = self.options['uniform_yaw'] = self.load_or_def_option('uniform_yaw', False)
lr_dropout = self.load_or_def_option('lr_dropout', 'n')
lr_dropout = {True:'y', False:'n'}.get(lr_dropout, lr_dropout) #backward comp
default_lr_dropout = self.options['lr_dropout'] = lr_dropout
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_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_bg_style_power = self.options['bg_style_power'] = self.load_or_def_option('bg_style_power', 0.0)
default_ct_mode = self.options['ct_mode'] = self.load_or_def_option('ct_mode', 'none')
default_clipgrad = self.options['clipgrad'] = self.load_or_def_option('clipgrad', False)
default_pretrain = self.options['pretrain'] = self.load_or_def_option('pretrain', False)
ask_override = self.ask_override()
if self.is_first_run() or ask_override:
self.ask_autobackup_hour()
self.ask_write_preview_history()
self.ask_target_iter()
self.ask_random_flip()
self.ask_batch_size(suggest_batch_size)
if self.is_first_run():
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 16 and 32 for -d archi.")
resolution = np.clip ( (resolution // 16) * 16, min_res, max_res)
self.options['resolution'] = resolution
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head'], help_message="Half / mid face / full face / whole face / head. 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()
while True:
archi = io.input_str ("AE architecture", default_archi, help_message=\
"""
'df' keeps more identity-preserved face.
'liae' can fix overly different face shapes.
'-u' increased likeness of the face.
'-d' (experimental) doubling the resolution using the same computation cost.
Examples: df, liae, df-d, df-ud, liae-ud, ...
""").lower()
archi_split = archi.split('-')
if len(archi_split) == 2:
archi_type, archi_opts = archi_split
elif len(archi_split) == 1:
archi_type, archi_opts = archi_split[0], None
else:
continue
if archi_type not in ['df', 'liae']:
continue
if archi_opts is not None:
if len(archi_opts) == 0:
continue
if len([ 1 for opt in archi_opts if opt not in ['u','d'] ]) != 0:
continue
if 'd' in archi_opts:
self.options['resolution'] = np.clip ( (self.options['resolution'] // 32) * 32, min_res, max_res)
break
self.options['archi'] = archi
default_d_dims = self.options['d_dims'] = self.load_or_def_option('d_dims', 64)
default_d_mask_dims = default_d_dims // 3
default_d_mask_dims += default_d_mask_dims % 2
default_d_mask_dims = self.options['d_mask_dims'] = self.load_or_def_option('d_mask_dims', default_d_mask_dims)
if self.is_first_run():
self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dimensions", default_ae_dims, add_info="32-1024", help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
e_dims = np.clip ( io.input_int("Encoder dimensions", default_e_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['e_dims'] = e_dims + e_dims % 2
d_dims = np.clip ( io.input_int("Decoder dimensions", default_d_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['d_dims'] = d_dims + d_dims % 2
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:
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.")
self.options['eyes_prio'] = io.input_bool ("Eyes priority", default_eyes_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction ( especially on HD architectures ) by forcing the neural network to train eyes with higher priority. before/after https://i.imgur.com/YQHOuSR.jpg ')
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.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.")
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.")
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.")
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 '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 )
else:
self.options['true_face_power'] = 0.0
self.options['face_style_power'] = np.clip ( io.input_number("Face style power", default_face_style_power, add_info="0.0..100.0", help_message="Learn the color of the predicted face to be the same as dst inside mask. If you want to use this option with 'whole_face' you have to use XSeg trained mask. Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style. Start from 0.001 value and check history changes. Enabling this option increases the chance of model collapse."), 0.0, 100.0 )
self.options['bg_style_power'] = np.clip ( io.input_number("Background style power", default_bg_style_power, add_info="0.0..100.0", help_message="Learn the area outside mask of the predicted face to be the same as dst. If you want to use this option with 'whole_face' you have to use XSeg trained mask. For whole_face you have to use XSeg trained mask. This can make face more like dst. Enabling this option increases the chance of model collapse. Typical value is 2.0"), 0.0, 100.0 )
self.options['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain, help_message="Pretrain the model with large amount of various faces. After that, model can be used to train the fakes more quickly.")
if self.options['pretrain'] and self.get_pretraining_data_path() is None:
raise Exception("pretraining_data_path is not defined")
self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
def on_initialize_options(self):
device_config = nn.getCurrentDeviceConfig()
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_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)
inter_dims = self.load_or_def_option('inter_dims', None)
if inter_dims is None:
inter_dims = self.options['ae_dims']
default_inter_dims = self.options['inter_dims'] = inter_dims
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_uniform_yaw = self.options['uniform_yaw'] = self.load_or_def_option('uniform_yaw', False)
default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
default_ct_mode = self.options['ct_mode'] = self.load_or_def_option('ct_mode', 'none')
default_clipgrad = self.options['clipgrad'] = self.load_or_def_option('clipgrad', False)
ask_override = self.ask_override()
if self.is_first_run() or ask_override:
self.ask_autobackup_hour()
self.ask_write_preview_history()
self.ask_target_iter()
self.ask_random_src_flip()
self.ask_random_dst_flip()
self.ask_batch_size(8)
if self.is_first_run():
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()
default_d_dims = self.options['d_dims'] = self.load_or_def_option('d_dims', 64)
default_d_mask_dims = default_d_dims // 3
default_d_mask_dims += default_d_mask_dims % 2
default_d_mask_dims = self.options['d_mask_dims'] = self.load_or_def_option('d_mask_dims', default_d_mask_dims)
if self.is_first_run():
self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dimensions", default_ae_dims, add_info="32-1024", help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
self.options['inter_dims'] = np.clip ( io.input_int("Inter dimensions", default_inter_dims, add_info="32-2048", help_message="Should be equal or more than AutoEncoder dimensions. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 2048 )
e_dims = np.clip ( io.input_int("Encoder dimensions", default_e_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['e_dims'] = e_dims + e_dims % 2
d_dims = np.clip ( io.input_int("Decoder dimensions", default_d_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['d_dims'] = d_dims + d_dims % 2
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
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.is_first_run() or ask_override:
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.')
default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
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)
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.")
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.")
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
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['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
self.gan_model_changed = (default_gan_patch_size != self.options['gan_patch_size']) or (default_gan_dims != self.options['gan_dims'])
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
self.model_data_format = "NCHW" if len(
device_config.devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
conv_kernel_initializer = nn.initializers.ca()
class Downscale(nn.ModelBase):
def __init__(self,
in_ch,
out_ch,
kernel_size=3,
dilations=1,
use_activator=True,
*kwargs):
self.in_ch = in_ch
self.out_ch = out_ch
self.kernel_size = kernel_size
self.dilations = dilations
self.use_activator = use_activator
super().__init__(*kwargs)
def on_build(self, *args, **kwargs):
self.conv1 = nn.Conv2D(
self.in_ch,
self.out_ch,
kernel_size=self.kernel_size,
strides=2,
padding='SAME',
dilations=self.dilations,
kernel_initializer=conv_kernel_initializer)
def forward(self, x):
x = self.conv1(x)
if self.use_activator:
x = tf.nn.leaky_relu(x, 0.1)
return x
class Upscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=3):
self.conv1 = nn.Conv2D(
in_ch,
out_ch * 4,
kernel_size=kernel_size,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
def forward(self, x):
x = self.conv1(x)
x = tf.nn.leaky_relu(x, 0.1)
x = nn.depth_to_space(x, 2)
return x
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, mod=1, kernel_size=3):
self.conv1 = nn.Conv2D(
ch,
ch * mod,
kernel_size=kernel_size,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
self.conv2 = nn.Conv2D(
ch * mod,
ch,
kernel_size=kernel_size,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
def forward(self, inp):
x = self.conv1(inp)
x = tf.nn.leaky_relu(x, 0.1)
x = self.conv2(x)
x = inp + x
x = tf.nn.leaky_relu(x, 0.1)
return x
class Encoder(nn.ModelBase):
def on_build(self, in_ch, e_ch):
self.conv1 = Downscale(in_ch, e_ch)
self.conv2 = Downscale(e_ch, e_ch * 2)
self.conv3 = Downscale(e_ch * 2, e_ch * 4)
self.conv4 = Downscale(e_ch * 4, e_ch * 8)
self.conv5 = Downscale(e_ch * 8, e_ch * 16)
self.conv6 = Downscale(e_ch * 16, e_ch * 32)
self.conv7 = Downscale(e_ch * 32, e_ch * 64)
self.res1 = ResidualBlock(e_ch)
self.res2 = ResidualBlock(e_ch * 2)
self.res3 = ResidualBlock(e_ch * 4)
self.res4 = ResidualBlock(e_ch * 8)
self.res5 = ResidualBlock(e_ch * 16)
self.res6 = ResidualBlock(e_ch * 32)
self.res7 = ResidualBlock(e_ch * 64)
def forward(self, inp):
x = self.conv1(inp)
x = self.res1(x)
x = self.conv2(x)
x = self.res2(x)
x = self.conv3(x)
x = self.res3(x)
x = self.conv4(x)
x = self.res4(x)
x = self.conv5(x)
x = self.res5(x)
x = self.conv6(x)
x = self.res6(x)
x = self.conv7(x)
x = self.res7(x)
return x
class Inter(nn.ModelBase):
def __init__(self, in_ch, ae_ch, **kwargs):
self.in_ch, self.ae_ch = in_ch, ae_ch
super().__init__(**kwargs)
def on_build(self):
in_ch, ae_ch = self.in_ch, self.ae_ch
self.dense_conv1 = nn.Conv2D(
in_ch,
64,
kernel_size=1,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
self.dense_conv2 = nn.Conv2D(
64,
in_ch,
kernel_size=1,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
self.conv7 = Upscale(in_ch, in_ch // 2)
self.conv6 = Upscale(in_ch // 2, in_ch // 4)
def forward(self, inp):
x = inp
x = self.dense_conv1(x)
x = self.dense_conv2(x)
x = self.conv7(x)
x = self.conv6(x)
return x
class Decoder(nn.ModelBase):
def on_build(self, in_ch):
self.upscale6 = Upscale(in_ch, in_ch // 2)
self.upscale5 = Upscale(in_ch // 2, in_ch // 4)
self.upscale4 = Upscale(in_ch // 4, in_ch // 8)
self.upscale3 = Upscale(in_ch // 8, in_ch // 16)
self.upscale2 = Upscale(in_ch // 16, in_ch // 32)
#self.upscale1 = Upscale(in_ch//32, in_ch//64)
self.out_conv = nn.Conv2D(
in_ch // 32,
3,
kernel_size=1,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
self.res61 = ResidualBlock(in_ch // 2, mod=8)
self.res62 = ResidualBlock(in_ch // 2, mod=8)
self.res63 = ResidualBlock(in_ch // 2, mod=8)
self.res51 = ResidualBlock(in_ch // 4, mod=8)
self.res52 = ResidualBlock(in_ch // 4, mod=8)
self.res53 = ResidualBlock(in_ch // 4, mod=8)
self.res41 = ResidualBlock(in_ch // 8, mod=8)
self.res42 = ResidualBlock(in_ch // 8, mod=8)
self.res43 = ResidualBlock(in_ch // 8, mod=8)
self.res31 = ResidualBlock(in_ch // 16, mod=8)
self.res32 = ResidualBlock(in_ch // 16, mod=8)
self.res33 = ResidualBlock(in_ch // 16, mod=8)
self.res21 = ResidualBlock(in_ch // 32, mod=8)
self.res22 = ResidualBlock(in_ch // 32, mod=8)
self.res23 = ResidualBlock(in_ch // 32, mod=8)
m_ch = in_ch // 2
self.upscalem6 = Upscale(in_ch, m_ch // 2)
self.upscalem5 = Upscale(m_ch // 2, m_ch // 4)
self.upscalem4 = Upscale(m_ch // 4, m_ch // 8)
self.upscalem3 = Upscale(m_ch // 8, m_ch // 16)
self.upscalem2 = Upscale(m_ch // 16, m_ch // 32)
#self.upscalem1 = Upscale(m_ch//32, m_ch//64)
self.out_convm = nn.Conv2D(
m_ch // 32,
1,
kernel_size=1,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
def forward(self, inp):
z = inp
x = self.upscale6(z)
x = self.res61(x)
x = self.res62(x)
x = self.res63(x)
x = self.upscale5(x)
x = self.res51(x)
x = self.res52(x)
x = self.res53(x)
x = self.upscale4(x)
x = self.res41(x)
x = self.res42(x)
x = self.res43(x)
x = self.upscale3(x)
x = self.res31(x)
x = self.res32(x)
x = self.res33(x)
x = self.upscale2(x)
x = self.res21(x)
x = self.res22(x)
x = self.res23(x)
#x = self.upscale1 (x)
y = self.upscalem6(z)
y = self.upscalem5(y)
y = self.upscalem4(y)
y = self.upscalem3(y)
y = self.upscalem2(y)
#y = self.upscalem1 (y)
return tf.nn.sigmoid(self.out_conv(x)), \
tf.nn.sigmoid(self.out_convm(y))
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
resolution = self.resolution = 128
ae_dims = 128
e_dims = 16
self.pretrain = False
self.pretrain_just_disabled = False
masked_training = True
models_opt_on_gpu = len(devices) >= 1 and all(
[dev.total_mem_gb >= 4 for dev in devices])
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device == '/CPU:0'
input_ch = 3
output_ch = 3
bgr_shape = nn.get4Dshape(resolution, resolution, input_ch)
mask_shape = nn.get4Dshape(resolution, resolution, 1)
self.model_filename_list = []
with tf.device('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder(nn.floatx, bgr_shape)
self.warped_dst = tf.placeholder(nn.floatx, bgr_shape)
self.target_src = tf.placeholder(nn.floatx, bgr_shape)
self.target_dst = tf.placeholder(nn.floatx, bgr_shape)
self.target_srcm = tf.placeholder(nn.floatx, mask_shape)
self.target_dstm = tf.placeholder(nn.floatx, mask_shape)
# Initializing model classes
with tf.device(models_opt_device):
self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, name='encoder')
self.inter = Inter(in_ch=e_dims * 64, ae_ch=ae_dims, name='inter')
self.decoder_src = Decoder(in_ch=e_dims * 16, name='decoder_src')
self.decoder_dst = Decoder(in_ch=e_dims * 16, name='decoder_dst')
self.model_filename_list += [[self.encoder, 'encoder.npy'],
[self.inter, 'inter.npy'],
[self.decoder_src, 'decoder_src.npy'],
[self.decoder_dst, 'decoder_dst.npy']]
if self.is_training:
self.src_dst_trainable_weights = self.encoder.get_weights(
) + self.inter.get_weights() + self.decoder_src.get_weights(
) + self.decoder_dst.get_weights()
# Initialize optimizers
self.src_dst_opt = nn.RMSprop(lr=5e-5, name='src_dst_opt')
self.src_dst_opt.initialize_variables(
self.src_dst_trainable_weights,
vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [(self.src_dst_opt,
'src_dst_opt.npy')]
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices))
bs_per_gpu = max(1, 4 // gpu_count)
self.set_batch_size(gpu_count * bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device(
f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0'):
batch_slice = slice(gpu_id * bs_per_gpu,
(gpu_id + 1) * bs_per_gpu)
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
gpu_warped_src = self.warped_src[batch_slice, :, :, :]
gpu_warped_dst = self.warped_dst[batch_slice, :, :, :]
gpu_target_src = self.target_src[batch_slice, :, :, :]
gpu_target_dst = self.target_dst[batch_slice, :, :, :]
gpu_target_srcm = self.target_srcm[
batch_slice, :, :, :]
gpu_target_dstm = self.target_dstm[
batch_slice, :, :, :]
# process model tensors
gpu_src_code = self.inter(self.encoder(gpu_warped_src))
gpu_dst_code = self.inter(self.encoder(gpu_warped_dst))
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(
gpu_src_code)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(
gpu_dst_code)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
gpu_dst_code)
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
gpu_target_srcm_blur = nn.gaussian_blur(
gpu_target_srcm, max(1, resolution // 32))
gpu_target_dstm_blur = nn.gaussian_blur(
gpu_target_dstm, max(1, resolution // 32))
gpu_target_dst_masked = gpu_target_dst * gpu_target_dstm_blur
gpu_target_dst_anti_masked = gpu_target_dst * (
1.0 - gpu_target_dstm_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_masked = gpu_pred_src_dst * gpu_target_dstm_blur
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst * (
1.0 - gpu_target_dstm_blur)
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])
gpu_src_loss += tf.reduce_mean(
10 * tf.square(gpu_target_src_masked_opt -
gpu_pred_src_src_masked_opt),
axis=[1, 2, 3])
gpu_src_loss += tf.reduce_mean(
10 * tf.square(gpu_target_srcm - gpu_pred_src_srcm),
axis=[1, 2, 3])
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])
gpu_dst_loss += tf.reduce_mean(
10 * tf.square(gpu_target_dst_masked_opt -
gpu_pred_dst_dst_masked_opt),
axis=[1, 2, 3])
gpu_dst_loss += tf.reduce_mean(
10 * tf.square(gpu_target_dstm - gpu_pred_dst_dstm),
axis=[1, 2, 3])
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_G_loss = gpu_src_loss + gpu_dst_loss
gpu_src_dst_loss_gvs += [
nn.gradients(gpu_G_loss,
self.src_dst_trainable_weights)
]
# Average losses and gradients, and create optimizer update ops
with tf.device(models_opt_device):
pred_src_src = nn.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = nn.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = nn.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
src_loss = nn.average_tensor_list(gpu_src_losses)
dst_loss = nn.average_tensor_list(gpu_dst_losses)
src_dst_loss_gv = nn.average_gv_list(gpu_src_dst_loss_gvs)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op(
src_dst_loss_gv)
# Initializing training and view functions
def src_dst_train(warped_src, target_src, target_srcm, \
warped_dst, target_dst, target_dstm):
s, d, _ = nn.tf_sess.run(
[src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={
self.warped_src: warped_src,
self.target_src: target_src,
self.target_srcm: target_srcm,
self.warped_dst: warped_dst,
self.target_dst: target_dst,
self.target_dstm: target_dstm,
})
s = np.mean(s)
d = np.mean(d)
return s, d
self.src_dst_train = src_dst_train
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run([
pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst,
pred_src_dstm
],
feed_dict={
self.warped_src: warped_src,
self.warped_dst: warped_dst
})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device(f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
def AE_merge(warped_dst):
return nn.tf_sess.run(
[gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm],
feed_dict={self.warped_dst: warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(
self.model_filename_list, "Initializing models"):
if self.pretrain_just_disabled:
do_init = False
if model == self.inter:
do_init = True
else:
do_init = self.is_first_run()
if not do_init:
do_init = not model.load_weights(
self.get_strpath_storage_for_file(filename))
if do_init and self.pretrained_model_path is not None:
pretrained_filepath = self.pretrained_model_path / filename
if pretrained_filepath.exists():
do_init = not model.load_weights(pretrained_filepath)
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
t = SampleProcessor.Types
face_type = t.FACE_TYPE_FULL
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path(
)
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path(
)
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
self.set_training_data_generators([
SampleGeneratorFace(
training_data_src_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True if self.pretrain else False),
output_sample_types=[{
'types':
(t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR),
'data_format':
nn.data_format,
'resolution':
resolution,
}, {
'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR),
'data_format':
nn.data_format,
'resolution':
resolution,
}, {
'types': (t.IMG_TRANSFORMED, face_type,
t.MODE_FACE_MASK_ALL_HULL),
'data_format':
nn.data_format,
'resolution':
resolution
}],
generators_count=src_generators_count),
SampleGeneratorFace(
training_data_dst_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True if self.pretrain else False),
output_sample_types=[{
'types':
(t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR),
'data_format':
nn.data_format,
'resolution':
resolution
}, {
'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR),
'data_format':
nn.data_format,
'resolution':
resolution
}, {
'types': (t.IMG_TRANSFORMED, face_type,
t.MODE_FACE_MASK_ALL_HULL),
'data_format':
nn.data_format,
'resolution':
resolution
}],
generators_count=dst_generators_count)
])
self.last_samples = None
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.model_data_format = "NCHW" if len(
devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
class EncBlock(nn.ModelBase):
def on_build(self, in_ch, out_ch, level):
self.zero_level = level == 0
self.conv1 = nn.Conv2D(in_ch,
out_ch,
kernel_size=3,
padding='SAME')
self.conv2 = nn.Conv2D(
out_ch,
out_ch,
kernel_size=4 if self.zero_level else 3,
padding='VALID' if self.zero_level else 'SAME')
def forward(self, x):
x = tf.nn.leaky_relu(self.conv1(x), 0.2)
x = tf.nn.leaky_relu(self.conv2(x), 0.2)
if not self.zero_level:
x = nn.max_pool(x)
#if self.zero_level:
return x
class DecBlock(nn.ModelBase):
def on_build(self, in_ch, out_ch, level):
self.zero_level = level == 0
self.conv1 = nn.Conv2D(
in_ch,
out_ch,
kernel_size=4 if self.zero_level else 3,
padding=3 if self.zero_level else 'SAME')
self.conv2 = nn.Conv2D(out_ch,
out_ch,
kernel_size=3,
padding='SAME')
def forward(self, x):
if not self.zero_level:
x = nn.upsample2d(x)
x = tf.nn.leaky_relu(self.conv1(x), 0.2)
x = tf.nn.leaky_relu(self.conv2(x), 0.2)
return x
class InterBlock(nn.ModelBase):
def on_build(self, in_ch, out_ch, level):
self.zero_level = level == 0
self.dense1 = nn.Dense()
def forward(self, x):
x = tf.nn.leaky_relu(self.conv1(x), 0.2)
x = tf.nn.leaky_relu(self.conv2(x), 0.2)
if not self.zero_level:
x = nn.max_pool(x)
#if self.zero_level:
return x
class FromRGB(nn.ModelBase):
def on_build(self, out_ch):
self.conv1 = nn.Conv2D(3,
out_ch,
kernel_size=1,
padding='SAME')
def forward(self, x):
return tf.nn.leaky_relu(self.conv1(x), 0.2)
class ToRGB(nn.ModelBase):
def on_build(self, in_ch):
self.conv = nn.Conv2D(in_ch, 3, kernel_size=1, padding='SAME')
self.convm = nn.Conv2D(in_ch, 1, kernel_size=1, padding='SAME')
def forward(self, x):
return tf.nn.sigmoid(self.conv(x)), tf.nn.sigmoid(
self.convm(x))
ed_dims = 16
ae_res = 4
level_chs = {
i - 1: v
for i, v in enumerate([
np.clip(ed_dims * (2**i), 0, 512)
for i in range(self.stage_max + 2)
][::-1])
}
ae_ch = level_chs[0]
class Encoder(nn.ModelBase):
def on_build(self, e_ch, levels):
self.enc_blocks = {}
self.from_rgbs = {}
self.dense_norm = nn.DenseNorm()
for level in range(levels, -1, -1):
self.from_rgbs[level] = FromRGB(level_chs[level])
if level != 0:
self.enc_blocks[level] = EncBlock(
level_chs[level], level_chs[level - 1], level)
self.ae_dense1 = nn.Dense(ae_res * ae_res * ae_ch, 256)
self.ae_dense2 = nn.Dense(256, ae_res * ae_res * ae_ch)
def forward(self, stage, inp, prev_inp=None, alpha=None):
x = inp
for level in range(stage, -1, -1):
if stage in self.from_rgbs:
if level == stage:
x = self.from_rgbs[level](x)
elif level == stage - 1:
x = x * alpha + self.from_rgbs[level](prev_inp) * (
1 - alpha)
if level != 0:
x = self.enc_blocks[level](x)
x = nn.flatten(x)
x = self.dense_norm(x)
x = self.ae_dense1(x)
x = self.ae_dense2(x)
x = nn.reshape_4D(x, ae_res, ae_res, ae_ch)
return x
def get_stage_weights(self, stage):
self.get_weights()
weights = []
for level in range(stage, -1, -1):
if stage in self.from_rgbs:
if level == stage or level == stage - 1:
weights.append(self.from_rgbs[level].get_weights())
if level != 0:
weights.append(
self.enc_blocks[level].get_weights())
weights.append(self.ae_dense1.get_weights())
weights.append(self.ae_dense2.get_weights())
if len(weights) == 0:
return []
elif len(weights) == 1:
return weights[0]
else:
return sum(weights[1:], weights[0])
class Decoder(nn.ModelBase):
def on_build(self, levels_range):
self.dec_blocks = {}
self.to_rgbs = {}
for level in range(levels_range[0], levels_range[1] + 1):
self.to_rgbs[level] = ToRGB(level_chs[level])
if level != 0:
self.dec_blocks[level] = DecBlock(
level_chs[level - 1], level_chs[level], level)
def forward(self, stage, inp, alpha=None, inter=None):
x = inp
for level in range(stage + 1):
if level in self.to_rgbs:
if level == stage and stage > 0:
prev_level = level - 1
#prev_x, prev_xm = (inter.to_rgbs[prev_level] if inter is not None and prev_level in inter.to_rgbs else self.to_rgbs[prev_level])(x)
prev_x, prev_xm = self.to_rgbs[prev_level](x)
prev_x = nn.upsample2d(prev_x)
prev_xm = nn.upsample2d(prev_xm)
if level != 0:
x = self.dec_blocks[level](x)
if level == stage:
x, xm = self.to_rgbs[level](x)
if stage > 0:
x = x * alpha + prev_x * (1 - alpha)
xm = xm * alpha + prev_xm * (1 - alpha)
return x, xm
return x
def get_stage_weights(self, stage):
# Call internal get_weights in order to initialize inner logic
self.get_weights()
weights = []
for level in range(stage + 1):
if level in self.to_rgbs:
if level != 0:
weights.append(
self.dec_blocks[level].get_weights())
if level == stage or level == stage - 1:
weights.append(self.to_rgbs[level].get_weights())
if len(weights) == 0:
return []
elif len(weights) == 1:
return weights[0]
else:
return sum(weights[1:], weights[0])
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.stage = stage = self.options['stage']
self.start_stage_iter = self.options.get('start_stage_iter', 0)
self.target_stage_iter = self.options.get('target_stage_iter', 0)
resolution = self.options['resolution']
stage_resolutions = [2**(i + 2) for i in range(self.stage_max + 1)]
stage_resolution = stage_resolutions[stage]
prev_stage = stage - 1 if stage != 0 else stage
prev_stage_resolution = stage_resolutions[
stage - 1] if stage != 0 else stage_resolution
self.pretrain = False
self.pretrain_just_disabled = False
masked_training = True
models_opt_on_gpu = len(devices) == 1 and devices[0].total_mem_gb >= 4
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device == '/CPU:0'
input_nc = 3
output_nc = 3
prev_bgr_shape = nn.get4Dshape(prev_stage_resolution,
prev_stage_resolution, output_nc)
bgr_shape = nn.get4Dshape(stage_resolution, stage_resolution,
output_nc)
mask_shape = nn.get4Dshape(stage_resolution, stage_resolution, 1)
self.model_filename_list = []
with tf.device('/CPU:0'):
#Place holders on CPU
self.prev_warped_src = tf.placeholder(tf.float32, prev_bgr_shape)
self.warped_src = tf.placeholder(tf.float32, bgr_shape)
self.prev_warped_dst = tf.placeholder(tf.float32, prev_bgr_shape)
self.warped_dst = tf.placeholder(tf.float32, bgr_shape)
self.target_src = tf.placeholder(tf.float32, bgr_shape)
self.target_dst = tf.placeholder(tf.float32, bgr_shape)
self.target_srcm = tf.placeholder(tf.float32, mask_shape)
self.target_dstm = tf.placeholder(tf.float32, mask_shape)
self.alpha_t = tf.placeholder(tf.float32, (None, 1, 1, 1))
# Initializing model classes
with tf.device(models_opt_device):
self.encoder = Encoder(e_ch=ed_dims,
levels=self.stage_max,
name='encoder')
#self.inter = Decoder(d_ch=ed_dims, total_levels=self.stage_max, levels_range=[0,2], name='inter')
self.decoder_src = Decoder(levels_range=[0, self.stage_max],
name='decoder_src')
self.decoder_dst = Decoder(levels_range=[0, self.stage_max],
name='decoder_dst')
self.model_filename_list += [
[self.encoder, 'encoder.npy'],
#[self.inter, 'inter.npy' ],
[self.decoder_src, 'decoder_src.npy'],
[self.decoder_dst, 'decoder_dst.npy']
]
if self.is_training:
self.src_dst_all_weights = self.encoder.get_weights(
) + self.decoder_src.get_weights(
) + self.decoder_dst.get_weights()
self.src_dst_trainable_weights = self.encoder.get_stage_weights(stage) \
+ self.decoder_src.get_stage_weights(stage) \
+ self.decoder_dst.get_stage_weights(stage)
# Initialize optimizers
self.src_dst_opt = nn.RMSprop(lr=2e-4,
lr_dropout=1.0,
name='src_dst_opt')
self.src_dst_opt.initialize_variables(
self.src_dst_all_weights,
vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [(self.src_dst_opt,
'src_dst_opt.npy')]
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices))
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size(gpu_count * bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device(
f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0'):
batch_slice = slice(gpu_id * bs_per_gpu,
(gpu_id + 1) * bs_per_gpu)
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
gpu_prev_warped_src = self.prev_warped_src[
batch_slice, :, :, :]
gpu_warped_src = self.warped_src[batch_slice, :, :, :]
gpu_prev_warped_dst = self.prev_warped_dst[
batch_slice, :, :, :]
gpu_warped_dst = self.warped_dst[batch_slice, :, :, :]
gpu_target_src = self.target_src[batch_slice, :, :, :]
gpu_target_dst = self.target_dst[batch_slice, :, :, :]
gpu_target_srcm = self.target_srcm[
batch_slice, :, :, :]
gpu_target_dstm = self.target_dstm[
batch_slice, :, :, :]
gpu_alpha_t = self.alpha_t[batch_slice, :, :, :]
# process model tensors
#gpu_src_code = self.inter(stage, self.encoder(stage, gpu_warped_src, gpu_prev_warped_src, gpu_alpha_t), gpu_alpha_t )
#gpu_dst_code = self.inter(stage, self.encoder(stage, gpu_warped_dst, gpu_prev_warped_dst, gpu_alpha_t), gpu_alpha_t )
gpu_src_code = self.encoder(stage, gpu_warped_src,
gpu_prev_warped_src,
gpu_alpha_t)
gpu_dst_code = self.encoder(stage, gpu_warped_dst,
gpu_prev_warped_dst,
gpu_alpha_t)
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(
stage, gpu_src_code, gpu_alpha_t) #, inter=self.inter)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(
stage, gpu_dst_code, gpu_alpha_t) #, inter=self.inter)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
stage, gpu_dst_code, gpu_alpha_t) #, inter=self.inter)
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
gpu_target_srcm_blur = nn.gaussian_blur(
gpu_target_srcm, max(1, stage_resolution // 32))
gpu_target_dstm_blur = nn.gaussian_blur(
gpu_target_dstm, max(1, stage_resolution // 32))
gpu_target_dst_masked = gpu_target_dst * gpu_target_dstm_blur
gpu_target_dst_anti_masked = gpu_target_dst * (
1.0 - gpu_target_dstm_blur)
gpu_target_srcmasked_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_masked = gpu_pred_src_dst * gpu_target_dstm_blur
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst * (
1.0 - gpu_target_dstm_blur)
gpu_src_loss = tf.reduce_mean(
10 * tf.square(gpu_target_srcmasked_opt -
gpu_pred_src_src_masked_opt),
axis=[1, 2, 3])
gpu_src_loss += tf.reduce_mean(
tf.square(gpu_target_srcm - gpu_pred_src_srcm),
axis=[1, 2, 3])
if stage_resolution >= 16:
gpu_src_loss += tf.reduce_mean(
5 *
nn.dssim(gpu_target_srcmasked_opt,
gpu_pred_src_src_masked_opt,
max_val=1.0,
filter_size=int(stage_resolution / 11.6)),
axis=[1])
if stage_resolution >= 32:
gpu_src_loss += tf.reduce_mean(
5 *
nn.dssim(gpu_target_srcmasked_opt,
gpu_pred_src_src_masked_opt,
max_val=1.0,
filter_size=int(stage_resolution / 23.2)),
axis=[1])
gpu_dst_loss = tf.reduce_mean(
10 * tf.square(gpu_target_dst_masked_opt -
gpu_pred_dst_dst_masked_opt),
axis=[1, 2, 3])
gpu_dst_loss += tf.reduce_mean(
tf.square(gpu_target_dstm - gpu_pred_dst_dstm),
axis=[1, 2, 3])
if stage_resolution >= 16:
gpu_dst_loss += tf.reduce_mean(
5 *
nn.dssim(gpu_target_dst_masked_opt,
gpu_pred_dst_dst_masked_opt,
max_val=1.0,
filter_size=int(stage_resolution / 11.6)),
axis=[1])
if stage_resolution >= 32:
gpu_dst_loss += tf.reduce_mean(
5 *
nn.dssim(gpu_target_dst_masked_opt,
gpu_pred_dst_dst_masked_opt,
max_val=1.0,
filter_size=int(stage_resolution / 23.2)),
axis=[1])
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_src_dst_loss = gpu_src_loss + gpu_dst_loss
gpu_src_dst_loss_gvs += [
nn.gradients(gpu_src_dst_loss,
self.src_dst_trainable_weights)
]
# Average losses and gradients, and create optimizer update ops
with tf.device(models_opt_device):
if gpu_count == 1:
pred_src_src = gpu_pred_src_src_list[0]
pred_dst_dst = gpu_pred_dst_dst_list[0]
pred_src_dst = gpu_pred_src_dst_list[0]
pred_src_srcm = gpu_pred_src_srcm_list[0]
pred_dst_dstm = gpu_pred_dst_dstm_list[0]
pred_src_dstm = gpu_pred_src_dstm_list[0]
src_loss = gpu_src_losses[0]
dst_loss = gpu_dst_losses[0]
src_dst_loss_gv = gpu_src_dst_loss_gvs[0]
else:
pred_src_src = tf.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = tf.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = tf.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = tf.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = tf.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = tf.concat(gpu_pred_src_dstm_list, 0)
src_loss = nn.average_tensor_list(gpu_src_losses)
dst_loss = nn.average_tensor_list(gpu_dst_losses)
src_dst_loss_gv = nn.average_gv_list(gpu_src_dst_loss_gvs)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op(
src_dst_loss_gv)
# Initializing training and view functions
def get_alpha(batch_size):
alpha = 0
if self.stage != 0:
alpha = (self.iter - self.start_stage_iter) / (
self.target_stage_iter - self.start_stage_iter)
alpha = np.clip(alpha, 0, 1)
alpha = np.array([alpha], nn.floatx.as_numpy_dtype).reshape(
(1, 1, 1, 1))
alpha = np.repeat(alpha, batch_size, 0)
return alpha
def src_dst_train(prev_warped_src, warped_src, target_src, target_srcm, \
prev_warped_dst, warped_dst, target_dst, target_dstm):
s, d, _ = nn.tf_sess.run(
[src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={
self.prev_warped_src: prev_warped_src,
self.warped_src: warped_src,
self.target_src: target_src,
self.target_srcm: target_srcm,
self.prev_warped_dst: prev_warped_dst,
self.warped_dst: warped_dst,
self.target_dst: target_dst,
self.target_dstm: target_dstm,
self.alpha_t: get_alpha(prev_warped_src.shape[0])
})
s = np.mean(s)
d = np.mean(d)
return s, d
self.src_dst_train = src_dst_train
def AE_view(prev_warped_src, warped_src, prev_warped_dst,
warped_dst):
return nn.tf_sess.run(
[
pred_src_src, pred_dst_dst, pred_dst_dstm,
pred_src_dst, pred_src_dstm
],
feed_dict={
self.prev_warped_src: prev_warped_src,
self.warped_src: warped_src,
self.prev_warped_dst: prev_warped_dst,
self.warped_dst: warped_dst,
self.alpha_t: get_alpha(prev_warped_src.shape[0])
})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device(f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
gpu_dst_code, stage=stage)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code,
stage=stage)
def AE_merge(warped_dst):
return nn.tf_sess.run(
[gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm],
feed_dict={self.warped_dst: warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(
self.model_filename_list, "Initializing models"):
do_init = self.is_first_run()
if self.pretrain_just_disabled:
if model == self.inter:
do_init = True
if not do_init:
do_init = not model.load_weights(
self.get_strpath_storage_for_file(filename))
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
self.face_type = FaceType.FULL
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path(
)
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path(
)
cpu_count = multiprocessing.cpu_count()
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count - src_generators_count
self.set_training_data_generators([
SampleGeneratorFace(
training_data_src_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=False),
output_sample_types=[
{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp': True,
'transform': True,
'channel_type': SampleProcessor.ChannelType.BGR,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution,
'nearest_resize_to': prev_stage_resolution
},
{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp': True,
'transform': True,
'channel_type': SampleProcessor.ChannelType.BGR,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution,
'nearest_resize_to': stage_resolution
},
{
'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,
'nearest_resize_to': prev_stage_resolution
},
{
'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,
'nearest_resize_to': stage_resolution
},
{
'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,
'nearest_resize_to': stage_resolution
},
],
generators_count=src_generators_count),
SampleGeneratorFace(
training_data_dst_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=False),
output_sample_types=[
{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp': True,
'transform': True,
'channel_type': SampleProcessor.ChannelType.BGR,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution,
'nearest_resize_to': prev_stage_resolution
},
{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp': True,
'transform': True,
'channel_type': SampleProcessor.ChannelType.BGR,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution,
'nearest_resize_to': stage_resolution
},
{
'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,
'nearest_resize_to': prev_stage_resolution
},
{
'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,
'nearest_resize_to': stage_resolution
},
{
'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,
'nearest_resize_to': stage_resolution
},
],
generators_count=dst_generators_count)
])
self.last_samples = None
def on_initialize_options(self):
device_config = nn.getCurrentDeviceConfig()
lowest_vram = 2
if len(device_config.devices) != 0:
lowest_vram = device_config.devices.get_worst_device().total_mem_gb
suggest_batch_size = 4
yn_str = {True: 'y', False: 'n'}
default_models_opt_on_gpu = self.options[
'models_opt_on_gpu'] = self.load_or_def_option(
'models_opt_on_gpu', False)
default_ae_dims = self.options['ae_dims'] = self.load_or_def_option(
'ae_dims', 256)
default_e_dims = self.options['e_dims'] = self.load_or_def_option(
'e_dims', 64)
default_d_dims = self.options['d_dims'] = self.load_or_def_option(
'd_dims', 64)
default_d_mask_dims = self.options[
'd_mask_dims'] = self.load_or_def_option('d_mask_dims', 16)
default_gan_power = self.options[
'gan_power'] = self.load_or_def_option('gan_power', 0.0)
default_clipgrad = self.options['clipgrad'] = self.load_or_def_option(
'clipgrad', False)
ask_override = self.ask_override()
if self.is_first_run() or ask_override:
self.ask_autobackup_hour()
self.ask_write_preview_history()
self.ask_target_iter()
self.ask_batch_size(suggest_batch_size)
if self.is_first_run():
self.options['ae_dims'] = np.clip(
io.input_int(
"AutoEncoder dimensions",
default_ae_dims,
add_info="32-1024",
help_message=
"All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU."
), 32, 1024)
e_dims = np.clip(
io.input_int(
"Encoder dimensions",
default_e_dims,
add_info="16-256",
help_message=
"More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU."
), 16, 256)
self.options['e_dims'] = e_dims + e_dims % 2
d_dims = np.clip(
io.input_int(
"Decoder dimensions",
default_d_dims,
add_info="16-256",
help_message=
"More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU."
), 16, 256)
self.options['d_dims'] = d_dims + d_dims % 2
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:
if len(device_config.devices) == 1:
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."
)
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. Accelerates the speed of training. Forces the neural network to learn small details of the face. You can enable/disable this option at any time. Typical value is 1.0"
), 0.0, 10.0)
self.options['clipgrad'] = io.input_bool(
"Enable gradient clipping",
default_clipgrad,
help_message=
"Gradient clipping reduces chance of model collapse, sacrificing speed of training."
)
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
self.model_data_format = "NCHW" if len(
device_config.devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
conv_kernel_initializer = nn.initializers.ca()
class Downscale(nn.ModelBase):
def __init__(self,
in_ch,
out_ch,
kernel_size=5,
dilations=1,
subpixel=True,
use_activator=True,
*kwargs):
self.in_ch = in_ch
self.out_ch = out_ch
self.kernel_size = kernel_size
self.dilations = dilations
self.subpixel = subpixel
self.use_activator = use_activator
super().__init__(*kwargs)
def on_build(self, *args, **kwargs):
self.conv1 = nn.Conv2D(
self.in_ch,
self.out_ch // (4 if self.subpixel else 1),
kernel_size=self.kernel_size,
strides=1 if self.subpixel else 2,
padding='SAME',
dilations=self.dilations,
kernel_initializer=conv_kernel_initializer)
def forward(self, x):
x = self.conv1(x)
if self.subpixel:
x = nn.space_to_depth(x, 2)
if self.use_activator:
x = tf.nn.leaky_relu(x, 0.1)
return x
def get_out_ch(self):
return (self.out_ch // 4) * 4
class DownscaleBlock(nn.ModelBase):
def on_build(self,
in_ch,
ch,
n_downscales,
kernel_size,
dilations=1,
subpixel=True):
self.downs = []
last_ch = in_ch
for i in range(n_downscales):
cur_ch = ch * (min(2**i, 8))
self.downs.append(
Downscale(last_ch,
cur_ch,
kernel_size=kernel_size,
dilations=dilations,
subpixel=subpixel))
last_ch = self.downs[-1].get_out_ch()
def forward(self, inp):
x = inp
for down in self.downs:
x = down(x)
return x
class Upscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=3):
self.conv1 = nn.Conv2D(
in_ch,
out_ch * 4,
kernel_size=kernel_size,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
def forward(self, x):
x = self.conv1(x)
x = tf.nn.leaky_relu(x, 0.1)
x = nn.depth_to_space(x, 2)
return x
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3):
self.conv1 = nn.Conv2D(
ch,
ch,
kernel_size=kernel_size,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
self.conv2 = nn.Conv2D(
ch,
ch,
kernel_size=kernel_size,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
def forward(self, inp):
x = self.conv1(inp)
x = tf.nn.leaky_relu(x, 0.2)
x = self.conv2(x)
x = tf.nn.leaky_relu(inp + x, 0.2)
return x
class UpdownResidualBlock(nn.ModelBase):
def on_build(self, ch, inner_ch, kernel_size=3):
self.up = Upscale(ch, inner_ch, kernel_size=kernel_size)
self.res = ResidualBlock(inner_ch, kernel_size=kernel_size)
self.down = Downscale(inner_ch,
ch,
kernel_size=kernel_size,
use_activator=False)
def forward(self, inp):
x = self.up(inp)
x = upx = self.res(x)
x = self.down(x)
x = x + inp
x = tf.nn.leaky_relu(x, 0.2)
return x, upx
class Encoder(nn.ModelBase):
def on_build(self, in_ch, e_ch):
self.down1 = DownscaleBlock(in_ch,
e_ch,
n_downscales=5,
kernel_size=5,
dilations=1,
subpixel=False)
def forward(self, inp):
x = nn.flatten(self.down1(inp))
return x
class Inter(nn.ModelBase):
def __init__(self, in_ch, lowest_dense_res, ae_ch, ae_out_ch,
**kwargs):
self.in_ch, self.lowest_dense_res, self.ae_ch, self.ae_out_ch = in_ch, lowest_dense_res, ae_ch, ae_out_ch
super().__init__(**kwargs)
def on_build(self):
in_ch, lowest_dense_res, ae_ch, ae_out_ch = self.in_ch, self.lowest_dense_res, self.ae_ch, self.ae_out_ch
self.dense1 = nn.Dense(in_ch, ae_ch)
self.dense2 = nn.Dense(
ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch)
self.upscale1 = Upscale(ae_out_ch, ae_out_ch * 2)
def forward(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
x = nn.reshape_4D(x, lowest_dense_res, lowest_dense_res,
self.ae_out_ch)
x = self.upscale1(x)
return x
def get_out_ch(self):
return self.ae_out_ch
class Decoder(nn.ModelBase):
def on_build(self, in_ch, d_ch, d_mask_ch):
self.upscale0 = Upscale(in_ch, d_ch * 8, kernel_size=3)
self.upscale1 = Upscale(d_ch * 8, d_ch * 4, kernel_size=3)
self.upscale2 = Upscale(d_ch * 4, d_ch * 2, kernel_size=3)
self.upscale3 = Upscale(d_ch * 2, d_ch * 1, kernel_size=3)
self.res0 = ResidualBlock(d_ch * 8, kernel_size=3)
self.res1 = ResidualBlock(d_ch * 4, kernel_size=3)
self.res2 = ResidualBlock(d_ch * 2, kernel_size=3)
self.res3 = ResidualBlock(d_ch * 1, kernel_size=3)
self.out_conv = nn.Conv2D(
d_ch * 1,
3,
kernel_size=1,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
self.upscalem0 = Upscale(in_ch, d_mask_ch * 8, kernel_size=3)
self.upscalem1 = Upscale(d_mask_ch * 8,
d_mask_ch * 4,
kernel_size=3)
self.upscalem2 = Upscale(d_mask_ch * 4,
d_mask_ch * 2,
kernel_size=3)
self.upscalem3 = Upscale(d_mask_ch * 2,
d_mask_ch * 1,
kernel_size=3)
self.out_convm = nn.Conv2D(
d_mask_ch * 1,
1,
kernel_size=1,
padding='SAME',
kernel_initializer=conv_kernel_initializer)
"""
def get_weights_ex(self, include_mask):
# Call internal get_weights in order to initialize inner logic
self.get_weights()
weights = self.upscale0.get_weights() + self.upscale1.get_weights() + self.upscale2.get_weights() \
+ self.res0.get_weights() + self.res1.get_weights() + self.res2.get_weights() + self.out_conv.get_weights()
if include_mask:
weights += self.upscalem0.get_weights() + self.upscalem1.get_weights() + self.upscalem2.get_weights() \
+ self.out_convm.get_weights()
return weights
"""
def get_weights_ex(self, include_mask):
# Call internal get_weights in order to initialize inner logic
self.get_weights()
weights = self.upscale0.get_weights() + self.upscale1.get_weights() + self.upscale2.get_weights() + self.upscale3.get_weights()\
+ self.res0.get_weights() + self.res1.get_weights() + self.res2.get_weights() + self.res3.get_weights() + self.out_conv.get_weights()
if include_mask:
weights += self.upscalem0.get_weights() + self.upscalem1.get_weights() + self.upscalem2.get_weights() + self.upscalem3.get_weights() \
+ self.out_convm.get_weights()
return weights
def forward(self, inp):
z = inp
x = self.upscale0(z)
x = self.res0(x)
x = self.upscale1(x)
x = self.res1(x)
x = self.upscale2(x)
x = self.res2(x)
x = self.upscale3(x)
x = self.res3(x)
m = self.upscalem0(z)
m = self.upscalem1(m)
m = self.upscalem2(m)
m = self.upscalem3(m)
return tf.nn.sigmoid(self.out_conv(x)), \
tf.nn.sigmoid(self.out_convm(m))
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.resolution = resolution = 448
self.learn_mask = learn_mask = True
eyes_prio = True
ae_dims = self.options['ae_dims']
e_dims = self.options['e_dims']
d_dims = self.options['d_dims']
d_mask_dims = self.options['d_mask_dims']
self.pretrain = False
self.pretrain_just_disabled = False
if self.pretrain_just_disabled:
self.set_iter(0)
self.gan_power = gan_power = self.options[
'gan_power'] if not self.pretrain else 0.0
masked_training = True
models_opt_on_gpu = False if len(devices) == 0 else True if len(
devices) > 1 else self.options['models_opt_on_gpu']
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device == '/CPU:0'
input_ch = 3
output_ch = 3
bgr_shape = nn.get4Dshape(resolution, resolution, input_ch)
mask_shape = nn.get4Dshape(resolution, resolution, 1)
lowest_dense_res = resolution // 32
self.model_filename_list = []
with tf.device('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder(nn.floatx, bgr_shape)
self.warped_dst = tf.placeholder(nn.floatx, bgr_shape)
self.target_src = tf.placeholder(nn.floatx, bgr_shape)
self.target_dst = tf.placeholder(nn.floatx, bgr_shape)
self.target_srcm_all = tf.placeholder(nn.floatx, mask_shape)
self.target_dstm_all = tf.placeholder(nn.floatx, mask_shape)
# Initializing model classes
with tf.device(models_opt_device):
self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, name='encoder')
encoder_out_ch = self.encoder.compute_output_channels(
(nn.floatx, bgr_shape))
self.inter = Inter(in_ch=encoder_out_ch,
lowest_dense_res=lowest_dense_res,
ae_ch=ae_dims,
ae_out_ch=ae_dims,
name='inter')
inter_out_ch = self.inter.compute_output_channels(
(nn.floatx, (None, encoder_out_ch)))
self.decoder_src = Decoder(in_ch=inter_out_ch,
d_ch=d_dims,
d_mask_ch=d_mask_dims,
name='decoder_src')
self.decoder_dst = Decoder(in_ch=inter_out_ch,
d_ch=d_dims,
d_mask_ch=d_mask_dims,
name='decoder_dst')
self.model_filename_list += [[self.encoder, 'encoder.npy'],
[self.inter, 'inter.npy'],
[self.decoder_src, 'decoder_src.npy'],
[self.decoder_dst, 'decoder_dst.npy']]
if self.is_training:
if gan_power != 0:
self.D_src = nn.PatchDiscriminator(patch_size=resolution //
16,
in_ch=output_ch,
base_ch=256,
name="D_src")
self.D_dst = nn.PatchDiscriminator(patch_size=resolution //
16,
in_ch=output_ch,
base_ch=256,
name="D_dst")
self.model_filename_list += [[self.D_src, 'D_src.npy']]
self.model_filename_list += [[self.D_dst, 'D_dst.npy']]
# Initialize optimizers
lr = 5e-5
clipnorm = 1.0 if self.options['clipgrad'] else 0.0
self.src_dst_opt = nn.RMSprop(lr=lr,
clipnorm=clipnorm,
name='src_dst_opt')
self.model_filename_list += [(self.src_dst_opt,
'src_dst_opt.npy')]
self.src_dst_all_trainable_weights = self.encoder.get_weights(
) + self.inter.get_weights() + self.decoder_src.get_weights(
) + self.decoder_dst.get_weights()
self.src_dst_trainable_weights = self.encoder.get_weights(
) + self.inter.get_weights() + self.decoder_src.get_weights_ex(
learn_mask) + self.decoder_dst.get_weights_ex(learn_mask)
self.src_dst_opt.initialize_variables(
self.src_dst_all_trainable_weights,
vars_on_cpu=optimizer_vars_on_cpu)
if gan_power != 0:
self.D_src_dst_opt = nn.RMSprop(lr=lr,
clipnorm=clipnorm,
name='D_src_dst_opt')
self.D_src_dst_opt.initialize_variables(
self.D_src.get_weights() + self.D_dst.get_weights(),
vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [(self.D_src_dst_opt,
'D_src_dst_opt.npy')]
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices))
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size(gpu_count * bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_G_loss_gvs = []
gpu_D_code_loss_gvs = []
gpu_D_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device(
f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0'):
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
batch_slice = slice(gpu_id * bs_per_gpu,
(gpu_id + 1) * bs_per_gpu)
gpu_warped_src = self.warped_src[batch_slice, :, :, :]
gpu_warped_dst = self.warped_dst[batch_slice, :, :, :]
gpu_target_src = self.target_src[batch_slice, :, :, :]
gpu_target_dst = self.target_dst[batch_slice, :, :, :]
gpu_target_srcm_all = self.target_srcm_all[
batch_slice, :, :, :]
gpu_target_dstm_all = self.target_dstm_all[
batch_slice, :, :, :]
# process model tensors
gpu_src_code = self.inter(self.encoder(gpu_warped_src))
gpu_dst_code = self.inter(self.encoder(gpu_warped_dst))
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(
gpu_src_code)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(
gpu_dst_code)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
gpu_dst_code)
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
# unpack masks from one combined mask
gpu_target_srcm = tf.clip_by_value(gpu_target_srcm_all, 0,
1)
gpu_target_dstm = tf.clip_by_value(gpu_target_dstm_all, 0,
1)
gpu_target_srcm_eyes = tf.clip_by_value(
gpu_target_srcm_all - 1, 0, 1)
gpu_target_dstm_eyes = tf.clip_by_value(
gpu_target_dstm_all - 1, 0, 1)
gpu_target_srcm_blur = nn.gaussian_blur(
gpu_target_srcm, max(1, resolution // 32))
gpu_target_dstm_blur = nn.gaussian_blur(
gpu_target_dstm, max(1, resolution // 32))
gpu_target_dst_masked = gpu_target_dst * gpu_target_dstm_blur
gpu_target_dst_anti_masked = gpu_target_dst * (
1.0 - gpu_target_dstm_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_masked = gpu_pred_src_dst * gpu_target_dstm_blur
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst * (
1.0 - gpu_target_dstm_blur)
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])
gpu_src_loss += tf.reduce_mean(
10 * tf.square(gpu_target_src_masked_opt -
gpu_pred_src_src_masked_opt),
axis=[1, 2, 3])
if eyes_prio:
gpu_src_loss += tf.reduce_mean(
300 *
tf.abs(gpu_target_src * gpu_target_srcm_eyes -
gpu_pred_src_src * gpu_target_srcm_eyes),
axis=[1, 2, 3])
if learn_mask:
gpu_src_loss += tf.reduce_mean(
10 *
tf.square(gpu_target_srcm - gpu_pred_src_srcm),
axis=[1, 2, 3])
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])
gpu_dst_loss += tf.reduce_mean(
10 * tf.square(gpu_target_dst_masked_opt -
gpu_pred_dst_dst_masked_opt),
axis=[1, 2, 3])
if eyes_prio:
gpu_dst_loss += tf.reduce_mean(
300 *
tf.abs(gpu_target_dst * gpu_target_dstm_eyes -
gpu_pred_dst_dst * gpu_target_dstm_eyes),
axis=[1, 2, 3])
if learn_mask:
gpu_dst_loss += tf.reduce_mean(
10 *
tf.square(gpu_target_dstm - gpu_pred_dst_dstm),
axis=[1, 2, 3])
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_G_loss = gpu_src_loss + gpu_dst_loss
def DLoss(labels, logits):
return tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=labels, logits=logits),
axis=[1, 2, 3])
if gan_power != 0:
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_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_dst_dst_d = self.D_dst(
gpu_pred_dst_dst_masked_opt)
gpu_pred_dst_dst_d_ones = tf.ones_like(
gpu_pred_dst_dst_d)
gpu_pred_dst_dst_d_zeros = tf.zeros_like(
gpu_pred_dst_dst_d)
gpu_target_dst_d = self.D_dst(
gpu_target_dst_masked_opt)
gpu_target_dst_d_ones = tf.ones_like(gpu_target_dst_d)
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_dst_d_ones , gpu_target_dst_d) + \
DLoss(gpu_pred_dst_dst_d_zeros, gpu_pred_dst_dst_d) ) * 0.5
gpu_D_src_dst_loss_gvs += [
nn.gradients(
gpu_D_src_dst_loss,
self.D_src.get_weights() +
self.D_dst.get_weights())
]
gpu_G_loss += gan_power * (
DLoss(gpu_pred_src_src_d_ones,
gpu_pred_src_src_d) +
DLoss(gpu_pred_dst_dst_d_ones, gpu_pred_dst_dst_d))
gpu_G_loss_gvs += [
nn.gradients(gpu_G_loss,
self.src_dst_trainable_weights)
]
# Average losses and gradients, and create optimizer update ops
with tf.device(models_opt_device):
pred_src_src = nn.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = nn.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = nn.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
src_loss = nn.average_tensor_list(gpu_src_losses)
dst_loss = nn.average_tensor_list(gpu_dst_losses)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op(
nn.average_gv_list(gpu_G_loss_gvs))
if gan_power != 0:
src_D_src_dst_loss_gv_op = self.D_src_dst_opt.get_update_op(
nn.average_gv_list(gpu_D_src_dst_loss_gvs))
# Initializing training and view functions
def src_dst_train(warped_src, target_src, target_srcm_all, \
warped_dst, target_dst, target_dstm_all):
s, d, _ = nn.tf_sess.run(
[src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={
self.warped_src: warped_src,
self.target_src: target_src,
self.target_srcm_all: target_srcm_all,
self.warped_dst: warped_dst,
self.target_dst: target_dst,
self.target_dstm_all: target_dstm_all,
})
s = np.mean(s)
d = np.mean(d)
return s, d
self.src_dst_train = src_dst_train
if gan_power != 0:
def D_src_dst_train(warped_src, target_src, target_srcm_all, \
warped_dst, target_dst, target_dstm_all):
nn.tf_sess.run(
[src_D_src_dst_loss_gv_op],
feed_dict={
self.warped_src: warped_src,
self.target_src: target_src,
self.target_srcm_all: target_srcm_all,
self.warped_dst: warped_dst,
self.target_dst: target_dst,
self.target_dstm_all: target_dstm_all
})
self.D_src_dst_train = D_src_dst_train
if learn_mask:
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run([
pred_src_src, pred_dst_dst, pred_dst_dstm,
pred_src_dst, pred_src_dstm
],
feed_dict={
self.warped_src: warped_src,
self.warped_dst: warped_dst
})
else:
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run(
[pred_src_src, pred_dst_dst, pred_src_dst],
feed_dict={
self.warped_src: warped_src,
self.warped_dst: warped_dst
})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device(f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
if learn_mask:
def AE_merge(warped_dst):
return nn.tf_sess.run([
gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm
],
feed_dict={
self.warped_dst: warped_dst
})
else:
def AE_merge(warped_dst):
return nn.tf_sess.run(
[gpu_pred_src_dst],
feed_dict={self.warped_dst: warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(
self.model_filename_list, "Initializing models"):
if self.pretrain_just_disabled:
do_init = False
if model == self.inter:
do_init = True
else:
do_init = self.is_first_run()
if not do_init:
do_init = not model.load_weights(
self.get_strpath_storage_for_file(filename))
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
t = SampleProcessor.Types
face_type = t.FACE_TYPE_HEAD
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path(
)
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path(
)
t_img_warped = t.IMG_WARPED_TRANSFORMED
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
self.set_training_data_generators([
SampleGeneratorFace(
training_data_src_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=False),
output_sample_types=[
{
'types': (t_img_warped, face_type, t.MODE_BGR),
'data_format': nn.data_format,
'resolution': resolution
},
{
'types':
(t.IMG_TRANSFORMED, face_type, t.MODE_BGR),
'data_format': nn.data_format,
'resolution': resolution
},
{
'types': (t.IMG_TRANSFORMED, face_type,
t.MODE_FACE_MASK_ALL_EYES_HULL),
'data_format':
nn.data_format,
'resolution':
resolution
},
],
generators_count=src_generators_count),
SampleGeneratorFace(
training_data_dst_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=False),
output_sample_types=[
{
'types': (t_img_warped, face_type, t.MODE_BGR),
'data_format': nn.data_format,
'resolution': resolution
},
{
'types':
(t.IMG_TRANSFORMED, face_type, t.MODE_BGR),
'data_format': nn.data_format,
'resolution': resolution
},
{
'types': (t.IMG_TRANSFORMED, face_type,
t.MODE_FACE_MASK_ALL_EYES_HULL),
'data_format':
nn.data_format,
'resolution':
resolution
},
],
generators_count=dst_generators_count)
])
if self.pretrain_just_disabled:
self.update_sample_for_preview(force_new=True)
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.model_data_format = "NCHW"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
input_ch=3
resolution = self.resolution = self.options['resolution']
e_dims = self.options['e_dims']
ae_dims = self.options['ae_dims']
inter_dims = self.inter_dims = self.options['inter_dims']
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'] ]
morph_factor = self.options['morph_factor']
gan_power = self.gan_power = self.options['gan_power']
random_warp = self.options['random_warp']
blur_out_mask = self.options['blur_out_mask']
ct_mode = self.options['ct_mode']
if ct_mode == 'none':
ct_mode = None
use_fp16 = False
if self.is_exporting:
use_fp16 = io.input_bool ("Export quantized?", False, help_message='Makes the exported model faster. If you have problems, disable this option.')
conv_dtype = tf.float16 if use_fp16 else tf.float32
class Downscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=5 ):
self.conv1 = nn.Conv2D( in_ch, out_ch, kernel_size=kernel_size, strides=2, padding='SAME', dtype=conv_dtype)
def forward(self, x):
return tf.nn.leaky_relu(self.conv1(x), 0.1)
class Upscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=3 ):
self.conv1 = nn.Conv2D(in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
def forward(self, x):
x = nn.depth_to_space(tf.nn.leaky_relu(self.conv1(x), 0.1), 2)
return x
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3 ):
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
def forward(self, inp):
x = self.conv1(inp)
x = tf.nn.leaky_relu(x, 0.2)
x = self.conv2(x)
x = tf.nn.leaky_relu(inp+x, 0.2)
return x
class Encoder(nn.ModelBase):
def on_build(self):
self.down1 = Downscale(input_ch, e_dims, kernel_size=5)
self.res1 = ResidualBlock(e_dims)
self.down2 = Downscale(e_dims, e_dims*2, kernel_size=5)
self.down3 = Downscale(e_dims*2, e_dims*4, kernel_size=5)
self.down4 = Downscale(e_dims*4, e_dims*8, kernel_size=5)
self.down5 = Downscale(e_dims*8, e_dims*8, kernel_size=5)
self.res5 = ResidualBlock(e_dims*8)
self.dense1 = nn.Dense( (( resolution//(2**5) )**2) * e_dims*8, ae_dims )
def forward(self, x):
if use_fp16:
x = tf.cast(x, tf.float16)
x = self.down1(x)
x = self.res1(x)
x = self.down2(x)
x = self.down3(x)
x = self.down4(x)
x = self.down5(x)
x = self.res5(x)
if use_fp16:
x = tf.cast(x, tf.float32)
x = nn.pixel_norm(nn.flatten(x), axes=-1)
x = self.dense1(x)
return x
class Inter(nn.ModelBase):
def on_build(self):
self.dense2 = nn.Dense(ae_dims, inter_res * inter_res * inter_dims)
def forward(self, inp):
x = inp
x = self.dense2(x)
x = nn.reshape_4D (x, inter_res, inter_res, inter_dims)
return x
class Decoder(nn.ModelBase):
def on_build(self ):
self.upscale0 = Upscale(inter_dims, d_dims*8, kernel_size=3)
self.upscale1 = Upscale(d_dims*8, d_dims*8, kernel_size=3)
self.upscale2 = Upscale(d_dims*8, d_dims*4, kernel_size=3)
self.upscale3 = Upscale(d_dims*4, d_dims*2, kernel_size=3)
self.res0 = ResidualBlock(d_dims*8, kernel_size=3)
self.res1 = ResidualBlock(d_dims*8, kernel_size=3)
self.res2 = ResidualBlock(d_dims*4, kernel_size=3)
self.res3 = ResidualBlock(d_dims*2, kernel_size=3)
self.upscalem0 = Upscale(inter_dims, d_mask_dims*8, kernel_size=3)
self.upscalem1 = Upscale(d_mask_dims*8, d_mask_dims*8, kernel_size=3)
self.upscalem2 = Upscale(d_mask_dims*8, d_mask_dims*4, kernel_size=3)
self.upscalem3 = Upscale(d_mask_dims*4, d_mask_dims*2, kernel_size=3)
self.upscalem4 = Upscale(d_mask_dims*2, d_mask_dims*1, kernel_size=3)
self.out_convm = nn.Conv2D( d_mask_dims*1, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
self.out_conv = nn.Conv2D( d_dims*2, 3, kernel_size=1, padding='SAME', dtype=conv_dtype)
self.out_conv1 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
self.out_conv2 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
self.out_conv3 = nn.Conv2D( d_dims*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
def forward(self, z):
if use_fp16:
z = tf.cast(z, tf.float16)
x = self.upscale0(z)
x = self.res0(x)
x = self.upscale1(x)
x = self.res1(x)
x = self.upscale2(x)
x = self.res2(x)
x = self.upscale3(x)
x = self.res3(x)
x = tf.nn.sigmoid( nn.depth_to_space(tf.concat( (self.out_conv(x),
self.out_conv1(x),
self.out_conv2(x),
self.out_conv3(x)), nn.conv2d_ch_axis), 2) )
m = self.upscalem0(z)
m = self.upscalem1(m)
m = self.upscalem2(m)
m = self.upscalem3(m)
m = self.upscalem4(m)
m = tf.nn.sigmoid(self.out_convm(m))
if use_fp16:
x = tf.cast(x, tf.float32)
m = tf.cast(m, tf.float32)
return x, m
models_opt_on_gpu = False if len(devices) == 0 else self.options['models_opt_on_gpu']
models_opt_device = nn.tf_default_device_name if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
bgr_shape = self.bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
mask_shape = nn.get4Dshape(resolution,resolution,1)
self.model_filename_list = []
with tf.device ('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder (nn.floatx, bgr_shape, name='warped_src')
self.warped_dst = tf.placeholder (nn.floatx, bgr_shape, name='warped_dst')
self.target_src = tf.placeholder (nn.floatx, bgr_shape, name='target_src')
self.target_dst = tf.placeholder (nn.floatx, bgr_shape, name='target_dst')
self.target_srcm = tf.placeholder (nn.floatx, mask_shape, name='target_srcm')
self.target_srcm_em = tf.placeholder (nn.floatx, mask_shape, name='target_srcm_em')
self.target_dstm = tf.placeholder (nn.floatx, mask_shape, name='target_dstm')
self.target_dstm_em = tf.placeholder (nn.floatx, mask_shape, name='target_dstm_em')
self.morph_value_t = tf.placeholder (nn.floatx, (1,), name='morph_value_t')
# Initializing model classes
with tf.device (models_opt_device):
self.encoder = Encoder(name='encoder')
self.inter_src = Inter(name='inter_src')
self.inter_dst = Inter(name='inter_dst')
self.decoder = Decoder(name='decoder')
self.model_filename_list += [ [self.encoder, 'encoder.npy'],
[self.inter_src, 'inter_src.npy'],
[self.inter_dst , 'inter_dst.npy'],
[self.decoder , 'decoder.npy'] ]
if self.is_training:
# Initialize optimizers
clipnorm = 1.0 if self.options['clipgrad'] else 0.0
lr_dropout = 0.3 if self.options['lr_dropout'] in ['y','cpu'] else 1.0
self.G_weights = self.encoder.get_weights() + self.decoder.get_weights()
#if random_warp:
# self.G_weights += self.inter_src.get_weights() + self.inter_dst.get_weights()
self.src_dst_opt = nn.AdaBelief(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'], name="GAN")
self.GAN_opt = nn.AdaBelief(lr=5e-5, lr_dropout=lr_dropout, 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.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices) )
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size( gpu_count*bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_G_loss_gradients = []
gpu_GAN_loss_gradients = []
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])
def DLossZeros(logits):
return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(logits), logits=logits), axis=[1,2,3])
for gpu_id in range(gpu_count):
with tf.device( f'/{devices[gpu_id].tf_dev_type}:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
gpu_warped_src = self.warped_src [batch_slice,:,:,:]
gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
gpu_target_src = self.target_src [batch_slice,:,:,:]
gpu_target_dst = self.target_dst [batch_slice,:,:,:]
gpu_target_srcm = self.target_srcm[batch_slice,:,:,:]
gpu_target_srcm_em = self.target_srcm_em[batch_slice,:,:,:]
gpu_target_dstm = self.target_dstm[batch_slice,:,:,:]
gpu_target_dstm_em = self.target_dstm_em[batch_slice,:,:,:]
# process model tensors
gpu_src_code = self.encoder (gpu_warped_src)
gpu_dst_code = self.encoder (gpu_warped_dst)
gpu_src_inter_src_code, gpu_src_inter_dst_code = self.inter_src (gpu_src_code), self.inter_dst (gpu_src_code)
gpu_dst_inter_src_code, gpu_dst_inter_dst_code = self.inter_src (gpu_dst_code), self.inter_dst (gpu_dst_code)
inter_dims_bin = int(inter_dims*morph_factor)
with tf.device(f'/CPU:0'):
inter_rnd_binomial = tf.stack([tf.random.shuffle(tf.concat([tf.tile(tf.constant([1], tf.float32), ( inter_dims_bin, )),
tf.tile(tf.constant([0], tf.float32), ( inter_dims-inter_dims_bin, ))], 0 )) for _ in range(bs_per_gpu)], 0)
inter_rnd_binomial = tf.stop_gradient(inter_rnd_binomial[...,None,None])
gpu_src_code = gpu_src_inter_src_code * inter_rnd_binomial + gpu_src_inter_dst_code * (1-inter_rnd_binomial)
gpu_dst_code = gpu_dst_inter_dst_code
inter_dims_slice = tf.cast(inter_dims*self.morph_value_t[0], tf.int32)
gpu_src_dst_code = tf.concat( (tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, inter_dims_slice , inter_res, inter_res]),
tf.slice(gpu_dst_inter_dst_code, [0,inter_dims_slice,0,0], [-1,inter_dims-inter_dims_slice, inter_res,inter_res]) ), 1 )
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_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
gpu_pred_src_src_list.append(gpu_pred_src_src), gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst), gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dst_list.append(gpu_pred_src_dst), gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
gpu_target_srcm_anti = 1-gpu_target_srcm
gpu_target_dstm_anti = 1-gpu_target_dstm
gpu_target_srcm_gblur = nn.gaussian_blur(gpu_target_srcm, resolution // 32)
gpu_target_dstm_gblur = nn.gaussian_blur(gpu_target_dstm, resolution // 32)
gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_gblur, 0, 0.5) * 2
gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_gblur, 0, 0.5) * 2
gpu_target_srcm_anti_blur = 1.0-gpu_target_srcm_blur
gpu_target_dstm_anti_blur = 1.0-gpu_target_dstm_blur
if blur_out_mask:
sigma = resolution / 128
x = nn.gaussian_blur(gpu_target_src*gpu_target_srcm_anti, sigma)
y = 1-nn.gaussian_blur(gpu_target_srcm, sigma)
y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
gpu_target_src = gpu_target_src*gpu_target_srcm + (x/y)*gpu_target_srcm_anti
x = nn.gaussian_blur(gpu_target_dst*gpu_target_dstm_anti, sigma)
y = 1-nn.gaussian_blur(gpu_target_dstm, sigma)
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_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])
# Eyes+mouth prio loss
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])
gpu_dst_loss += tf.reduce_mean (300*tf.abs (gpu_target_dst*gpu_target_dstm_em-gpu_pred_dst_dst*gpu_target_dstm_em), axis=[1,2,3])
# Mask loss
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_G_loss = gpu_src_loss + gpu_dst_loss
# dst-dst background weak loss
gpu_G_loss += tf.reduce_mean(0.1*tf.square(gpu_pred_dst_dst_anti_masked-gpu_target_dst_anti_masked),axis=[1,2,3] )
gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_dst_dst_anti_masked)
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)
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_gradients += [ nn.gradients ( gpu_G_loss, self.G_weights ) ]
# Average losses and gradients, and create optimizer update ops
with tf.device(f'/CPU:0'):
pred_src_src = nn.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = nn.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = nn.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
with tf.device (models_opt_device):
src_loss = tf.concat(gpu_src_losses, 0)
dst_loss = tf.concat(gpu_dst_losses, 0)
train_op = self.src_dst_opt.get_update_op (nn.average_gv_list (gpu_G_loss_gradients))
if gan_power != 0:
GAN_train_op = self.GAN_opt.get_update_op (nn.average_gv_list(gpu_GAN_loss_gradients) )
# Initializing training and view functions
def train(warped_src, target_src, target_srcm, target_srcm_em, \
warped_dst, target_dst, target_dstm, target_dstm_em, ):
s, d, _ = nn.tf_sess.run ([src_loss, dst_loss, train_op],
feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm:target_srcm,
self.target_srcm_em:target_srcm_em,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm:target_dstm,
self.target_dstm_em:target_dstm_em,
})
return s, d
self.train = train
if gan_power != 0:
def GAN_train(warped_src, target_src, target_srcm, target_srcm_em, \
warped_dst, target_dst, target_dstm, target_dstm_em, ):
nn.tf_sess.run ([GAN_train_op], feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm:target_srcm,
self.target_srcm_em:target_srcm_em,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm:target_dstm,
self.target_dstm_em:target_dstm_em})
self.GAN_train = GAN_train
def AE_view(warped_src, warped_dst, morph_value):
return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
feed_dict={self.warped_src:warped_src, self.warped_dst:warped_dst, self.morph_value_t:[morph_value] })
self.AE_view = AE_view
else:
#Initializing merge function
with tf.device( nn.tf_default_device_name if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.encoder (self.warped_dst)
gpu_dst_inter_src_code = self.inter_src (gpu_dst_code)
gpu_dst_inter_dst_code = self.inter_dst (gpu_dst_code)
inter_dims_slice = tf.cast(inter_dims*self.morph_value_t[0], tf.int32)
gpu_src_dst_code = tf.concat( ( tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, inter_dims_slice , inter_res, inter_res]),
tf.slice(gpu_dst_inter_dst_code, [0,inter_dims_slice,0,0], [-1,inter_dims-inter_dims_slice, inter_res,inter_res]) ), 1 )
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_inter_dst_code)
def AE_merge(warped_dst, morph_value):
return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst, self.morph_value_t:[morph_value] })
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
do_init = self.is_first_run()
if self.is_training and gan_power != 0 and model == self.GAN:
if self.gan_model_changed:
do_init = True
if not do_init:
do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
if do_init:
model.init_weights()
###############
# initializing sample generators
if self.is_training:
training_data_src_path = self.training_data_src_path #if not self.pretrain else self.get_pretraining_data_path()
training_data_dst_path = self.training_data_dst_path #if not self.pretrain else self.get_pretraining_data_path()
random_ct_samples_path=training_data_dst_path if ct_mode is not None else None #and not self.pretrain
cpu_count = multiprocessing.cpu_count()
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
if ct_mode is not None:
src_generators_count = int(src_generators_count * 1.5)
self.set_training_data_generators ([
SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=self.random_src_flip),
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},
{'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},
{'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},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':face_type, 'data_format':nn.data_format, 'resolution': resolution},
],
uniform_yaw_distribution=self.options['uniform_yaw'],# or self.pretrain,
generators_count=src_generators_count ),
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=self.random_dst_flip),
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},
{'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},
{'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},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':face_type, 'data_format':nn.data_format, 'resolution': resolution},
],
uniform_yaw_distribution=self.options['uniform_yaw'],# or self.pretrain,
generators_count=dst_generators_count )
])
self.last_src_samples_loss = []
self.last_dst_samples_loss = []
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.model_data_format = "NCHW" if len(devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
self.resolution = resolution = self.options['resolution']
self.face_type = {'h' : FaceType.HALF,
'mf' : FaceType.MID_FULL,
'f' : FaceType.FULL,
'wf' : FaceType.WHOLE_FACE}[ self.options['face_type'] ]
eyes_prio = self.options['eyes_prio']
archi = self.options['archi']
is_hd = 'hd' in archi
ae_dims = self.options['ae_dims']
e_dims = self.options['e_dims']
d_dims = self.options['d_dims']
d_mask_dims = self.options['d_mask_dims']
self.pretrain = self.options['pretrain']
if self.pretrain_just_disabled:
self.set_iter(0)
self.gan_power = gan_power = self.options['gan_power'] if not self.pretrain else 0.0
masked_training = self.options['masked_training']
ct_mode = self.options['ct_mode']
if ct_mode == 'none':
ct_mode = None
models_opt_on_gpu = False if len(devices) == 0 else self.options['models_opt_on_gpu']
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
input_ch=3
bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
mask_shape = nn.get4Dshape(resolution,resolution,1)
self.model_filename_list = []
with tf.device ('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder (nn.floatx, bgr_shape)
self.warped_dst = tf.placeholder (nn.floatx, bgr_shape)
self.src_code_in = tf.placeholder (nn.floatx, (None,256) )
self.target_src = tf.placeholder (nn.floatx, bgr_shape)
self.target_dst = tf.placeholder (nn.floatx, bgr_shape)
self.target_srcm_all = tf.placeholder (nn.floatx, mask_shape)
self.target_dstm_all = tf.placeholder (nn.floatx, mask_shape)
# Initializing model classes
model_archi = nn.DeepFakeArchi(resolution, mod='uhd' if 'uhd' in archi else None)
with tf.device (models_opt_device):
if 'df' in archi:
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, is_hd=is_hd, name='encoder')
encoder_out_ch = self.encoder.compute_output_channels ( (nn.floatx, bgr_shape))
self.inter = model_archi.Inter (in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims, is_hd=is_hd, name='inter')
inter_out_ch = self.inter.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
self.decoder_src = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd=is_hd, name='decoder_src')
self.decoder_dst = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd=is_hd, name='decoder_dst')
self.model_filename_list += [ [self.encoder, 'encoder.npy' ],
[self.inter, 'inter.npy' ],
[self.decoder_src, 'decoder_src.npy'],
[self.decoder_dst, 'decoder_dst.npy'] ]
if self.is_training:
if self.options['true_face_power'] != 0:
self.code_discriminator = nn.CodeDiscriminator(ae_dims, code_res=model_archi.Inter.get_code_res()*2, name='dis' )
self.model_filename_list += [ [self.code_discriminator, 'code_discriminator.npy'] ]
elif 'liae' in archi:
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, is_hd=is_hd, name='encoder')
encoder_out_ch = self.encoder.compute_output_channels ( (nn.floatx, bgr_shape))
self.inter_AB = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, is_hd=is_hd, name='inter_AB')
self.inter_B = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, is_hd=is_hd, name='inter_B')
inter_AB_out_ch = self.inter_AB.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
inter_B_out_ch = self.inter_B.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
inters_out_ch = inter_AB_out_ch+inter_B_out_ch
self.decoder = model_archi.Decoder(in_ch=inters_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, is_hd=is_hd, name='decoder')
self.model_filename_list += [ [self.encoder, 'encoder.npy'],
[self.inter_AB, 'inter_AB.npy'],
[self.inter_B , 'inter_B.npy'],
[self.decoder , 'decoder.npy'] ]
if self.is_training:
if gan_power != 0:
self.D_src = nn.PatchDiscriminator(patch_size=resolution//16, in_ch=input_ch, name="D_src")
self.D_dst = nn.PatchDiscriminator(patch_size=resolution//16, in_ch=input_ch, name="D_dst")
self.model_filename_list += [ [self.D_src, 'D_src.npy'] ]
self.model_filename_list += [ [self.D_dst, 'D_dst.npy'] ]
# Initialize optimizers
lr=5e-5
lr_dropout = 0.3 if self.options['lr_dropout'] and not self.pretrain else 1.0
clipnorm = 1.0 if self.options['clipgrad'] else 0.0
self.src_dst_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
if 'df' in archi:
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
elif 'liae' in archi:
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
self.src_dst_opt.initialize_variables (self.src_dst_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu)
if self.options['true_face_power'] != 0:
self.D_code_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_code_opt')
self.D_code_opt.initialize_variables ( self.code_discriminator.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [ (self.D_code_opt, 'D_code_opt.npy') ]
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.initialize_variables ( self.D_src.get_weights()+self.D_dst.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_dst_opt.npy') ]
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices) )
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size( gpu_count*bs_per_gpu)
# Compute losses per GPU
gpu_src_latent_code_list = []
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_G_loss_gvs = []
gpu_D_code_loss_gvs = []
gpu_D_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
gpu_warped_src = self.warped_src [batch_slice,:,:,:]
gpu_src_code_in = self.src_code_in[batch_slice,:]
gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
gpu_target_src = self.target_src [batch_slice,:,:,:]
gpu_target_dst = self.target_dst [batch_slice,:,:,:]
gpu_target_srcm_all = self.target_srcm_all[batch_slice,:,:,:]
gpu_target_dstm_all = self.target_dstm_all[batch_slice,:,:,:]
# process model tensors
if 'df' in archi:
gpu_src_latent_code = self.inter.dense1(self.encoder(gpu_warped_src))
gpu_src_in_code = self.inter.fd(gpu_src_code_in)
gpu_src_code = self.inter(self.encoder(gpu_warped_src))
gpu_dst_code = self.inter(self.encoder(gpu_warped_dst))
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(gpu_src_in_code)
#gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(gpu_src_code)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
elif 'liae' in archi:
gpu_src_code = self.encoder (gpu_warped_src)
gpu_src_inter_AB_code = self.inter_AB (gpu_src_code)
gpu_src_code = tf.concat([gpu_src_inter_AB_code,gpu_src_inter_AB_code], nn.conv2d_ch_axis )
gpu_dst_code = self.encoder (gpu_warped_dst)
gpu_dst_inter_B_code = self.inter_B (gpu_dst_code)
gpu_dst_inter_AB_code = self.inter_AB (gpu_dst_code)
gpu_dst_code = tf.concat([gpu_dst_inter_B_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis )
gpu_src_dst_code = tf.concat([gpu_dst_inter_AB_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis )
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_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
gpu_src_latent_code_list.append(gpu_src_latent_code)
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
# unpack masks from one combined mask
gpu_target_srcm = tf.clip_by_value (gpu_target_srcm_all, 0, 1)
gpu_target_dstm = tf.clip_by_value (gpu_target_dstm_all, 0, 1)
gpu_target_srcm_eyes = tf.clip_by_value (gpu_target_srcm_all-1, 0, 1)
gpu_target_dstm_eyes = tf.clip_by_value (gpu_target_dstm_all-1, 0, 1)
gpu_target_srcm_blur = nn.gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
gpu_target_dstm_blur = nn.gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
gpu_target_dst_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_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_masked = gpu_pred_src_dst*gpu_target_dstm_blur
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_blur)
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])
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
if eyes_prio:
gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_eyes - gpu_pred_src_src*gpu_target_srcm_eyes ), axis=[1,2,3])
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
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_psd_target_dst_masked, gpu_target_dst_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_anti_masked, gpu_target_dst_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_anti_masked - gpu_target_dst_anti_masked), axis=[1,2,3] )
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])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
if eyes_prio:
gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_eyes - gpu_pred_dst_dst*gpu_target_dstm_eyes ), axis=[1,2,3])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_G_loss = gpu_src_loss + gpu_dst_loss
def DLoss(labels,logits):
return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits), axis=[1,2,3])
if self.options['true_face_power'] != 0:
gpu_src_code_d = self.code_discriminator( gpu_src_code )
gpu_src_code_d_ones = tf.ones_like (gpu_src_code_d)
gpu_src_code_d_zeros = tf.zeros_like(gpu_src_code_d)
gpu_dst_code_d = self.code_discriminator( gpu_dst_code )
gpu_dst_code_d_ones = tf.ones_like(gpu_dst_code_d)
gpu_G_loss += self.options['true_face_power']*DLoss(gpu_src_code_d_ones, gpu_src_code_d)
gpu_D_code_loss = (DLoss(gpu_src_code_d_ones , gpu_dst_code_d) + \
DLoss(gpu_src_code_d_zeros, gpu_src_code_d) ) * 0.5
gpu_D_code_loss_gvs += [ nn.gradients (gpu_D_code_loss, self.code_discriminator.get_weights() ) ]
if gan_power != 0:
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_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_dst_dst_d = self.D_dst(gpu_pred_dst_dst_masked_opt)
gpu_pred_dst_dst_d_ones = tf.ones_like (gpu_pred_dst_dst_d)
gpu_pred_dst_dst_d_zeros = tf.zeros_like(gpu_pred_dst_dst_d)
gpu_target_dst_d = self.D_dst(gpu_target_dst_masked_opt)
gpu_target_dst_d_ones = tf.ones_like(gpu_target_dst_d)
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_dst_d_ones , gpu_target_dst_d) + \
DLoss(gpu_pred_dst_dst_d_zeros, gpu_pred_dst_dst_d) ) * 0.5
gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.D_src.get_weights()+self.D_dst.get_weights() ) ]
gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + DLoss(gpu_pred_dst_dst_d_ones, gpu_pred_dst_dst_d))
gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ]
# Average losses and gradients, and create optimizer update ops
with tf.device (models_opt_device):
src_latent_code = nn.concat(gpu_src_latent_code_list, 0)
pred_src_src = nn.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = nn.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = nn.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
src_loss = tf.concat(gpu_src_losses, 0)
dst_loss = tf.concat(gpu_dst_losses, 0)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op (nn.average_gv_list (gpu_G_loss_gvs))
if self.options['true_face_power'] != 0:
D_loss_gv_op = self.D_code_opt.get_update_op (nn.average_gv_list(gpu_D_code_loss_gvs))
if gan_power != 0:
src_D_src_dst_loss_gv_op = self.D_src_dst_opt.get_update_op (nn.average_gv_list(gpu_D_src_dst_loss_gvs) )
# Initializing training and view functions
def src_dst_train(warped_src, target_src, target_srcm_all, \
warped_dst, target_dst, target_dstm_all):
s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm_all:target_srcm_all,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm_all:target_dstm_all,
})
return s, d
self.src_dst_train = src_dst_train
if self.options['true_face_power'] != 0:
def D_train(warped_src, warped_dst):
nn.tf_sess.run ([D_loss_gv_op], feed_dict={self.warped_src: warped_src, self.warped_dst: warped_dst})
self.D_train = D_train
if gan_power != 0:
def D_src_dst_train(warped_src, target_src, target_srcm_all, \
warped_dst, target_dst, target_dstm_all):
nn.tf_sess.run ([src_D_src_dst_loss_gv_op], feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm_all:target_srcm_all,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm_all:target_dstm_all})
self.D_src_dst_train = D_src_dst_train
def AE_get_latent(warped_src):
return nn.tf_sess.run ( src_latent_code, feed_dict={self.warped_src:warped_src})
self.AE_get_latent = AE_get_latent
def AE_view_src(warped_src, src_code_in):
return nn.tf_sess.run ( pred_src_src,
feed_dict={self.warped_src:warped_src, self.src_code_in:src_code_in })
self.AE_view_src = AE_view_src
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
feed_dict={self.warped_src:warped_src,
self.warped_dst:warped_dst})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device( f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
if 'df' in archi:
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
elif 'liae' in archi:
gpu_dst_code = self.encoder (self.warped_dst)
gpu_dst_inter_B_code = self.inter_B (gpu_dst_code)
gpu_dst_inter_AB_code = self.inter_AB (gpu_dst_code)
gpu_dst_code = tf.concat([gpu_dst_inter_B_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis)
gpu_src_dst_code = tf.concat([gpu_dst_inter_AB_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
def AE_merge( warped_dst):
return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
if self.pretrain_just_disabled:
do_init = False
if 'df' in archi:
if model == self.inter:
do_init = True
elif 'liae' in archi:
if model == self.inter_AB:
do_init = True
else:
do_init = self.is_first_run()
if not do_init:
do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path()
random_ct_samples_path=training_data_dst_path if ct_mode is not None and not self.pretrain else None
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
if ct_mode is not None:
src_generators_count = int(src_generators_count * 1.5)
self.set_training_data_generators ([
SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':self.options['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},
{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':False, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE_EYES, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
{'sample_type': SampleProcessor.SampleType.PITCH_YAW_ROLL, 'resolution': resolution},
],
generators_count=src_generators_count ),
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':self.options['random_warp'], 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
{'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},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE_EYES, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
],
generators_count=dst_generators_count )
])
self.last_src_samples_loss = []
self.last_dst_samples_loss = []
if self.pretrain_just_disabled:
self.update_sample_for_preview(force_new=True)
class PRD(nn.ModelBase):
def on_build(self, ae_ch):
self.dense1 = nn.Dense( ae_ch+1, 1024 )
self.dense2 = nn.Dense( 1024, 2048 )
self.dense3 = nn.Dense( 2048, 4096 )
self.dense4 = nn.Dense( 4096, 4096 )
self.dense5 = nn.Dense( 4096, ae_ch )
def forward(self, inp, yaw_in):
x = tf.concat( [inp, yaw_in], -1 )
x = self.dense1(x)
x = self.dense2(x)
x = self.dense3(x)
x = self.dense4(x)
x = self.dense5(x)
return x
with tf.device( f'/GPU:0'):
prd_model = PRD(256, name='PRD')
prd_model.init_weights()
prd_in = tf.placeholder (nn.floatx, (None,256) )
prd_targ = tf.placeholder (nn.floatx, (None,256) )
yaw_diff_in = tf.placeholder (nn.floatx, (None,1) )
prd_out = prd_model(prd_in, yaw_diff_in)
loss = tf.reduce_sum ( tf.abs (prd_out - prd_targ) )
loss_gvs = nn.gradients (loss, prd_model.get_weights() )
prd_opt = nn.RMSprop(lr=5e-6, lr_dropout=0.3, name='prd_opt')
prd_opt.initialize_variables(prd_model.get_weights())
prd_opt.init_weights()
loss_gv_op = prd_opt.get_update_op (loss_gvs)
s_gen, _ = self.get_training_data_generators()
bs = self.get_batch_size()
for n in range(1000):
warped_src, target_src, target_srcm_all, src_pyr = s_gen.generate_next()
sl = self.AE_get_latent(target_src)
prd_in_np = []
prd_targ_np = []
yaw_diff_in_np = []
for i in range(bs):
prd_in_np += [sl[i]]
j = i
while j == i:
j = np.random.randint(bs)
prd_targ_np += [ sl[j] ]
yaw_diff_in_np += [ np.float32( [ src_pyr[j][1]-src_pyr[i][1] ] ) ]
prd_loss, _ = nn.tf_sess.run([loss, loss_gv_op], feed_dict={prd_in:prd_in_np, prd_targ:prd_targ_np, yaw_diff_in:yaw_diff_in_np} )
print(f'{n} loss = {prd_loss}')
warped_src, target_src, target_srcm_all, src_pyr = s_gen.generate_next()
sl = self.AE_get_latent(target_src)
yaw_diff_in_np = np.float32( [ [-0.4] ] *bs )
new_sl = nn.tf_sess.run(prd_out, feed_dict={prd_in:sl, yaw_diff_in:yaw_diff_in_np} )
new_target_src = self.AE_view_src( target_src, new_sl )
target_src = np.clip( nn.to_data_format( target_src ,"NHWC", self.model_data_format), 0.0, 1.0)
new_target_src = np.clip( nn.to_data_format( new_target_src ,"NHWC", self.model_data_format), 0.0, 1.0)
for i in range(bs):
screen = np.concatenate ( (target_src[i], new_target_src[i]), 1 )
cv2.imshow("", (screen*255).astype(np.uint8) )
cv2.waitKey(0)
import code
code.interact(local=dict(globals(), **locals()))
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.model_data_format = "NCHW" if len(
devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
resolution = self.resolution = 96
self.face_type = FaceType.FULL
ae_dims = 128
e_dims = 128
d_dims = 64
self.pretrain = False
self.pretrain_just_disabled = False
masked_training = True
models_opt_on_gpu = len(devices) >= 1 and all(
[dev.total_mem_gb >= 4 for dev in devices])
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device == '/CPU:0'
input_ch = 3
bgr_shape = nn.get4Dshape(resolution, resolution, input_ch)
mask_shape = nn.get4Dshape(resolution, resolution, 1)
self.model_filename_list = []
model_archi = nn.DeepFakeArchi(resolution, mod='quick')
with tf.device('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder(nn.floatx, bgr_shape)
self.warped_dst = tf.placeholder(nn.floatx, bgr_shape)
self.target_src = tf.placeholder(nn.floatx, bgr_shape)
self.target_dst = tf.placeholder(nn.floatx, bgr_shape)
self.target_srcm = tf.placeholder(nn.floatx, mask_shape)
self.target_dstm = tf.placeholder(nn.floatx, mask_shape)
# Initializing model classes
with tf.device(models_opt_device):
self.encoder = model_archi.Encoder(in_ch=input_ch,
e_ch=e_dims,
name='encoder')
encoder_out_ch = self.encoder.compute_output_channels(
(nn.floatx, bgr_shape))
self.inter = model_archi.Inter(in_ch=encoder_out_ch,
ae_ch=ae_dims,
ae_out_ch=ae_dims,
d_ch=d_dims,
name='inter')
inter_out_ch = self.inter.compute_output_channels(
(nn.floatx, (None, encoder_out_ch)))
self.decoder_src = model_archi.Decoder(in_ch=inter_out_ch,
d_ch=d_dims,
name='decoder_src')
self.decoder_dst = model_archi.Decoder(in_ch=inter_out_ch,
d_ch=d_dims,
name='decoder_dst')
self.model_filename_list += [[self.encoder, 'encoder.npy'],
[self.inter, 'inter.npy'],
[self.decoder_src, 'decoder_src.npy'],
[self.decoder_dst, 'decoder_dst.npy']]
if self.is_training:
self.src_dst_trainable_weights = self.encoder.get_weights(
) + self.inter.get_weights() + self.decoder_src.get_weights(
) + self.decoder_dst.get_weights()
# Initialize optimizers
self.src_dst_opt = nn.RMSprop(lr=2e-4,
lr_dropout=0.3,
name='src_dst_opt')
self.src_dst_opt.initialize_variables(
self.src_dst_trainable_weights,
vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [(self.src_dst_opt,
'src_dst_opt.npy')]
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices))
bs_per_gpu = max(1, 4 // gpu_count)
self.set_batch_size(gpu_count * bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device(
f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0'):
batch_slice = slice(gpu_id * bs_per_gpu,
(gpu_id + 1) * bs_per_gpu)
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
gpu_warped_src = self.warped_src[batch_slice, :, :, :]
gpu_warped_dst = self.warped_dst[batch_slice, :, :, :]
gpu_target_src = self.target_src[batch_slice, :, :, :]
gpu_target_dst = self.target_dst[batch_slice, :, :, :]
gpu_target_srcm = self.target_srcm[
batch_slice, :, :, :]
gpu_target_dstm = self.target_dstm[
batch_slice, :, :, :]
# process model tensors
gpu_src_code = self.inter(self.encoder(gpu_warped_src))
gpu_dst_code = self.inter(self.encoder(gpu_warped_dst))
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(
gpu_src_code)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(
gpu_dst_code)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
gpu_dst_code)
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
gpu_target_srcm_blur = nn.gaussian_blur(
gpu_target_srcm, max(1, resolution // 32))
gpu_target_dstm_blur = nn.gaussian_blur(
gpu_target_dstm, max(1, resolution // 32))
gpu_target_dst_masked = gpu_target_dst * gpu_target_dstm_blur
gpu_target_dst_anti_masked = gpu_target_dst * (
1.0 - gpu_target_dstm_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_masked = gpu_pred_src_dst * gpu_target_dstm_blur
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst * (
1.0 - gpu_target_dstm_blur)
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])
gpu_src_loss += tf.reduce_mean(
10 * tf.square(gpu_target_src_masked_opt -
gpu_pred_src_src_masked_opt),
axis=[1, 2, 3])
gpu_src_loss += tf.reduce_mean(
10 * tf.square(gpu_target_srcm - gpu_pred_src_srcm),
axis=[1, 2, 3])
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])
gpu_dst_loss += tf.reduce_mean(
10 * tf.square(gpu_target_dst_masked_opt -
gpu_pred_dst_dst_masked_opt),
axis=[1, 2, 3])
gpu_dst_loss += tf.reduce_mean(
10 * tf.square(gpu_target_dstm - gpu_pred_dst_dstm),
axis=[1, 2, 3])
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_G_loss = gpu_src_loss + gpu_dst_loss
gpu_src_dst_loss_gvs += [
nn.gradients(gpu_G_loss,
self.src_dst_trainable_weights)
]
# Average losses and gradients, and create optimizer update ops
with tf.device(models_opt_device):
pred_src_src = nn.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = nn.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = nn.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
src_loss = nn.average_tensor_list(gpu_src_losses)
dst_loss = nn.average_tensor_list(gpu_dst_losses)
src_dst_loss_gv = nn.average_gv_list(gpu_src_dst_loss_gvs)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op(
src_dst_loss_gv)
# Initializing training and view functions
def src_dst_train(warped_src, target_src, target_srcm, \
warped_dst, target_dst, target_dstm):
s, d, _ = nn.tf_sess.run(
[src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={
self.warped_src: warped_src,
self.target_src: target_src,
self.target_srcm: target_srcm,
self.warped_dst: warped_dst,
self.target_dst: target_dst,
self.target_dstm: target_dstm,
})
s = np.mean(s)
d = np.mean(d)
return s, d
self.src_dst_train = src_dst_train
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run([
pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst,
pred_src_dstm
],
feed_dict={
self.warped_src: warped_src,
self.warped_dst: warped_dst
})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device(f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
def AE_merge(warped_dst):
return nn.tf_sess.run(
[gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm],
feed_dict={self.warped_dst: warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(
self.model_filename_list, "Initializing models"):
if self.pretrain_just_disabled:
do_init = False
if model == self.inter:
do_init = True
else:
do_init = self.is_first_run()
if not do_init:
do_init = not model.load_weights(
self.get_strpath_storage_for_file(filename))
if do_init and self.pretrained_model_path is not None:
pretrained_filepath = self.pretrained_model_path / filename
if pretrained_filepath.exists():
do_init = not model.load_weights(pretrained_filepath)
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path(
)
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path(
)
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
self.set_training_data_generators([
SampleGeneratorFace(
training_data_src_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True if self.pretrain else False),
output_sample_types=[{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp':
True,
'transform':
True,
'channel_type':
SampleProcessor.ChannelType.BGR,
'face_type':
self.face_type,
'data_format':
nn.data_format,
'resolution':
resolution
}, {
'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
}, {
'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
}],
generators_count=src_generators_count),
SampleGeneratorFace(
training_data_dst_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True if self.pretrain else False),
output_sample_types=[{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp':
True,
'transform':
True,
'channel_type':
SampleProcessor.ChannelType.BGR,
'face_type':
self.face_type,
'data_format':
nn.data_format,
'resolution':
resolution
}, {
'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
}, {
'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
}],
generators_count=dst_generators_count)
])
self.last_samples = None
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
nn.initialize(data_format="NHWC")
tf = nn.tf
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.resolution = resolution = 256 #self.options['resolution']
#self.face_type = {'h' : FaceType.HALF,
# 'mf' : FaceType.MID_FULL,
# 'f' : FaceType.FULL,
# 'wf' : FaceType.WHOLE_FACE}[ self.options['face_type'] ]
self.face_type = FaceType.FULL
place_model_on_cpu = len(devices) == 0
models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
bgr_shape = nn.get4Dshape(resolution, resolution, 3)
mask_shape = nn.get4Dshape(resolution, resolution, 1)
# Initializing model classes
self.model = TernausNet(f'{self.model_name}_FANSeg',
resolution,
FaceType.toString(self.face_type),
load_weights=not self.is_first_run(),
weights_file_root=self.get_model_root_path(),
training=True,
place_model_on_cpu=place_model_on_cpu)
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices))
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size(gpu_count * bs_per_gpu)
# Compute losses per GPU
gpu_pred_list = []
gpu_losses = []
gpu_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device(
f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0'):
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
batch_slice = slice(gpu_id * bs_per_gpu,
(gpu_id + 1) * bs_per_gpu)
gpu_input_t = self.model.input_t[batch_slice, :, :, :]
gpu_target_t = self.model.target_t[
batch_slice, :, :, :]
# process model tensors
gpu_pred_logits_t, gpu_pred_t = self.model.net(
[gpu_input_t])
gpu_pred_list.append(gpu_pred_t)
gpu_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=gpu_target_t, logits=gpu_pred_logits_t),
axis=[1, 2, 3])
gpu_losses += [gpu_loss]
gpu_loss_gvs += [
nn.tf_gradients(gpu_loss, self.model.net_weights)
]
# Average losses and gradients, and create optimizer update ops
with tf.device(models_opt_device):
pred = nn.tf_concat(gpu_pred_list, 0)
loss = tf.reduce_mean(gpu_losses)
loss_gv_op = self.model.opt.get_update_op(
nn.tf_average_gv_list(gpu_loss_gvs))
# Initializing training and view functions
def train(input_np, target_np):
l, _ = nn.tf_sess.run([loss, loss_gv_op],
feed_dict={
self.model.input_t: input_np,
self.model.target_t: target_np
})
return l
self.train = train
def view(input_np):
return nn.tf_sess.run([pred],
feed_dict={self.model.input_t: input_np})
self.view = view
# initializing sample generators
training_data_src_path = self.training_data_src_path
training_data_dst_path = self.training_data_dst_path
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
src_generators_count = int(src_generators_count * 1.5)
src_generator = SampleGeneratorFace(
training_data_src_path,
random_ct_samples_path=training_data_src_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True),
output_sample_types=[
{
'sample_type': SampleProcessor.SampleType.FACE_IMAGE,
'ct_mode': 'lct',
'warp': True,
'transform': True,
'channel_type': SampleProcessor.ChannelType.BGR,
'face_type': self.face_type,
'motion_blur': (25, 5),
'gaussian_blur': (25, 5),
'data_format': nn.data_format,
'resolution': resolution
},
{
'sample_type': SampleProcessor.SampleType.FACE_MASK,
'warp': True,
'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
},
],
generators_count=src_generators_count)
dst_generator = SampleGeneratorFace(
training_data_dst_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True),
output_sample_types=[
{
'sample_type': SampleProcessor.SampleType.FACE_IMAGE,
'warp': False,
'transform': True,
'channel_type': SampleProcessor.ChannelType.BGR,
'face_type': self.face_type,
'motion_blur': (25, 5),
'gaussian_blur': (25, 5),
'data_format': nn.data_format,
'resolution': resolution
},
],
generators_count=dst_generators_count,
raise_on_no_data=False)
if not dst_generator.is_initialized():
io.log_info(
f"\nTo view the model on unseen faces, place any aligned faces in {training_data_dst_path}.\n"
)
self.set_training_data_generators([src_generator, dst_generator])
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
self.model_data_format = "NCHW" if len(device_config.devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
conv_kernel_initializer = nn.initializers.ca()
class Downscale(nn.ModelBase):
def __init__(self, in_ch, out_ch, kernel_size=5, dilations=1, subpixel=True, use_activator=True, *kwargs ):
self.in_ch = in_ch
self.out_ch = out_ch
self.kernel_size = kernel_size
self.dilations = dilations
self.subpixel = subpixel
self.use_activator = use_activator
super().__init__(*kwargs)
def on_build(self, *args, **kwargs ):
self.conv1 = nn.Conv2D( self.in_ch,
self.out_ch // (4 if self.subpixel else 1),
kernel_size=self.kernel_size,
strides=1 if self.subpixel else 2,
padding='SAME', dilations=self.dilations, kernel_initializer=conv_kernel_initializer )
def forward(self, x):
x = self.conv1(x)
if self.subpixel:
x = nn.tf_space_to_depth(x, 2)
if self.use_activator:
x = nn.tf_gelu(x)
return x
def get_out_ch(self):
return (self.out_ch // 4) * 4
class DownscaleBlock(nn.ModelBase):
def on_build(self, in_ch, ch, n_downscales, kernel_size, dilations=1, subpixel=True):
self.downs = []
last_ch = in_ch
for i in range(n_downscales):
cur_ch = ch*( min(2**i, 8) )
self.downs.append ( Downscale(last_ch, cur_ch, kernel_size=kernel_size, dilations=dilations, subpixel=subpixel) )
last_ch = self.downs[-1].get_out_ch()
def forward(self, inp):
x = inp
for down in self.downs:
x = down(x)
return x
class Upscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=3 ):
self.conv1 = nn.Conv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME', kernel_initializer=conv_kernel_initializer)
def forward(self, x):
x = self.conv1(x)
x = nn.tf_gelu(x)
x = nn.tf_depth_to_space(x, 2)
return x
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3 ):
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', kernel_initializer=conv_kernel_initializer)
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', kernel_initializer=conv_kernel_initializer)
def forward(self, inp):
x = self.conv1(inp)
x = nn.tf_gelu(x)
x = self.conv2(x)
x = inp + x
x = nn.tf_gelu(x)
return x
class Encoder(nn.ModelBase):
def on_build(self, in_ch, e_ch):
self.down1 = DownscaleBlock(in_ch, e_ch, n_downscales=4, kernel_size=5)
def forward(self, inp):
return nn.tf_flatten(self.down1(inp))
class Inter(nn.ModelBase):
def __init__(self, in_ch, lowest_dense_res, ae_ch, ae_out_ch, d_ch, **kwargs):
self.in_ch, self.lowest_dense_res, self.ae_ch, self.ae_out_ch, self.d_ch = in_ch, lowest_dense_res, ae_ch, ae_out_ch, d_ch
super().__init__(**kwargs)
def on_build(self):
in_ch, lowest_dense_res, ae_ch, ae_out_ch, d_ch = self.in_ch, self.lowest_dense_res, self.ae_ch, self.ae_out_ch, self.d_ch
self.dense1 = nn.Dense( in_ch, ae_ch, kernel_initializer=tf.initializers.orthogonal )
self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch, maxout_features=4, kernel_initializer=tf.initializers.orthogonal )
self.upscale1 = Upscale(ae_out_ch, d_ch*8)
self.res1 = ResidualBlock(d_ch*8)
def forward(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
x = nn.tf_reshape_4D (x, lowest_dense_res, lowest_dense_res, self.ae_out_ch)
x = self.upscale1(x)
x = self.res1(x)
return x
def get_out_ch(self):
return self.ae_out_ch
class Decoder(nn.ModelBase):
def on_build(self, in_ch, d_ch):
self.upscale1 = Upscale(in_ch, d_ch*4)
self.res1 = ResidualBlock(d_ch*4)
self.upscale2 = Upscale(d_ch*4, d_ch*2)
self.res2 = ResidualBlock(d_ch*2)
self.upscale3 = Upscale(d_ch*2, d_ch*1)
self.res3 = ResidualBlock(d_ch*1)
self.upscalem1 = Upscale(in_ch, d_ch)
self.upscalem2 = Upscale(d_ch, d_ch//2)
self.upscalem3 = Upscale(d_ch//2, d_ch//2)
self.out_conv = nn.Conv2D( d_ch*1, 3, kernel_size=1, padding='SAME', kernel_initializer=conv_kernel_initializer)
self.out_convm = nn.Conv2D( d_ch//2, 1, kernel_size=1, padding='SAME', kernel_initializer=conv_kernel_initializer)
def forward(self, inp):
z = inp
x = self.upscale1 (z)
x = self.res1 (x)
x = self.upscale2 (x)
x = self.res2 (x)
x = self.upscale3 (x)
x = self.res3 (x)
y = self.upscalem1 (z)
y = self.upscalem2 (y)
y = self.upscalem3 (y)
return tf.nn.sigmoid(self.out_conv(x)), \
tf.nn.sigmoid(self.out_convm(y))
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
resolution = self.resolution = 96
ae_dims = 128
e_dims = 128
d_dims = 64
self.pretrain = False
self.pretrain_just_disabled = False
masked_training = True
models_opt_on_gpu = len(devices) >= 1 and all([dev.total_mem_gb >= 2 for dev in devices])
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
input_ch = 3
output_ch = 3
bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
mask_shape = nn.get4Dshape(resolution,resolution,1)
lowest_dense_res = resolution // 16
self.model_filename_list = []
with tf.device ('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder (nn.tf_floatx, bgr_shape)
self.warped_dst = tf.placeholder (nn.tf_floatx, bgr_shape)
self.target_src = tf.placeholder (nn.tf_floatx, bgr_shape)
self.target_dst = tf.placeholder (nn.tf_floatx, bgr_shape)
self.target_srcm = tf.placeholder (nn.tf_floatx, mask_shape)
self.target_dstm = tf.placeholder (nn.tf_floatx, mask_shape)
# Initializing model classes
with tf.device (models_opt_device):
self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, name='encoder')
encoder_out_ch = self.encoder.compute_output_channels ( (nn.tf_floatx, bgr_shape))
self.inter = Inter (in_ch=encoder_out_ch, lowest_dense_res=lowest_dense_res, ae_ch=ae_dims, ae_out_ch=ae_dims, d_ch=d_dims, name='inter')
inter_out_ch = self.inter.compute_output_channels ( (nn.tf_floatx, (None,encoder_out_ch)))
self.decoder_src = Decoder(in_ch=inter_out_ch, d_ch=d_dims, name='decoder_src')
self.decoder_dst = Decoder(in_ch=inter_out_ch, d_ch=d_dims, name='decoder_dst')
self.model_filename_list += [ [self.encoder, 'encoder.npy' ],
[self.inter, 'inter.npy' ],
[self.decoder_src, 'decoder_src.npy'],
[self.decoder_dst, 'decoder_dst.npy'] ]
if self.is_training:
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
# Initialize optimizers
self.src_dst_opt = nn.TFRMSpropOptimizer(lr=2e-4, lr_dropout=0.3, name='src_dst_opt')
self.src_dst_opt.initialize_variables(self.src_dst_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu )
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices) )
bs_per_gpu = max(1, 4 // gpu_count)
self.set_batch_size( gpu_count*bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
gpu_warped_src = self.warped_src [batch_slice,:,:,:]
gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
gpu_target_src = self.target_src [batch_slice,:,:,:]
gpu_target_dst = self.target_dst [batch_slice,:,:,:]
gpu_target_srcm = self.target_srcm[batch_slice,:,:,:]
gpu_target_dstm = self.target_dstm[batch_slice,:,:,:]
# process model tensors
gpu_src_code = self.inter(self.encoder(gpu_warped_src))
gpu_dst_code = self.inter(self.encoder(gpu_warped_dst))
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(gpu_src_code)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
gpu_target_srcm_blur = nn.tf_gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
gpu_target_dstm_blur = nn.tf_gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
gpu_target_dst_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_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_masked = gpu_pred_src_dst*gpu_target_dstm_blur
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_blur)
gpu_src_loss = tf.reduce_mean ( 10*nn.tf_dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
gpu_dst_loss = tf.reduce_mean ( 10*nn.tf_dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_G_loss = gpu_src_loss + gpu_dst_loss
gpu_src_dst_loss_gvs += [ nn.tf_gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ]
# Average losses and gradients, and create optimizer update ops
with tf.device (models_opt_device):
pred_src_src = nn.tf_concat(gpu_pred_src_src_list, 0)
pred_dst_dst = nn.tf_concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = nn.tf_concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = nn.tf_concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = nn.tf_concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = nn.tf_concat(gpu_pred_src_dstm_list, 0)
src_loss = nn.tf_average_tensor_list(gpu_src_losses)
dst_loss = nn.tf_average_tensor_list(gpu_dst_losses)
src_dst_loss_gv = nn.tf_average_gv_list (gpu_src_dst_loss_gvs)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op (src_dst_loss_gv)
# Initializing training and view functions
def src_dst_train(warped_src, target_src, target_srcm, \
warped_dst, target_dst, target_dstm):
s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm:target_srcm,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm:target_dstm,
})
s = np.mean(s)
d = np.mean(d)
return s, d
self.src_dst_train = src_dst_train
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
feed_dict={self.warped_src:warped_src,
self.warped_dst:warped_dst})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device( f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
def AE_merge( warped_dst):
return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
if self.pretrain_just_disabled:
do_init = False
if model == self.inter:
do_init = True
else:
do_init = self.is_first_run()
if not do_init:
do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
if do_init and self.pretrained_model_path is not None:
pretrained_filepath = self.pretrained_model_path / filename
if pretrained_filepath.exists():
do_init = not model.load_weights(pretrained_filepath)
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
t = SampleProcessor.Types
face_type = t.FACE_TYPE_FULL
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path()
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
self.set_training_data_generators ([
SampleGeneratorFace(training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=True if self.pretrain else False),
output_sample_types = [ {'types' : (t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution':resolution, },
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution': resolution, },
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'data_format':nn.data_format, 'resolution': resolution } ],
generators_count=src_generators_count ),
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=True if self.pretrain else False),
output_sample_types = [ {'types' : (t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution':resolution},
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'data_format':nn.data_format, 'resolution': resolution},
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'data_format':nn.data_format, 'resolution': resolution} ],
generators_count=dst_generators_count )
])
self.last_samples = None
def on_initialize_options(self):
device_config = nn.getCurrentDeviceConfig()
lowest_vram = 2
if len(device_config.devices) != 0:
lowest_vram = device_config.devices.get_worst_device().total_mem_gb
if lowest_vram >= 4:
suggest_batch_size = 8
else:
suggest_batch_size = 4
yn_str = {True:'y',False:'n'}
default_resolution = self.options['resolution'] = self.load_or_def_option('resolution', 128)
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_archi = self.options['archi'] = self.load_or_def_option('archi', 'df')
default_ae_dims = self.options['ae_dims'] = self.load_or_def_option('ae_dims', 256)
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_masked_training = self.options['masked_training'] = self.load_or_def_option('masked_training', True)
default_eyes_prio = self.options['eyes_prio'] = self.load_or_def_option('eyes_prio', False)
default_lr_dropout = self.options['lr_dropout'] = self.load_or_def_option('lr_dropout', False)
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_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_bg_style_power = self.options['bg_style_power'] = self.load_or_def_option('bg_style_power', 0.0)
default_ct_mode = self.options['ct_mode'] = self.load_or_def_option('ct_mode', 'none')
default_clipgrad = self.options['clipgrad'] = self.load_or_def_option('clipgrad', False)
default_pretrain = self.options['pretrain'] = self.load_or_def_option('pretrain', False)
ask_override = self.ask_override()
if self.is_first_run() or ask_override:
self.ask_autobackup_hour()
self.ask_write_preview_history()
self.ask_target_iter()
self.ask_random_flip()
self.ask_batch_size(suggest_batch_size)
if self.is_first_run():
resolution = io.input_int("Resolution", default_resolution, add_info="64-512", help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
resolution = np.clip ( (resolution // 16) * 16, 64, 512)
self.options['resolution'] = resolution
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf'], help_message="Half / mid face / full face / whole face. 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, but requires manual merge in Adobe After Effects.").lower()
self.options['archi'] = io.input_str ("AE architecture", default_archi, ['df','liae','dfhd','liaehd'], help_message="'df' keeps faces more natural.\n'liae' can fix overly different face shapes.\n'hd' are experimental versions.").lower()
default_d_dims = 48 if self.options['archi'] == 'dfhd' else 64
default_d_dims = self.options['d_dims'] = self.load_or_def_option('d_dims', default_d_dims)
default_d_mask_dims = default_d_dims // 3
default_d_mask_dims += default_d_mask_dims % 2
default_d_mask_dims = self.options['d_mask_dims'] = self.load_or_def_option('d_mask_dims', default_d_mask_dims)
if self.is_first_run():
self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dimensions", default_ae_dims, add_info="32-1024", help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
e_dims = np.clip ( io.input_int("Encoder dimensions", default_e_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['e_dims'] = e_dims + e_dims % 2
d_dims = np.clip ( io.input_int("Decoder dimensions", default_d_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['d_dims'] = d_dims + d_dims % 2
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:
if self.options['face_type'] == 'wf':
self.options['masked_training'] = io.input_bool ("Masked training", default_masked_training, help_message="This option is available only for 'whole_face' type. Masked training clips training area to full_face mask, thus network will train the faces properly. When the face is trained enough, disable this option to train all area of the frame. Merge with 'raw-rgb' mode, then use Adobe After Effects to manually mask and compose whole face include forehead.")
self.options['eyes_prio'] = io.input_bool ("Eyes priority", default_eyes_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction ( especially on HD architectures ) by forcing the neural network to train eyes with higher priority. before/after https://i.imgur.com/YQHOuSR.jpg ')
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.")
self.options['lr_dropout'] = io.input_bool ("Use learning rate dropout", default_lr_dropout, 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.")
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.")
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. Accelerates the speed of training. Forces the neural network to learn small details of the face. You can enable/disable this option at any time. Typical value is 1.0"), 0.0, 10.0 )
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 )
else:
self.options['true_face_power'] = 0.0
if self.options['face_type'] != 'wf':
self.options['face_style_power'] = np.clip ( io.input_number("Face style power", default_face_style_power, add_info="0.0..100.0", help_message="Learn to transfer face style details such as light and color conditions. Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style. Start from 0.001 value and check history changes. Enabling this option increases the chance of model collapse."), 0.0, 100.0 )
self.options['bg_style_power'] = np.clip ( io.input_number("Background style power", default_bg_style_power, add_info="0.0..100.0", help_message="Learn to transfer background around face. This can make face more like dst. Enabling this option increases the chance of model collapse. Typical value is 2.0"), 0.0, 100.0 )
self.options['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")
self.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain, help_message="Pretrain the model with large amount of various faces. After that, model can be used to train the fakes more quickly.")
if self.options['pretrain'] and self.get_pretraining_data_path() is None:
raise Exception("pretraining_data_path is not defined")
self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.model_data_format = "NCHW" if len(
devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
resolution = self.resolution = 96
self.face_type = FaceType.FULL
ae_dims = 256
e_dims = 64
d_dims = 64
self.pretrain = False
self.pretrain_just_disabled = False
masked_training = True
models_opt_on_gpu = len(devices) >= 1 and all(
[dev.total_mem_gb >= 4 for dev in devices])
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device == '/CPU:0'
input_ch = 3
bgr_shape = nn.get4Dshape(resolution, resolution, input_ch)
mask_shape = nn.get4Dshape(resolution, resolution, 1)
self.model_filename_list = []
kernel_initializer = tf.initializers.glorot_uniform()
class Upscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=3):
self.conv1 = nn.Conv2D(in_ch,
out_ch * 4,
kernel_size=kernel_size,
padding='SAME',
kernel_initializer=kernel_initializer)
def forward(self, x):
x = self.conv1(x)
x = tf.nn.leaky_relu(x, 0.1)
x = nn.depth_to_space(x, 2)
return x
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3):
self.conv1 = nn.Conv2D(ch,
ch,
kernel_size=kernel_size,
padding='SAME',
kernel_initializer=kernel_initializer)
self.conv2 = nn.Conv2D(ch,
ch,
kernel_size=kernel_size,
padding='SAME',
kernel_initializer=kernel_initializer)
def forward(self, inp):
x = self.conv1(inp)
x = tf.nn.leaky_relu(x, 0.2)
x = self.conv2(x)
x = tf.nn.leaky_relu(inp + x, 0.2)
return x
class Encoder(nn.ModelBase):
def on_build(self, in_ch, e_ch):
self.down11 = nn.Conv2D(in_ch,
e_ch,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down12 = nn.Conv2D(e_ch,
e_ch,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down21 = nn.Conv2D(e_ch,
e_ch * 2,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down22 = nn.Conv2D(e_ch * 2,
e_ch * 2,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down31 = nn.Conv2D(e_ch * 2,
e_ch * 4,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down32 = nn.Conv2D(e_ch * 4,
e_ch * 4,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down33 = nn.Conv2D(e_ch * 4,
e_ch * 4,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down41 = nn.Conv2D(e_ch * 4,
e_ch * 8,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down42 = nn.Conv2D(e_ch * 8,
e_ch * 8,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down43 = nn.Conv2D(e_ch * 8,
e_ch * 8,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down51 = nn.Conv2D(e_ch * 8,
e_ch * 8,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down52 = nn.Conv2D(e_ch * 8,
e_ch * 8,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
self.down53 = nn.Conv2D(e_ch * 8,
e_ch * 8,
kernel_size=3,
strides=1,
padding='SAME',
kernel_initializer=kernel_initializer)
def forward(self, inp):
x = inp
x = self.down11(x)
x = self.down12(x)
x = nn.max_pool(x)
x = self.down21(x)
x = self.down22(x)
x = nn.max_pool(x)
x = self.down31(x)
x = self.down32(x)
x = self.down33(x)
x = nn.max_pool(x)
x = self.down41(x)
x = self.down42(x)
x = self.down43(x)
x = nn.max_pool(x)
x = self.down51(x)
x = self.down52(x)
x = self.down53(x)
x = nn.max_pool(x)
x = nn.flatten(x)
return x
class Downscale(nn.ModelBase):
def __init__(self,
in_ch,
out_ch,
kernel_size=5,
dilations=1,
subpixel=True,
use_activator=True,
*kwargs):
self.in_ch = in_ch
self.out_ch = out_ch
self.kernel_size = kernel_size
self.dilations = dilations
self.subpixel = subpixel
self.use_activator = use_activator
super().__init__(*kwargs)
def on_build(self, *args, **kwargs):
self.conv1 = nn.Conv2D(self.in_ch,
self.out_ch //
(4 if self.subpixel else 1),
kernel_size=self.kernel_size,
strides=1 if self.subpixel else 2,
padding='SAME',
dilations=self.dilations,
kernel_initializer=kernel_initializer)
def forward(self, x):
x = self.conv1(x)
if self.subpixel:
x = nn.space_to_depth(x, 2)
if self.use_activator:
x = tf.nn.leaky_relu(x, 0.1)
return x
def get_out_ch(self):
return (self.out_ch // 4) * 4
class DownscaleBlock(nn.ModelBase):
def on_build(self,
in_ch,
ch,
n_downscales,
kernel_size,
dilations=1,
subpixel=True):
self.downs = []
last_ch = in_ch
for i in range(n_downscales):
cur_ch = ch * (min(2**i, 8))
self.downs.append(
Downscale(last_ch,
cur_ch,
kernel_size=kernel_size,
dilations=dilations,
subpixel=subpixel))
last_ch = self.downs[-1].get_out_ch()
def forward(self, inp):
x = inp
for down in self.downs:
x = down(x)
return x
class Upscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=3):
self.conv1 = nn.Conv2D(in_ch,
out_ch * 4,
kernel_size=kernel_size,
padding='SAME',
kernel_initializer=kernel_initializer)
def forward(self, x):
x = self.conv1(x)
x = tf.nn.leaky_relu(x, 0.1)
x = nn.depth_to_space(x, 2)
return x
class Encoder(nn.ModelBase):
def on_build(self, in_ch, e_ch):
self.down1 = DownscaleBlock(in_ch,
e_ch,
n_downscales=4,
kernel_size=5,
dilations=1,
subpixel=False)
def forward(self, inp):
x = nn.flatten(self.down1(inp))
return x
class Branch(nn.ModelBase):
def on_build(self, in_ch, ae_ch):
self.dense1 = nn.Dense(in_ch, ae_ch)
def forward(self, inp):
x = self.dense1(inp)
return x
class Classifier(nn.ModelBase):
def on_build(self, in_ch, n_classes):
self.dense1 = nn.Dense(in_ch, 4096)
self.dense2 = nn.Dense(4096, 4096)
self.pitch_dense = nn.Dense(4096, n_classes)
self.yaw_dense = nn.Dense(4096, n_classes)
def forward(self, inp):
x = inp
x = self.dense1(x)
x = self.dense2(x)
return self.pitch_dense(x), self.yaw_dense(x)
lowest_dense_res = resolution // 16
class Inter(nn.ModelBase):
def on_build(self, in_ch, ae_out_ch):
self.ae_out_ch = ae_out_ch
self.dense2 = nn.Dense(
in_ch, lowest_dense_res * lowest_dense_res * ae_out_ch)
self.upscale1 = Upscale(ae_out_ch, ae_out_ch)
def forward(self, inp):
x = inp
x = self.dense2(x)
x = nn.reshape_4D(x, lowest_dense_res, lowest_dense_res,
self.ae_out_ch)
x = self.upscale1(x)
return x
def get_out_ch(self):
return self.ae_out_ch
class Decoder(nn.ModelBase):
def on_build(self, in_ch, d_ch, d_mask_ch):
self.upscale0 = Upscale(in_ch, d_ch * 8, kernel_size=3)
self.upscale1 = Upscale(d_ch * 8, d_ch * 4, kernel_size=3)
self.upscale2 = Upscale(d_ch * 4, d_ch * 2, kernel_size=3)
self.res0 = ResidualBlock(d_ch * 8, kernel_size=3)
self.res1 = ResidualBlock(d_ch * 4, kernel_size=3)
self.res2 = ResidualBlock(d_ch * 2, kernel_size=3)
self.out_conv = nn.Conv2D(
d_ch * 2,
3,
kernel_size=1,
padding='SAME',
kernel_initializer=kernel_initializer)
def forward(self, inp):
z = inp
x = self.upscale0(z)
x = self.res0(x)
x = self.upscale1(x)
x = self.res1(x)
x = self.upscale2(x)
x = self.res2(x)
return tf.nn.sigmoid(self.out_conv(x))
n_pyr_degs = self.n_pyr_degs = 3
n_pyr_classes = self.n_pyr_classes = 180 // self.n_pyr_degs
with tf.device('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder(nn.floatx, bgr_shape)
self.target_src = tf.placeholder(nn.floatx, bgr_shape)
self.target_dst = tf.placeholder(nn.floatx, bgr_shape)
self.pitches_vector = tf.placeholder(nn.floatx,
(None, n_pyr_classes))
self.yaws_vector = tf.placeholder(nn.floatx, (None, n_pyr_classes))
# Initializing model classes
with tf.device(models_opt_device):
self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, name='encoder')
encoder_out_ch = self.encoder.compute_output_channels(
(nn.floatx, bgr_shape))
self.bT = Branch(in_ch=encoder_out_ch, ae_ch=ae_dims, name='bT')
self.bP = Branch(in_ch=encoder_out_ch, ae_ch=ae_dims, name='bP')
self.bTC = Classifier(in_ch=ae_dims,
n_classes=self.n_pyr_classes,
name='bTC')
self.bPC = Classifier(in_ch=ae_dims,
n_classes=self.n_pyr_classes,
name='bPC')
self.inter = Inter(in_ch=ae_dims * 2,
ae_out_ch=ae_dims * 2,
name='inter')
self.decoder = Decoder(in_ch=ae_dims * 2,
d_ch=d_dims,
d_mask_ch=d_dims,
name='decoder')
self.model_filename_list += [[self.encoder, 'encoder.npy'],
[self.bT, 'bT.npy'],
[self.bTC, 'bTC.npy'],
[self.bP, 'bP.npy'],
[self.bPC, 'bPC.npy'],
[self.inter, 'inter.npy'],
[self.decoder, 'decoder.npy']]
if self.is_training:
self.all_trainable_weights = self.encoder.get_weights() + \
self.bT.get_weights() +\
self.bTC.get_weights() +\
self.bP.get_weights() +\
self.bPC.get_weights() +\
self.inter.get_weights() +\
self.decoder.get_weights()
# Initialize optimizers
self.src_dst_opt = nn.RMSprop(lr=5e-5, name='src_dst_opt')
self.src_dst_opt.initialize_variables(
self.all_trainable_weights,
vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [(self.src_dst_opt,
'src_dst_opt.npy')]
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices))
bs_per_gpu = max(1, 32 // gpu_count)
self.set_batch_size(gpu_count * bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_list = []
gpu_pred_dst_list = []
gpu_A_losses = []
gpu_B_losses = []
gpu_C_losses = []
gpu_D_losses = []
gpu_A_loss_gvs = []
gpu_B_loss_gvs = []
gpu_C_loss_gvs = []
gpu_D_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device(
f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0'):
batch_slice = slice(gpu_id * bs_per_gpu,
(gpu_id + 1) * bs_per_gpu)
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
gpu_warped_src = self.warped_src[batch_slice, :, :, :]
gpu_target_src = self.target_src[batch_slice, :, :, :]
gpu_target_dst = self.target_dst[batch_slice, :, :, :]
gpu_pitches_vector = self.pitches_vector[
batch_slice, :]
gpu_yaws_vector = self.yaws_vector[batch_slice, :]
# process model tensors
gpu_src_enc_code = self.encoder(gpu_warped_src)
gpu_dst_enc_code = self.encoder(gpu_target_dst)
gpu_src_bT_code = self.bT(gpu_src_enc_code)
gpu_src_bT_code_ng = tf.stop_gradient(gpu_src_bT_code)
gpu_src_T_pitch, gpu_src_T_yaw = self.bTC(gpu_src_bT_code)
gpu_dst_bT_code = self.bT(gpu_dst_enc_code)
gpu_src_bP_code = self.bP(gpu_src_enc_code)
gpu_src_P_pitch, gpu_src_P_yaw = self.bPC(gpu_src_bP_code)
def crossentropy(target, output):
output = tf.nn.softmax(output)
output = tf.clip_by_value(output, 1e-7, 1 - 1e-7)
return tf.reduce_sum(target * -tf.log(output),
axis=-1,
keepdims=False)
def negative_crossentropy(n_classes, output):
output = tf.nn.softmax(output)
output = tf.clip_by_value(output, 1e-7, 1 - 1e-7)
return (1.0 / n_classes) * tf.reduce_sum(
tf.log(output), axis=-1, keepdims=False)
gpu_src_bT_code_n = gpu_src_bT_code_ng + tf.random.normal(
tf.shape(gpu_src_bT_code_ng))
gpu_src_bP_code_n = gpu_src_bP_code + tf.random.normal(
tf.shape(gpu_src_bP_code))
gpu_pred_src = self.decoder(
self.inter(
tf.concat([gpu_src_bT_code_ng, gpu_src_bP_code],
axis=-1)))
gpu_pred_src_n = self.decoder(
self.inter(
tf.concat([gpu_src_bT_code_n, gpu_src_bP_code_n],
axis=-1)))
gpu_pred_dst = self.decoder(
self.inter(
tf.concat([gpu_dst_bT_code, gpu_src_bP_code],
axis=-1)))
gpu_A_loss = 1.0*crossentropy(gpu_pitches_vector, gpu_src_T_pitch ) + \
1.0*crossentropy(gpu_yaws_vector, gpu_src_T_yaw )
gpu_B_loss = 0.1*crossentropy(gpu_pitches_vector, gpu_src_P_pitch ) + \
0.1*crossentropy(gpu_yaws_vector, gpu_src_P_yaw )
gpu_C_loss = 0.1*negative_crossentropy( n_pyr_classes, gpu_src_P_pitch ) + \
0.1*negative_crossentropy( n_pyr_classes, gpu_src_P_yaw )
gpu_D_loss = 0.0000001*(\
0.5*tf.reduce_sum(tf.square(gpu_target_src-gpu_pred_src), axis=[1,2,3]) + \
0.5*tf.reduce_sum(tf.square(gpu_target_src-gpu_pred_src_n), axis=[1,2,3]) )
gpu_pred_src_list.append(gpu_pred_src)
gpu_pred_dst_list.append(gpu_pred_dst)
gpu_A_losses += [gpu_A_loss]
gpu_B_losses += [gpu_B_loss]
gpu_C_losses += [gpu_C_loss]
gpu_D_losses += [gpu_D_loss]
A_weights = self.encoder.get_weights(
) + self.bT.get_weights() + self.bTC.get_weights()
B_weights = self.bPC.get_weights()
C_weights = self.encoder.get_weights(
) + self.bP.get_weights()
D_weights = self.inter.get_weights(
) + self.decoder.get_weights()
gpu_A_loss_gvs += [nn.gradients(gpu_A_loss, A_weights)]
gpu_B_loss_gvs += [nn.gradients(gpu_B_loss, B_weights)]
gpu_C_loss_gvs += [nn.gradients(gpu_C_loss, C_weights)]
gpu_D_loss_gvs += [nn.gradients(gpu_D_loss, D_weights)]
# Average losses and gradients, and create optimizer update ops
with tf.device(models_opt_device):
pred_src = nn.concat(gpu_pred_src_list, 0)
pred_dst = nn.concat(gpu_pred_dst_list, 0)
A_loss = nn.average_tensor_list(gpu_A_losses)
B_loss = nn.average_tensor_list(gpu_B_losses)
C_loss = nn.average_tensor_list(gpu_C_losses)
D_loss = nn.average_tensor_list(gpu_D_losses)
A_loss_gv = nn.average_gv_list(gpu_A_loss_gvs)
B_loss_gv = nn.average_gv_list(gpu_B_loss_gvs)
C_loss_gv = nn.average_gv_list(gpu_C_loss_gvs)
D_loss_gv = nn.average_gv_list(gpu_D_loss_gvs)
A_loss_gv_op = self.src_dst_opt.get_update_op(A_loss_gv)
B_loss_gv_op = self.src_dst_opt.get_update_op(B_loss_gv)
C_loss_gv_op = self.src_dst_opt.get_update_op(C_loss_gv)
D_loss_gv_op = self.src_dst_opt.get_update_op(D_loss_gv)
# Initializing training and view functions
def A_train(warped_src, target_src, pitches_vector, yaws_vector):
l, _ = nn.tf_sess.run(
[A_loss, A_loss_gv_op],
feed_dict={
self.warped_src: warped_src,
self.target_src: target_src,
self.pitches_vector: pitches_vector,
self.yaws_vector: yaws_vector
})
return np.mean(l)
self.A_train = A_train
def B_train(warped_src, target_src, pitches_vector, yaws_vector):
l, _ = nn.tf_sess.run(
[B_loss, B_loss_gv_op],
feed_dict={
self.warped_src: warped_src,
self.target_src: target_src,
self.pitches_vector: pitches_vector,
self.yaws_vector: yaws_vector
})
return np.mean(l)
self.B_train = B_train
def C_train(warped_src, target_src, pitches_vector, yaws_vector):
l, _ = nn.tf_sess.run(
[C_loss, C_loss_gv_op],
feed_dict={
self.warped_src: warped_src,
self.target_src: target_src,
self.pitches_vector: pitches_vector,
self.yaws_vector: yaws_vector
})
return np.mean(l)
self.C_train = C_train
def D_train(warped_src, target_src, pitches_vector, yaws_vector):
l, _ = nn.tf_sess.run(
[D_loss, D_loss_gv_op],
feed_dict={
self.warped_src: warped_src,
self.target_src: target_src,
self.pitches_vector: pitches_vector,
self.yaws_vector: yaws_vector
})
return np.mean(l)
self.D_train = D_train
def AE_view(warped_src):
return nn.tf_sess.run([pred_src],
feed_dict={self.warped_src: warped_src})
self.AE_view = AE_view
def AE_view2(warped_src, target_dst):
return nn.tf_sess.run([pred_dst],
feed_dict={
self.warped_src: warped_src,
self.target_dst: target_dst
})
self.AE_view2 = AE_view2
else:
# Initializing merge function
with tf.device(f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
def AE_merge(warped_dst):
return nn.tf_sess.run(
[gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm],
feed_dict={self.warped_dst: warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(
self.model_filename_list, "Initializing models"):
if self.pretrain_just_disabled:
do_init = False
if model == self.inter:
do_init = True
else:
do_init = self.is_first_run()
if not do_init:
do_init = not model.load_weights(
self.get_strpath_storage_for_file(filename))
if do_init and self.pretrained_model_path is not None:
pretrained_filepath = self.pretrained_model_path / filename
if pretrained_filepath.exists():
do_init = not model.load_weights(pretrained_filepath)
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path(
)
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path(
)
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
self.set_training_data_generators([
SampleGeneratorFace(
training_data_src_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True if self.pretrain else False),
output_sample_types=[
{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp': True,
'transform': True,
'channel_type': SampleProcessor.ChannelType.BGR,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution
},
{
'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
},
{
'sample_type':
SampleProcessor.SampleType.PITCH_YAW_ROLL_SIGMOID,
'resolution': resolution
},
],
generators_count=src_generators_count),
SampleGeneratorFace(
training_data_dst_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True if self.pretrain else False),
output_sample_types=[
{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp': True,
'transform': True,
'channel_type': SampleProcessor.ChannelType.BGR,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution
},
{
'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
},
{
'sample_type':
SampleProcessor.SampleType.PITCH_YAW_ROLL_SIGMOID,
'resolution': resolution
},
],
generators_count=dst_generators_count)
])
self.last_samples = None
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
self.model_data_format = "NCHW" if self.is_exporting or (len(
device_config.devices) != 0 and not self.is_debug()) else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.resolution = resolution = 256
self.face_type = {
'h': FaceType.HALF,
'mf': FaceType.MID_FULL,
'f': FaceType.FULL,
'wf': FaceType.WHOLE_FACE,
'head': FaceType.HEAD
}[self.options['face_type']]
place_model_on_cpu = len(devices) == 0
models_opt_device = '/CPU:0' if place_model_on_cpu else nn.tf_default_device_name
bgr_shape = nn.get4Dshape(resolution, resolution, 3)
mask_shape = nn.get4Dshape(resolution, resolution, 1)
# Initializing model classes
self.model = XSegNet(name='XSeg',
resolution=resolution,
load_weights=not self.is_first_run(),
weights_file_root=self.get_model_root_path(),
training=True,
place_model_on_cpu=place_model_on_cpu,
optimizer=nn.RMSprop(lr=0.0001,
lr_dropout=0.3,
name='opt'),
data_format=nn.data_format)
self.pretrain = self.options['pretrain']
if self.pretrain_just_disabled:
self.set_iter(0)
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices))
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size(gpu_count * bs_per_gpu)
# Compute losses per GPU
gpu_pred_list = []
gpu_losses = []
gpu_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device(f'/{devices[gpu_id].tf_dev_type}:{gpu_id}'
if len(devices) != 0 else f'/CPU:0'):
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
batch_slice = slice(gpu_id * bs_per_gpu,
(gpu_id + 1) * bs_per_gpu)
gpu_input_t = self.model.input_t[batch_slice, :, :, :]
gpu_target_t = self.model.target_t[
batch_slice, :, :, :]
# process model tensors
gpu_pred_logits_t, gpu_pred_t = self.model.flow(
gpu_input_t, pretrain=self.pretrain)
gpu_pred_list.append(gpu_pred_t)
if self.pretrain:
# Structural loss
gpu_loss = tf.reduce_mean(
5 * nn.dssim(gpu_target_t,
gpu_pred_t,
max_val=1.0,
filter_size=int(resolution / 11.6)),
axis=[1])
gpu_loss += tf.reduce_mean(
5 * nn.dssim(gpu_target_t,
gpu_pred_t,
max_val=1.0,
filter_size=int(resolution / 23.2)),
axis=[1])
# Pixel loss
gpu_loss += tf.reduce_mean(
10 * tf.square(gpu_target_t - gpu_pred_t),
axis=[1, 2, 3])
else:
gpu_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=gpu_target_t, logits=gpu_pred_logits_t),
axis=[1, 2, 3])
gpu_losses += [gpu_loss]
gpu_loss_gvs += [
nn.gradients(gpu_loss, self.model.get_weights())
]
# Average losses and gradients, and create optimizer update ops
#with tf.device(f'/CPU:0'): # Temporary fix. Unknown bug with training freeze starts from 2.4.0, but 2.3.1 was ok
with tf.device(models_opt_device):
pred = tf.concat(gpu_pred_list, 0)
loss = tf.concat(gpu_losses, 0)
loss_gv_op = self.model.opt.get_update_op(
nn.average_gv_list(gpu_loss_gvs))
# Initializing training and view functions
if self.pretrain:
def train(input_np, target_np):
l, _ = nn.tf_sess.run(
[loss, loss_gv_op],
feed_dict={
self.model.input_t: input_np,
self.model.target_t: target_np
})
return l
else:
def train(input_np, target_np):
l, _ = nn.tf_sess.run(
[loss, loss_gv_op],
feed_dict={
self.model.input_t: input_np,
self.model.target_t: target_np
})
return l
self.train = train
def view(input_np):
return nn.tf_sess.run([pred],
feed_dict={self.model.input_t: input_np})
self.view = view
# initializing sample generators
cpu_count = min(multiprocessing.cpu_count(), 8)
src_dst_generators_count = cpu_count // 2
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
if self.pretrain:
pretrain_gen = SampleGeneratorFace(
self.get_pretraining_data_path(),
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True),
output_sample_types=[
{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp': True,
'transform': True,
'channel_type': SampleProcessor.ChannelType.BGR,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution
},
{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp': True,
'transform': True,
'channel_type': SampleProcessor.ChannelType.G,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution
},
],
uniform_yaw_distribution=False,
generators_count=cpu_count)
self.set_training_data_generators([pretrain_gen])
else:
srcdst_generator = SampleGeneratorFaceXSeg(
[self.training_data_src_path, self.training_data_dst_path],
debug=self.is_debug(),
batch_size=self.get_batch_size(),
resolution=resolution,
face_type=self.face_type,
generators_count=src_dst_generators_count,
data_format=nn.data_format)
src_generator = SampleGeneratorFace(
self.training_data_src_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=False),
output_sample_types=[
{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp': False,
'transform': False,
'channel_type': SampleProcessor.ChannelType.BGR,
'border_replicate': False,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution
},
],
generators_count=src_generators_count,
raise_on_no_data=False)
dst_generator = SampleGeneratorFace(
self.training_data_dst_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=False),
output_sample_types=[
{
'sample_type':
SampleProcessor.SampleType.FACE_IMAGE,
'warp': False,
'transform': False,
'channel_type': SampleProcessor.ChannelType.BGR,
'border_replicate': False,
'face_type': self.face_type,
'data_format': nn.data_format,
'resolution': resolution
},
],
generators_count=dst_generators_count,
raise_on_no_data=False)
self.set_training_data_generators(
[srcdst_generator, src_generator, dst_generator])
def on_initialize(self):
nn.initialize()
tf = nn.tf
class EncBlock(nn.ModelBase):
def on_build(self, in_ch, out_ch, level):
self.zero_level = level == 0
self.conv1 = nn.Conv2D(in_ch,
out_ch,
kernel_size=3,
padding='SAME')
self.conv2 = nn.Conv2D(
out_ch,
out_ch,
kernel_size=4 if self.zero_level else 3,
padding='VALID' if self.zero_level else 'SAME')
def forward(self, x):
x = tf.nn.leaky_relu(self.conv1(x), 0.2)
x = tf.nn.leaky_relu(self.conv2(x), 0.2)
if not self.zero_level:
x = nn.max_pool(x)
return x
class DecBlock(nn.ModelBase):
def on_build(self, in_ch, out_ch, level):
self.zero_level = level == 0
self.conv1 = nn.Conv2D(
in_ch,
out_ch,
kernel_size=4 if self.zero_level else 3,
padding=3 if self.zero_level else 'SAME')
self.conv2 = nn.Conv2D(out_ch,
out_ch,
kernel_size=3,
padding='SAME')
def forward(self, x):
if not self.zero_level:
x = nn.upsample2d(x)
x = tf.nn.leaky_relu(self.conv1(x), 0.2)
x = tf.nn.leaky_relu(self.conv2(x), 0.2)
return x
class FromRGB(nn.ModelBase):
def on_build(self, out_ch):
self.conv1 = nn.Conv2D(3,
out_ch,
kernel_size=1,
padding='SAME')
def forward(self, x):
return tf.nn.leaky_relu(self.conv1(x), 0.2)
class ToRGB(nn.ModelBase):
def on_build(self, in_ch):
self.conv = nn.Conv2D(in_ch, 3, kernel_size=1, padding='SAME')
self.convm = nn.Conv2D(in_ch, 1, kernel_size=1, padding='SAME')
def forward(self, x):
return tf.nn.sigmoid(self.conv(x)), tf.nn.sigmoid(
self.convm(x))
class Encoder(nn.ModelBase):
def on_build(self, e_ch, levels):
self.enc_blocks = {}
self.from_rgbs = {}
self.dense_norm = nn.DenseNorm()
in_ch = e_ch
out_ch = in_ch
for level in range(levels, -1, -1):
self.max_ch = out_ch = np.clip(out_ch * 2, 0, 512)
self.enc_blocks[level] = EncBlock(in_ch, out_ch, level)
self.from_rgbs[level] = FromRGB(in_ch)
in_ch = out_ch
def forward(self, inp, stage):
x = inp
for level in range(stage, -1, -1):
if stage in self.enc_blocks:
if level == stage:
x = self.from_rgbs[level](x)
x = self.enc_blocks[level](x)
x = nn.flatten(x)
x = self.dense_norm(x)
x = nn.reshape_4D(x, 1, 1, self.max_ch)
return x
def get_stage_weights(self, stage):
self.get_weights()
weights = []
for level in range(stage, -1, -1):
if stage in self.enc_blocks:
if level == stage:
weights.append(self.from_rgbs[level].get_weights())
weights.append(self.enc_blocks[level].get_weights())
if len(weights) == 0:
return []
elif len(weights) == 1:
return weights[0]
else:
return sum(weights[1:], weights[0])
class Decoder(nn.ModelBase):
def on_build(self, d_ch, total_levels, levels_range):
self.dec_blocks = {}
self.to_rgbs = {}
level_ch = {}
ch = d_ch
for level in range(total_levels, -2, -1):
level_ch[level] = ch
ch = np.clip(ch * 2, 0, 512)
out_ch = level_ch[levels_range[1]]
for level in range(levels_range[1], levels_range[0] - 1, -1):
in_ch = level_ch[level - 1]
self.dec_blocks[level] = DecBlock(in_ch, out_ch, level)
self.to_rgbs[level] = ToRGB(out_ch)
out_ch = in_ch
def forward(self, inp, stage):
x = inp
for level in range(stage + 1):
if level in self.dec_blocks:
x = self.dec_blocks[level](x)
if level == stage:
x = self.to_rgbs[level](x)
return x
def get_stage_weights(self, stage):
# Call internal get_weights in order to initialize inner logic
self.get_weights()
weights = []
for level in range(stage + 1):
if level in self.dec_blocks:
weights.append(self.dec_blocks[level].get_weights())
if level == stage:
weights.append(self.to_rgbs[level].get_weights())
if len(weights) == 0:
return []
elif len(weights) == 1:
return weights[0]
else:
return sum(weights[1:], weights[0])
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.stage = stage = self.options['stage']
self.start_stage_iter = self.options.get('start_stage_iter', 0)
self.target_stage_iter = self.options.get('target_stage_iter', 0)
stage_resolutions = [2**(i + 2) for i in range(self.stage_max + 1)]
stage_resolution = stage_resolutions[stage]
ed_dims = 16
self.pretrain = False
self.pretrain_just_disabled = False
masked_training = True
models_opt_on_gpu = len(devices) == 1 and devices[0].total_mem_gb >= 4
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device == '/CPU:0'
input_nc = 3
output_nc = 3
bgr_shape = (stage_resolution, stage_resolution, output_nc)
mask_shape = (stage_resolution, stage_resolution, 1)
self.model_filename_list = []
with tf.device('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder(tf.float32, (None, ) + bgr_shape)
self.warped_dst = tf.placeholder(tf.float32, (None, ) + bgr_shape)
self.target_src = tf.placeholder(tf.float32, (None, ) + bgr_shape)
self.target_dst = tf.placeholder(tf.float32, (None, ) + bgr_shape)
self.target_srcm = tf.placeholder(tf.float32,
(None, ) + mask_shape)
self.target_dstm = tf.placeholder(tf.float32,
(None, ) + mask_shape)
# Initializing model classes
with tf.device(models_opt_device):
self.encoder = Encoder(e_ch=ed_dims,
levels=self.stage_max,
name='encoder')
self.inter = Decoder(d_ch=ed_dims,
total_levels=self.stage_max,
levels_range=[0, 2],
name='inter')
self.decoder_src = Decoder(d_ch=ed_dims,
total_levels=self.stage_max,
levels_range=[3, self.stage_max],
name='decoder_src')
self.decoder_dst = Decoder(d_ch=ed_dims,
total_levels=self.stage_max,
levels_range=[3, self.stage_max],
name='decoder_dst')
self.model_filename_list += [[self.encoder, 'encoder.npy'],
[self.inter, 'inter.npy'],
[self.decoder_src, 'decoder_src.npy'],
[self.decoder_dst, 'decoder_dst.npy']]
if self.is_training:
self.src_dst_all_weights = self.encoder.get_weights(
) + self.inter.get_weights() + self.decoder_src.get_weights(
) + self.decoder_dst.get_weights()
self.src_dst_trainable_weights = self.encoder.get_stage_weights(stage) + self.inter.get_stage_weights(stage) \
+ self.decoder_src.get_stage_weights(stage) \
+ self.decoder_dst.get_stage_weights(stage)
# Initialize optimizers
self.src_dst_opt = nn.RMSprop(lr=2e-4,
lr_dropout=0.3,
name='src_dst_opt')
self.src_dst_opt.initialize_variables(
self.src_dst_all_weights,
vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [(self.src_dst_opt,
'src_dst_opt.npy')]
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices))
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size(gpu_count * bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device(
f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0'):
batch_slice = slice(gpu_id * bs_per_gpu,
(gpu_id + 1) * bs_per_gpu)
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
gpu_warped_src = self.warped_src[batch_slice, :, :, :]
gpu_warped_dst = self.warped_dst[batch_slice, :, :, :]
gpu_target_src = self.target_src[batch_slice, :, :, :]
gpu_target_dst = self.target_dst[batch_slice, :, :, :]
gpu_target_srcm = self.target_srcm[
batch_slice, :, :, :]
gpu_target_dstm = self.target_dstm[
batch_slice, :, :, :]
# process model tensors
gpu_src_code = self.inter(
self.encoder(gpu_warped_src, stage), stage)
gpu_dst_code = self.inter(
self.encoder(gpu_warped_dst, stage), stage)
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(
gpu_src_code, stage)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(
gpu_dst_code, stage)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
gpu_dst_code, stage)
import code
code.interact(local=dict(globals(), **locals()))
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
gpu_target_srcm_blur = nn.gaussian_blur(
gpu_target_srcm, max(1, resolution // 32))
gpu_target_dstm_blur = nn.gaussian_blur(
gpu_target_dstm, max(1, resolution // 32))
gpu_target_dst_masked = gpu_target_dst * gpu_target_dstm_blur
gpu_target_dst_anti_masked = gpu_target_dst * (
1.0 - gpu_target_dstm_blur)
gpu_target_srcmasked_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_masked = gpu_pred_src_dst * gpu_target_dstm_blur
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst * (
1.0 - gpu_target_dstm_blur)
gpu_src_loss = tf.reduce_mean(
10 * nn.dssim(gpu_target_srcmasked_opt,
gpu_pred_src_src_masked_opt,
max_val=1.0,
filter_size=int(resolution / 11.6)),
axis=[1])
gpu_src_loss += tf.reduce_mean(
10 * tf.square(gpu_target_srcmasked_opt -
gpu_pred_src_src_masked_opt),
axis=[1, 2, 3])
gpu_src_loss += tf.reduce_mean(
tf.square(gpu_target_srcm - gpu_pred_src_srcm),
axis=[1, 2, 3])
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])
gpu_dst_loss += tf.reduce_mean(
10 * tf.square(gpu_target_dst_masked_opt -
gpu_pred_dst_dst_masked_opt),
axis=[1, 2, 3])
gpu_dst_loss += tf.reduce_mean(
tf.square(gpu_target_dstm - gpu_pred_dst_dstm),
axis=[1, 2, 3])
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_src_dst_loss = gpu_src_loss + gpu_dst_loss
gpu_src_dst_loss_gvs += [
nn.gradients(gpu_src_dst_loss,
self.src_dst_trainable_weights)
]
# Average losses and gradients, and create optimizer update ops
with tf.device(models_opt_device):
if gpu_count == 1:
pred_src_src = gpu_pred_src_src_list[0]
pred_dst_dst = gpu_pred_dst_dst_list[0]
pred_src_dst = gpu_pred_src_dst_list[0]
pred_src_srcm = gpu_pred_src_srcm_list[0]
pred_dst_dstm = gpu_pred_dst_dstm_list[0]
pred_src_dstm = gpu_pred_src_dstm_list[0]
src_loss = gpu_src_losses[0]
dst_loss = gpu_dst_losses[0]
src_dst_loss_gv = gpu_src_dst_loss_gvs[0]
else:
pred_src_src = tf.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = tf.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = tf.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = tf.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = tf.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = tf.concat(gpu_pred_src_dstm_list, 0)
src_loss = nn.average_tensor_list(gpu_src_losses)
dst_loss = nn.average_tensor_list(gpu_dst_losses)
src_dst_loss_gv = nn.average_gv_list(gpu_src_dst_loss_gvs)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op(
src_dst_loss_gv)
# Initializing training and view functions
def src_dst_train(warped_src, target_src, target_srcm, \
warped_dst, target_dst, target_dstm):
s, d, _ = nn.tf_sess.run(
[src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={
self.warped_src: warped_src,
self.target_src: target_src,
self.target_srcm: target_srcm,
self.warped_dst: warped_dst,
self.target_dst: target_dst,
self.target_dstm: target_dstm,
})
s = np.mean(s)
d = np.mean(d)
return s, d
self.src_dst_train = src_dst_train
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run([
pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst,
pred_src_dstm
],
feed_dict={
self.warped_src: warped_src,
self.warped_dst: warped_dst
})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device(f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(
gpu_dst_code, stage=stage)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code,
stage=stage)
def AE_merge(warped_dst):
return nn.tf_sess.run(
[gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm],
feed_dict={self.warped_dst: warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(
self.model_filename_list, "Initializing models"):
do_init = self.is_first_run()
if self.pretrain_just_disabled:
if model == self.inter:
do_init = True
if not do_init:
do_init = not model.load_weights(
self.get_strpath_storage_for_file(filename))
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
t = SampleProcessor.Types
face_type = t.FACE_TYPE_FULL
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path(
)
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path(
)
cpu_count = multiprocessing.cpu_count()
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count - src_generators_count
self.set_training_data_generators([
SampleGeneratorFace(
training_data_src_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True if self.pretrain else False),
output_sample_types=[{
'types':
(t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR),
'resolution':
resolution,
}, {
'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR),
'resolution':
resolution,
}, {
'types': (t.IMG_TRANSFORMED, face_type,
t.MODE_FACE_MASK_ALL_HULL),
'resolution':
resolution
}],
generators_count=src_generators_count),
SampleGeneratorFace(
training_data_dst_path,
debug=self.is_debug(),
batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(
random_flip=True if self.pretrain else False),
output_sample_types=[{
'types':
(t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR),
'resolution':
resolution
}, {
'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR),
'resolution':
resolution
}, {
'types': (t.IMG_TRANSFORMED, face_type,
t.MODE_FACE_MASK_ALL_HULL),
'resolution':
resolution
}],
generators_count=dst_generators_count)
])
self.last_samples = None
def main(model_class_name=None,
saved_models_path=None,
training_data_src_path=None,
force_model_name=None,
input_path=None,
output_path=None,
output_mask_path=None,
aligned_path=None,
force_gpu_idxs=None,
cpu_only=None):
io.log_info("Running merger.\r\n")
try:
if not input_path.exists():
io.log_err('Input directory not found. Please ensure it exists.')
return
if not output_path.exists():
output_path.mkdir(parents=True, exist_ok=True)
if not output_mask_path.exists():
output_mask_path.mkdir(parents=True, exist_ok=True)
if not saved_models_path.exists():
io.log_err('Model directory not found. Please ensure it exists.')
return
# Initialize model
import models
model = models.import_model(model_class_name)(
is_training=False,
saved_models_path=saved_models_path,
force_gpu_idxs=force_gpu_idxs,
cpu_only=cpu_only)
predictor_func, predictor_input_shape, cfg = model.get_MergerConfig()
# Preparing MP functions
predictor_func = MPFunc(predictor_func)
run_on_cpu = len(nn.getCurrentDeviceConfig().devices) == 0
xseg_256_extract_func = MPClassFuncOnDemand(
XSegNet,
'extract',
name='XSeg',
resolution=256,
weights_file_root=saved_models_path,
place_model_on_cpu=True,
run_on_cpu=run_on_cpu)
face_enhancer_func = MPClassFuncOnDemand(FaceEnhancer,
'enhance',
place_model_on_cpu=True,
run_on_cpu=run_on_cpu)
is_interactive = io.input_bool("Use interactive merger?",
True) if not io.is_colab() else False
# if not is_interactive:
# cfg.ask_settings()
subprocess_count = multiprocessing.cpu_count()
# subprocess_count = io.input_int("Number of workers?", max(8, multiprocessing.cpu_count()),
# valid_range=[1, multiprocessing.cpu_count()], help_message="Specify the number of threads to process. A low value may affect performance. A high value may result in memory error. The value may not be greater than CPU cores." )
input_path_image_paths = pathex.get_image_paths(input_path)
if cfg.type == MergerConfig.TYPE_MASKED:
if not aligned_path.exists():
io.log_err(
'Aligned directory not found. Please ensure it exists.')
return
packed_samples = None
try:
packed_samples = samplelib.PackedFaceset.load(aligned_path)
except:
io.log_err(
f"Error occured while loading samplelib.PackedFaceset.load {str(aligned_path)}, {traceback.format_exc()}"
)
if packed_samples is not None:
io.log_info("Using packed faceset.")
def generator():
for sample in io.progress_bar_generator(
packed_samples, "Collecting alignments"):
filepath = Path(sample.filename)
yield filepath, DFLIMG.load(
filepath,
loader_func=lambda x: sample.read_raw_file())
else:
def generator():
for filepath in io.progress_bar_generator(
pathex.get_image_paths(aligned_path),
"Collecting alignments"):
filepath = Path(filepath)
yield filepath, DFLIMG.load(filepath)
alignments = {}
multiple_faces_detected = False
for filepath, dflimg in generator():
if dflimg is None or not dflimg.has_data():
io.log_err(f"{filepath.name} is not a dfl image file")
continue
source_filename = dflimg.get_source_filename()
if source_filename is None:
continue
source_filepath = Path(source_filename)
source_filename_stem = source_filepath.stem
if source_filename_stem not in alignments.keys():
alignments[source_filename_stem] = []
alignments_ar = alignments[source_filename_stem]
alignments_ar.append(
(dflimg.get_source_landmarks(), filepath, source_filepath))
if len(alignments_ar) > 1:
multiple_faces_detected = True
if multiple_faces_detected:
io.log_info("")
io.log_info(
"Warning: multiple faces detected. Only one alignment file should refer one source file."
)
io.log_info("")
for a_key in list(alignments.keys()):
a_ar = alignments[a_key]
if len(a_ar) > 1:
for _, filepath, source_filepath in a_ar:
io.log_info(
f"alignment {filepath.name} refers to {source_filepath.name} "
)
io.log_info("")
alignments[a_key] = [a[0] for a in a_ar]
if multiple_faces_detected:
io.log_info(
"It is strongly recommended to process the faces separatelly."
)
io.log_info(
"Use 'recover original filename' to determine the exact duplicates."
)
io.log_info("")
frames = [
InteractiveMergerSubprocessor.Frame(frame_info=FrameInfo(
filepath=Path(p),
landmarks_list=alignments.get(Path(p).stem, None)))
for p in input_path_image_paths
]
if multiple_faces_detected:
io.log_info(
"Warning: multiple faces detected. Motion blur will not be used."
)
io.log_info("")
else:
s = 256
local_pts = [(s // 2 - 1, s // 2 - 1),
(s // 2 - 1, 0)] #center+up
frames_len = len(frames)
for i in io.progress_bar_generator(range(len(frames)),
"Computing motion vectors"):
fi_prev = frames[max(0, i - 1)].frame_info
fi = frames[i].frame_info
fi_next = frames[min(i + 1, frames_len - 1)].frame_info
if len(fi_prev.landmarks_list) == 0 or \
len(fi.landmarks_list) == 0 or \
len(fi_next.landmarks_list) == 0:
continue
mat_prev = LandmarksProcessor.get_transform_mat(
fi_prev.landmarks_list[0], s, face_type=FaceType.FULL)
mat = LandmarksProcessor.get_transform_mat(
fi.landmarks_list[0], s, face_type=FaceType.FULL)
mat_next = LandmarksProcessor.get_transform_mat(
fi_next.landmarks_list[0], s, face_type=FaceType.FULL)
pts_prev = LandmarksProcessor.transform_points(
local_pts, mat_prev, True)
pts = LandmarksProcessor.transform_points(
local_pts, mat, True)
pts_next = LandmarksProcessor.transform_points(
local_pts, mat_next, True)
prev_vector = pts[0] - pts_prev[0]
next_vector = pts_next[0] - pts[0]
motion_vector = pts_next[0] - pts_prev[0]
fi.motion_power = npla.norm(motion_vector)
motion_vector = motion_vector / fi.motion_power if fi.motion_power != 0 else np.array(
[0, 0], dtype=np.float32)
fi.motion_deg = -math.atan2(
motion_vector[1], motion_vector[0]) * 180 / math.pi
if len(frames) == 0:
io.log_info("No frames to merge in input_dir.")
else:
if False:
pass
else:
InteractiveMergerSubprocessor(
is_interactive=is_interactive,
merger_session_filepath=model.get_strpath_storage_for_file(
'merger_session.dat'),
predictor_func=predictor_func,
predictor_input_shape=predictor_input_shape,
face_enhancer_func=face_enhancer_func,
xseg_256_extract_func=xseg_256_extract_func,
merger_config=cfg,
frames=frames,
frames_root_path=input_path,
output_path=output_path,
output_mask_path=output_mask_path,
model_iter=model.get_iter(),
subprocess_count=subprocess_count,
).run()
model.finalize()
except Exception as e:
print(traceback.format_exc())
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.model_data_format = "NCHW"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
self.resolution = resolution = self.options['resolution']
lowest_dense_res = self.lowest_dense_res = resolution // 32
class Downscale(nn.ModelBase):
def __init__(self, in_ch, out_ch, kernel_size=5, *kwargs ):
self.in_ch = in_ch
self.out_ch = out_ch
self.kernel_size = kernel_size
super().__init__(*kwargs)
def on_build(self, *args, **kwargs ):
self.conv1 = nn.Conv2D( self.in_ch, self.out_ch, kernel_size=self.kernel_size, strides=2, padding='SAME')
def forward(self, x):
x = self.conv1(x)
x = tf.nn.leaky_relu(x, 0.1)
return x
def get_out_ch(self):
return self.out_ch
class Upscale(nn.ModelBase):
def on_build(self, in_ch, out_ch, kernel_size=3 ):
self.conv1 = nn.Conv2D( in_ch, out_ch*4, kernel_size=kernel_size, padding='SAME')
def forward(self, x):
x = self.conv1(x)
x = tf.nn.leaky_relu(x, 0.1)
x = nn.depth_to_space(x, 2)
return x
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3 ):
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
def forward(self, inp):
x = self.conv1(inp)
x = tf.nn.leaky_relu(x, 0.2)
x = self.conv2(x)
x = tf.nn.leaky_relu(inp+x, 0.2)
return x
class Encoder(nn.ModelBase):
def on_build(self, in_ch, e_ch, ae_ch):
self.down1 = Downscale(in_ch, e_ch, kernel_size=5)
self.res1 = ResidualBlock(e_ch)
self.down2 = Downscale(e_ch, e_ch*2, kernel_size=5)
self.down3 = Downscale(e_ch*2, e_ch*4, kernel_size=5)
self.down4 = Downscale(e_ch*4, e_ch*8, kernel_size=5)
self.down5 = Downscale(e_ch*8, e_ch*8, kernel_size=5)
self.res5 = ResidualBlock(e_ch*8)
self.dense1 = nn.Dense( lowest_dense_res*lowest_dense_res*e_ch*8, ae_ch )
def forward(self, inp):
x = inp
x = self.down1(x)
x = self.res1(x)
x = self.down2(x)
x = self.down3(x)
x = self.down4(x)
x = self.down5(x)
x = self.res5(x)
x = nn.flatten(x)
x = nn.pixel_norm(x, axes=-1)
x = self.dense1(x)
return x
class Inter(nn.ModelBase):
def __init__(self, ae_ch, ae_out_ch, **kwargs):
self.ae_ch, self.ae_out_ch = ae_ch, ae_out_ch
super().__init__(**kwargs)
def on_build(self):
ae_ch, ae_out_ch = self.ae_ch, self.ae_out_ch
self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch )
def forward(self, inp):
x = inp
x = self.dense2(x)
x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, self.ae_out_ch)
return x
def get_out_ch(self):
return self.ae_out_ch
class Decoder(nn.ModelBase):
def on_build(self, in_ch, d_ch, d_mask_ch ):
self.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3)
self.upscale1 = Upscale(d_ch*8, d_ch*8, kernel_size=3)
self.upscale2 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
self.upscale3 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
self.res0 = ResidualBlock(d_ch*8, kernel_size=3)
self.res1 = ResidualBlock(d_ch*8, kernel_size=3)
self.res2 = ResidualBlock(d_ch*4, kernel_size=3)
self.res3 = ResidualBlock(d_ch*2, kernel_size=3)
self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3)
self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*8, kernel_size=3)
self.upscalem2 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3)
self.upscalem3 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3)
self.upscalem4 = Upscale(d_mask_ch*2, d_mask_ch*1, kernel_size=3)
self.out_convm = nn.Conv2D( d_mask_ch*1, 1, kernel_size=1, padding='SAME')
self.out_conv = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME')
self.out_conv1 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME')
self.out_conv2 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME')
self.out_conv3 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME')
def forward(self, inp):
z = inp
x = self.upscale0(z)
x = self.res0(x)
x = self.upscale1(x)
x = self.res1(x)
x = self.upscale2(x)
x = self.res2(x)
x = self.upscale3(x)
x = self.res3(x)
x = tf.nn.sigmoid( nn.depth_to_space(tf.concat( (self.out_conv(x),
self.out_conv1(x),
self.out_conv2(x),
self.out_conv3(x)), nn.conv2d_ch_axis), 2) )
m = self.upscalem0(z)
m = self.upscalem1(m)
m = self.upscalem2(m)
m = self.upscalem3(m)
m = self.upscalem4(m)
m = tf.nn.sigmoid(self.out_convm(m))
return x, m
self.face_type = {'wf' : FaceType.WHOLE_FACE,
'head' : FaceType.HEAD}[ self.options['face_type'] ]
if 'eyes_prio' in self.options:
self.options.pop('eyes_prio')
eyes_mouth_prio = self.options['eyes_mouth_prio']
ae_dims = self.ae_dims = self.options['ae_dims']
e_dims = self.options['e_dims']
d_dims = self.options['d_dims']
d_mask_dims = self.options['d_mask_dims']
morph_factor = self.options['morph_factor']
pretrain = self.pretrain = self.options['pretrain']
if self.pretrain_just_disabled:
self.set_iter(0)
self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
random_warp = False if self.pretrain else self.options['random_warp']
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
if self.pretrain:
self.options_show_override['gan_power'] = 0.0
self.options_show_override['random_warp'] = False
self.options_show_override['lr_dropout'] = 'n'
self.options_show_override['uniform_yaw'] = True
masked_training = self.options['masked_training']
ct_mode = self.options['ct_mode']
if ct_mode == 'none':
ct_mode = None
models_opt_on_gpu = False if len(devices) == 0 else self.options['models_opt_on_gpu']
models_opt_device = nn.tf_default_device_name if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
input_ch=3
bgr_shape = self.bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
mask_shape = nn.get4Dshape(resolution,resolution,1)
self.model_filename_list = []
with tf.device ('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder (nn.floatx, bgr_shape, name='warped_src')
self.warped_dst = tf.placeholder (nn.floatx, bgr_shape, name='warped_dst')
self.target_src = tf.placeholder (nn.floatx, bgr_shape, name='target_src')
self.target_dst = tf.placeholder (nn.floatx, bgr_shape, name='target_dst')
self.target_srcm = tf.placeholder (nn.floatx, mask_shape, name='target_srcm')
self.target_srcm_em = tf.placeholder (nn.floatx, mask_shape, name='target_srcm_em')
self.target_dstm = tf.placeholder (nn.floatx, mask_shape, name='target_dstm')
self.target_dstm_em = tf.placeholder (nn.floatx, mask_shape, name='target_dstm_em')
self.morph_value_t = tf.placeholder (nn.floatx, (1,), name='morph_value_t')
# Initializing model classes
with tf.device (models_opt_device):
self.encoder = Encoder(in_ch=input_ch, e_ch=e_dims, ae_ch=ae_dims, name='encoder')
self.inter_src = Inter(ae_ch=ae_dims, ae_out_ch=ae_dims, name='inter_src')
self.inter_dst = Inter(ae_ch=ae_dims, ae_out_ch=ae_dims, name='inter_dst')
self.decoder = Decoder(in_ch=ae_dims, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder')
self.model_filename_list += [ [self.encoder, 'encoder.npy'],
[self.inter_src, 'inter_src.npy'],
[self.inter_dst , 'inter_dst.npy'],
[self.decoder , 'decoder.npy'] ]
if self.is_training:
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'], name="GAN")
self.model_filename_list += [ [self.GAN, 'GAN.npy'] ]
# Initialize optimizers
lr=5e-5
lr_dropout = 0.3 if self.options['lr_dropout'] in ['y','cpu'] and not self.pretrain else 1.0
clipnorm = 1.0 if self.options['clipgrad'] else 0.0
self.all_weights = self.encoder.get_weights() + self.inter_src.get_weights() + self.inter_dst.get_weights() + self.decoder.get_weights()
if pretrain:
self.trainable_weights = self.encoder.get_weights() + self.inter_dst.get_weights() + self.decoder.get_weights()
else:
self.trainable_weights = self.encoder.get_weights() + self.inter_src.get_weights() + self.inter_dst.get_weights() + self.decoder.get_weights()
self.src_dst_opt = nn.AdaBelief(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
self.src_dst_opt.initialize_variables (self.all_weights, vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
if gan_power != 0:
self.GAN_opt = nn.AdaBelief(lr=lr, lr_dropout=lr_dropout, 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
gpu_count = max(1, len(devices) )
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size( gpu_count*bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_G_loss_gvs = []
gpu_GAN_loss_gvs = []
gpu_D_code_loss_gvs = []
gpu_D_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device( f'/{devices[gpu_id].tf_dev_type}:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
gpu_warped_src = self.warped_src [batch_slice,:,:,:]
gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
gpu_target_src = self.target_src [batch_slice,:,:,:]
gpu_target_dst = self.target_dst [batch_slice,:,:,:]
gpu_target_srcm = self.target_srcm[batch_slice,:,:,:]
gpu_target_srcm_em = self.target_srcm_em[batch_slice,:,:,:]
gpu_target_dstm = self.target_dstm[batch_slice,:,:,:]
gpu_target_dstm_em = self.target_dstm_em[batch_slice,:,:,:]
# process model tensors
gpu_src_code = self.encoder (gpu_warped_src)
gpu_dst_code = self.encoder (gpu_warped_dst)
if pretrain:
gpu_src_inter_src_code = self.inter_src (gpu_src_code)
gpu_dst_inter_dst_code = self.inter_dst (gpu_dst_code)
gpu_src_code = gpu_src_inter_src_code * nn.random_binomial( [bs_per_gpu, gpu_src_inter_src_code.shape.as_list()[1], 1,1] , p=morph_factor)
gpu_dst_code = gpu_src_dst_code = gpu_dst_inter_dst_code * nn.random_binomial( [bs_per_gpu, gpu_dst_inter_dst_code.shape.as_list()[1], 1,1] , p=0.25)
else:
gpu_src_inter_src_code = self.inter_src (gpu_src_code)
gpu_src_inter_dst_code = self.inter_dst (gpu_src_code)
gpu_dst_inter_src_code = self.inter_src (gpu_dst_code)
gpu_dst_inter_dst_code = self.inter_dst (gpu_dst_code)
inter_rnd_binomial = nn.random_binomial( [bs_per_gpu, gpu_src_inter_src_code.shape.as_list()[1], 1,1] , p=morph_factor)
gpu_src_code = gpu_src_inter_src_code * inter_rnd_binomial + gpu_src_inter_dst_code * (1-inter_rnd_binomial)
gpu_dst_code = gpu_dst_inter_dst_code
ae_dims_slice = tf.cast(ae_dims*self.morph_value_t[0], tf.int32)
gpu_src_dst_code = tf.concat( (tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, ae_dims_slice , lowest_dense_res, lowest_dense_res]),
tf.slice(gpu_dst_inter_dst_code, [0,ae_dims_slice,0,0], [-1,ae_dims-ae_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
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_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
gpu_target_srcm_blur = nn.gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_blur, 0, 0.5) * 2
gpu_target_dstm_blur = nn.gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
gpu_target_dst_anti_masked = gpu_target_dst*(1.0-gpu_target_dstm_blur)
gpu_target_src_anti_masked = gpu_target_src*(1.0-gpu_target_srcm_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*gpu_target_dstm_blur 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_src_src_anti_masked = gpu_pred_src_src*(1.0-gpu_target_srcm_blur)
gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
gpu_pred_dst_dst_anti_masked = gpu_pred_dst_dst*(1.0-gpu_target_dstm_blur)
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])
else:
gpu_dst_loss = tf.reduce_mean ( 5*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])
gpu_dst_loss += tf.reduce_mean ( 5*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
if eyes_mouth_prio:
gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_em - gpu_pred_dst_dst*gpu_target_dstm_em ), axis=[1,2,3])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
gpu_dst_loss += 0.1*tf.reduce_mean(tf.square(gpu_pred_dst_dst_anti_masked-gpu_target_dst_anti_masked),axis=[1,2,3] )
gpu_dst_losses += [gpu_dst_loss]
if not pretrain:
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:
gpu_src_loss = tf.reduce_mean ( 5*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])
gpu_src_loss += tf.reduce_mean ( 5*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
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])
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
else:
gpu_src_loss = gpu_dst_loss
gpu_src_losses += [gpu_src_loss]
if pretrain:
gpu_G_loss = gpu_dst_loss
else:
gpu_G_loss = gpu_src_loss + gpu_dst_loss
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])
def DLossZeros(logits):
return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(logits), logits=logits), axis=[1,2,3])
if gan_power != 0:
gpu_pred_src_src_d, gpu_pred_src_src_d2 = self.GAN(gpu_pred_src_src_masked_opt)
gpu_pred_dst_dst_d, gpu_pred_dst_dst_d2 = self.GAN(gpu_pred_dst_dst_masked_opt)
gpu_target_src_d, gpu_target_src_d2 = self.GAN(gpu_target_src_masked_opt)
gpu_target_dst_d, gpu_target_dst_d2 = self.GAN(gpu_target_dst_masked_opt)
gpu_D_src_dst_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)
gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_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
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_gvs += [ nn.gradients ( gpu_G_loss, self.trainable_weights ) ]
# Average losses and gradients, and create optimizer update ops
with tf.device(f'/CPU:0'):
pred_src_src = nn.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = nn.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = nn.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
with tf.device (models_opt_device):
src_loss = tf.concat(gpu_src_losses, 0)
dst_loss = tf.concat(gpu_dst_losses, 0)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op (nn.average_gv_list (gpu_G_loss_gvs))
if gan_power != 0:
src_D_src_dst_loss_gv_op = self.GAN_opt.get_update_op (nn.average_gv_list(gpu_D_src_dst_loss_gvs) )
#GAN_loss_gv_op = self.src_dst_opt.get_update_op (nn.average_gv_list(gpu_GAN_loss_gvs) )
# Initializing training and view functions
def src_dst_train(warped_src, target_src, target_srcm, target_srcm_em, \
warped_dst, target_dst, target_dstm, target_dstm_em, ):
s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm:target_srcm,
self.target_srcm_em:target_srcm_em,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm:target_dstm,
self.target_dstm_em:target_dstm_em,
})
return s, d
self.src_dst_train = src_dst_train
if gan_power != 0:
def D_src_dst_train(warped_src, target_src, target_srcm, target_srcm_em, \
warped_dst, target_dst, target_dstm, target_dstm_em, ):
nn.tf_sess.run ([src_D_src_dst_loss_gv_op], feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm:target_srcm,
self.target_srcm_em:target_srcm_em,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm:target_dstm,
self.target_dstm_em:target_dstm_em})
self.D_src_dst_train = D_src_dst_train
def AE_view(warped_src, warped_dst, morph_value):
return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
feed_dict={self.warped_src:warped_src, self.warped_dst:warped_dst, self.morph_value_t:[morph_value] })
self.AE_view = AE_view
else:
#Initializing merge function
with tf.device( nn.tf_default_device_name if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.encoder (self.warped_dst)
gpu_dst_inter_src_code = self.inter_src ( gpu_dst_code)
gpu_dst_inter_dst_code = self.inter_dst ( gpu_dst_code)
ae_dims_slice = tf.cast(ae_dims*self.morph_value_t[0], tf.int32)
gpu_src_dst_code = tf.concat( ( tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, ae_dims_slice , lowest_dense_res, lowest_dense_res]),
tf.slice(gpu_dst_inter_dst_code, [0,ae_dims_slice,0,0], [-1,ae_dims-ae_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_inter_dst_code)
def AE_merge(warped_dst, morph_value):
return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst, self.morph_value_t:[morph_value] })
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
if self.pretrain_just_disabled:
do_init = False
if model == self.inter_src or model == self.inter_dst:
do_init = True
else:
do_init = self.is_first_run()
if self.is_training and gan_power != 0 and model == self.GAN:
if self.gan_model_changed:
do_init = True
if not do_init:
do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
if do_init:
model.init_weights()
###############
# initializing sample generators
if self.is_training:
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path()
random_ct_samples_path=training_data_dst_path if ct_mode is not None and not self.pretrain else None
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
if ct_mode is not None:
src_generators_count = int(src_generators_count * 1.5)
self.set_training_data_generators ([
SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=random_src_flip),
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},
{'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},
{'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},
{'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},
],
uniform_yaw_distribution=self.options['uniform_yaw'] or self.pretrain,
generators_count=src_generators_count ),
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=random_dst_flip),
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},
{'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},
{'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},
{'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},
],
uniform_yaw_distribution=self.options['uniform_yaw'] or self.pretrain,
generators_count=dst_generators_count )
])
self.last_src_samples_loss = []
self.last_dst_samples_loss = []
if self.pretrain_just_disabled:
self.update_sample_for_preview(force_new=True)
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.model_data_format = "NCHW" if len(devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
self.resolution = resolution = self.options['resolution']
self.face_type = {'h' : FaceType.HALF,
'mf' : FaceType.MID_FULL,
'f' : FaceType.FULL,
'wf' : FaceType.WHOLE_FACE,
'head' : FaceType.HEAD}[ self.options['face_type'] ]
eyes_prio = self.options['eyes_prio']
archi_split = self.options['archi'].split('-')
if len(archi_split) == 2:
archi_type, archi_opts = archi_split
elif len(archi_split) == 1:
archi_type, archi_opts = archi_split[0], None
ae_dims = self.options['ae_dims']
e_dims = self.options['e_dims']
d_dims = self.options['d_dims']
d_mask_dims = self.options['d_mask_dims']
self.pretrain = self.options['pretrain']
if self.pretrain_just_disabled:
self.set_iter(0)
self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
random_warp = False if self.pretrain else self.options['random_warp']
if self.pretrain:
self.options_show_override['gan_power'] = 0.0
self.options_show_override['random_warp'] = False
self.options_show_override['lr_dropout'] = 'n'
self.options_show_override['face_style_power'] = 0.0
self.options_show_override['bg_style_power'] = 0.0
self.options_show_override['uniform_yaw'] = True
masked_training = self.options['masked_training']
import dfl
dfl.load_config()
masked_training = dfl.get_config("masked_training", "1") == "1"
ct_mode = self.options['ct_mode']
if ct_mode == 'none':
ct_mode = None
models_opt_on_gpu = False if len(devices) == 0 else self.options['models_opt_on_gpu']
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
input_ch=3
bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
mask_shape = nn.get4Dshape(resolution,resolution,1)
self.model_filename_list = []
with tf.device ('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder (nn.floatx, bgr_shape)
self.warped_dst = tf.placeholder (nn.floatx, bgr_shape)
self.target_src = tf.placeholder (nn.floatx, bgr_shape)
self.target_dst = tf.placeholder (nn.floatx, bgr_shape)
self.target_srcm_all = tf.placeholder (nn.floatx, mask_shape)
self.target_dstm_all = tf.placeholder (nn.floatx, mask_shape)
# Initializing model classes
model_archi = nn.DeepFakeArchi(resolution, opts=archi_opts)
with tf.device (models_opt_device):
if 'df' in archi_type:
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, name='encoder')
encoder_out_ch = self.encoder.compute_output_channels ( (nn.floatx, bgr_shape))
self.inter = model_archi.Inter (in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims, name='inter')
inter_out_ch = self.inter.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
self.decoder_src = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder_src')
self.decoder_dst = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder_dst')
self.model_filename_list += [ [self.encoder, 'encoder.npy' ],
[self.inter, 'inter.npy' ],
[self.decoder_src, 'decoder_src.npy'],
[self.decoder_dst, 'decoder_dst.npy'] ]
if self.is_training:
if self.options['true_face_power'] != 0:
self.code_discriminator = nn.CodeDiscriminator(ae_dims, code_res=model_archi.Inter.get_code_res()*2, name='dis' )
self.model_filename_list += [ [self.code_discriminator, 'code_discriminator.npy'] ]
elif 'liae' in archi_type:
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, name='encoder')
encoder_out_ch = self.encoder.compute_output_channels ( (nn.floatx, bgr_shape))
self.inter_AB = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, name='inter_AB')
self.inter_B = model_archi.Inter(in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims*2, name='inter_B')
inter_AB_out_ch = self.inter_AB.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
inter_B_out_ch = self.inter_B.compute_output_channels ( (nn.floatx, (None,encoder_out_ch)))
inters_out_ch = inter_AB_out_ch+inter_B_out_ch
self.decoder = model_archi.Decoder(in_ch=inters_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder')
self.model_filename_list += [ [self.encoder, 'encoder.npy'],
[self.inter_AB, 'inter_AB.npy'],
[self.inter_B , 'inter_B.npy'],
[self.decoder , 'decoder.npy'] ]
if self.is_training:
if gan_power != 0:
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
lr=5e-5
lr_dropout = 0.3 if self.options['lr_dropout'] in ['y','cpu'] and not self.pretrain else 1.0
clipnorm = 1.0 if self.options['clipgrad'] else 0.0
if 'df' in archi_type:
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
elif 'liae' in archi_type:
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
self.src_dst_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
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')
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
if self.options['true_face_power'] != 0:
self.D_code_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_code_opt')
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')
self.model_filename_list += [ (self.D_code_opt, 'D_code_opt.npy') ]
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.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:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices) )
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size( gpu_count*bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_G_loss_gvs = []
gpu_D_code_loss_gvs = []
gpu_D_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
gpu_warped_src = self.warped_src [batch_slice,:,:,:]
gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
gpu_target_src = self.target_src [batch_slice,:,:,:]
gpu_target_dst = self.target_dst [batch_slice,:,:,:]
gpu_target_srcm_all = self.target_srcm_all[batch_slice,:,:,:]
gpu_target_dstm_all = self.target_dstm_all[batch_slice,:,:,:]
# process model tensors
if 'df' in archi_type:
gpu_src_code = self.inter(self.encoder(gpu_warped_src))
gpu_dst_code = self.inter(self.encoder(gpu_warped_dst))
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(gpu_src_code)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
elif 'liae' in archi_type:
gpu_src_code = self.encoder (gpu_warped_src)
gpu_src_inter_AB_code = self.inter_AB (gpu_src_code)
gpu_src_code = tf.concat([gpu_src_inter_AB_code,gpu_src_inter_AB_code], nn.conv2d_ch_axis )
gpu_dst_code = self.encoder (gpu_warped_dst)
gpu_dst_inter_B_code = self.inter_B (gpu_dst_code)
gpu_dst_inter_AB_code = self.inter_AB (gpu_dst_code)
gpu_dst_code = tf.concat([gpu_dst_inter_B_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis )
gpu_src_dst_code = tf.concat([gpu_dst_inter_AB_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis )
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_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
# unpack masks from one combined mask
gpu_target_srcm = tf.clip_by_value (gpu_target_srcm_all, 0, 1)
gpu_target_dstm = tf.clip_by_value (gpu_target_dstm_all, 0, 1)
gpu_target_srcm_eyes = tf.clip_by_value (gpu_target_srcm_all-1, 0, 1)
gpu_target_dstm_eyes = tf.clip_by_value (gpu_target_dstm_all-1, 0, 1)
gpu_target_srcm_blur = nn.gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_blur, 0, 0.5) * 2
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_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
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*(1.0 - gpu_target_dstm_style_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*(1.0 - gpu_target_dstm_style_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:
gpu_src_loss = tf.reduce_mean ( 5*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])
gpu_src_loss += tf.reduce_mean ( 5*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
if eyes_prio:
gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_eyes - gpu_pred_src_src*gpu_target_srcm_eyes ), axis=[1,2,3])
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
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_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] )
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])
else:
gpu_dst_loss = tf.reduce_mean ( 5*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])
gpu_dst_loss += tf.reduce_mean ( 5*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/23.2) ), axis=[1])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
if eyes_prio:
gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_eyes - gpu_pred_dst_dst*gpu_target_dstm_eyes ), axis=[1,2,3])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_G_loss = gpu_src_loss + gpu_dst_loss
def DLoss(labels,logits):
return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits), axis=[1,2,3])
if self.options['true_face_power'] != 0:
gpu_src_code_d = self.code_discriminator( gpu_src_code )
gpu_src_code_d_ones = tf.ones_like (gpu_src_code_d)
gpu_src_code_d_zeros = tf.zeros_like(gpu_src_code_d)
gpu_dst_code_d = self.code_discriminator( gpu_dst_code )
gpu_dst_code_d_ones = tf.ones_like(gpu_dst_code_d)
gpu_G_loss += self.options['true_face_power']*DLoss(gpu_src_code_d_ones, gpu_src_code_d)
gpu_D_code_loss = (DLoss(gpu_src_code_d_ones , gpu_dst_code_d) + \
DLoss(gpu_src_code_d_zeros, gpu_src_code_d) ) * 0.5
gpu_D_code_loss_gvs += [ nn.gradients (gpu_D_code_loss, self.code_discriminator.get_weights() ) ]
if gan_power != 0:
gpu_pred_src_src_d, \
gpu_pred_src_src_d2 = 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_zeros = tf.zeros_like(gpu_pred_src_src_d)
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_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 ) ]
# Average losses and gradients, and create optimizer update ops
with tf.device (models_opt_device):
pred_src_src = nn.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = nn.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = nn.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
src_loss = tf.concat(gpu_src_losses, 0)
dst_loss = tf.concat(gpu_dst_losses, 0)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op (nn.average_gv_list (gpu_G_loss_gvs))
if self.options['true_face_power'] != 0:
D_loss_gv_op = self.D_code_opt.get_update_op (nn.average_gv_list(gpu_D_code_loss_gvs))
if gan_power != 0:
src_D_src_dst_loss_gv_op = self.D_src_dst_opt.get_update_op (nn.average_gv_list(gpu_D_src_dst_loss_gvs) )
# Initializing training and view functions
def src_dst_train(warped_src, target_src, target_srcm_all, \
warped_dst, target_dst, target_dstm_all):
s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm_all:target_srcm_all,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm_all:target_dstm_all,
})
return s, d
self.src_dst_train = src_dst_train
if self.options['true_face_power'] != 0:
def D_train(warped_src, warped_dst):
nn.tf_sess.run ([D_loss_gv_op], feed_dict={self.warped_src: warped_src, self.warped_dst: warped_dst})
self.D_train = D_train
if gan_power != 0:
def D_src_dst_train(warped_src, target_src, target_srcm_all, \
warped_dst, target_dst, target_dstm_all):
nn.tf_sess.run ([src_D_src_dst_loss_gv_op], feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm_all:target_srcm_all,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm_all:target_dstm_all})
self.D_src_dst_train = D_src_dst_train
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
feed_dict={self.warped_src:warped_src,
self.warped_dst:warped_dst})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device( f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
if 'df' in archi_type:
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
elif 'liae' in archi_type:
gpu_dst_code = self.encoder (self.warped_dst)
gpu_dst_inter_B_code = self.inter_B (gpu_dst_code)
gpu_dst_inter_AB_code = self.inter_AB (gpu_dst_code)
gpu_dst_code = tf.concat([gpu_dst_inter_B_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis)
gpu_src_dst_code = tf.concat([gpu_dst_inter_AB_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
def AE_merge( warped_dst):
return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
if self.pretrain_just_disabled:
do_init = False
if 'df' in archi_type:
if model == self.inter:
do_init = True
elif 'liae' in archi_type:
if model == self.inter_AB or model == self.inter_B:
do_init = True
else:
do_init = self.is_first_run()
if not do_init:
do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path()
random_ct_samples_path=training_data_dst_path if ct_mode is not None and not self.pretrain else None
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
if ct_mode is not None:
src_generators_count = int(src_generators_count * 1.5)
self.set_training_data_generators ([
SampleGeneratorFace(training_data_src_path, random_ct_samples_path=random_ct_samples_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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},
{'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},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE_EYES, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
],
uniform_yaw_distribution=self.options['uniform_yaw'] or self.pretrain,
generators_count=src_generators_count ),
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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},
{'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},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE_EYES, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
],
uniform_yaw_distribution=self.options['uniform_yaw'] or self.pretrain,
generators_count=dst_generators_count )
])
self.last_src_samples_loss = []
self.last_dst_samples_loss = []
if self.pretrain_just_disabled:
self.update_sample_for_preview(force_new=True)
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.model_data_format = "NHWC"#"NCHW" if len(devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
self.resolution = resolution = self.options['resolution']
self.face_type = {'h' : FaceType.HALF,
'mf' : FaceType.MID_FULL,
'f' : FaceType.FULL,
'wf' : FaceType.WHOLE_FACE,
'head' : FaceType.HEAD}[ self.options['face_type'] ]
models_opt_on_gpu = True#False if len(devices) == 0 else self.options['models_opt_on_gpu']
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
input_ch=3
bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
mask_shape = nn.get4Dshape(resolution,resolution,1)
self.model_filename_list = []
class BaseModel(nn.ModelBase):
def on_build(self, in_ch, base_ch, out_ch=None):
self.convs = [ nn.Conv2D( in_ch, base_ch, kernel_size=7, strides=1, padding='SAME'),
nn.Conv2D( base_ch, base_ch, kernel_size=3, strides=1, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch, base_ch*2, kernel_size=3, strides=2, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*2, base_ch*2, kernel_size=3, strides=1, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*2, base_ch*4, kernel_size=3, strides=2, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*4, base_ch*4, kernel_size=3, strides=1, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*4, base_ch*8, kernel_size=3, strides=2, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*8, base_ch*8, kernel_size=3, strides=1, use_bias=False, padding='SAME')
]
self.frns = [ None,
nn.FRNorm2D(base_ch),
nn.FRNorm2D(base_ch*2),
nn.FRNorm2D(base_ch*2),
nn.FRNorm2D(base_ch*4),
nn.FRNorm2D(base_ch*4),
nn.FRNorm2D(base_ch*8),
nn.FRNorm2D(base_ch*8),
]
self.tlus = [ nn.TLU(base_ch),
nn.TLU(base_ch),
nn.TLU(base_ch*2),
nn.TLU(base_ch*2),
nn.TLU(base_ch*4),
nn.TLU(base_ch*4),
nn.TLU(base_ch*8),
nn.TLU(base_ch*8),
]
if out_ch is not None:
self.out_conv = nn.Conv2D( base_ch*8, out_ch, kernel_size=1, strides=1, use_bias=False, padding='VALID')
else:
self.out_conv = None
def forward(self, inp):
x = inp
for i in range(len(self.convs)):
x = self.convs[i](x)
if self.frns[i] is not None:
x = self.frns[i](x)
x = self.tlus[i](x)
if self.out_conv is not None:
x = self.out_conv(x)
return x
class Regressor(nn.ModelBase):
def on_build(self, lmrks_ch, base_ch, out_ch):
self.convs = [ nn.Conv2D( base_ch*8+lmrks_ch, base_ch*8, kernel_size=3, strides=1, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*8, base_ch*8*4, kernel_size=3, strides=1, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*8, base_ch*4, kernel_size=3, strides=1, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*4, base_ch*4*4, kernel_size=3, strides=1, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*4, base_ch*2, kernel_size=3, strides=1, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*2, base_ch*2*4, kernel_size=3, strides=1, use_bias=False, padding='SAME'),
nn.Conv2D( base_ch*2, base_ch, kernel_size=3, strides=1, use_bias=False, padding='SAME'),
]
self.frns = [ nn.FRNorm2D(base_ch*8),
nn.FRNorm2D(base_ch*8*4),
nn.FRNorm2D(base_ch*4),
nn.FRNorm2D(base_ch*4*4),
nn.FRNorm2D(base_ch*2),
nn.FRNorm2D(base_ch*2*4),
nn.FRNorm2D(base_ch),
]
self.tlus = [ nn.TLU(base_ch*8),
nn.TLU(base_ch*8*4),
nn.TLU(base_ch*4),
nn.TLU(base_ch*4*4),
nn.TLU(base_ch*2),
nn.TLU(base_ch*2*4),
nn.TLU(base_ch),
]
self.use_upscale = [ False,
True,
False,
True,
False,
True,
False,
]
self.out_conv = nn.Conv2D( base_ch, out_ch, kernel_size=3, strides=1, padding='SAME')
def forward(self, inp):
x = inp
for i in range(len(self.convs)):
x = self.convs[i](x)
x = self.frns[i](x)
x = self.tlus[i](x)
if self.use_upscale[i]:
x = nn.depth_to_space(x, 2)
x = self.out_conv(x)
x = tf.nn.sigmoid(x)
return x
def get_coord(x, other_axis, axis_size):
# get "x-y" coordinates:
g_c_prob = tf.reduce_mean(x, axis=other_axis) # B,W,NMAP
g_c_prob = tf.nn.softmax(g_c_prob, axis=1) # B,W,NMAP
coord_pt = tf.to_float(tf.linspace(-1.0, 1.0, axis_size)) # W
coord_pt = tf.reshape(coord_pt, [1, axis_size, 1])
g_c = tf.reduce_sum(g_c_prob * coord_pt, axis=1)
return g_c, g_c_prob
def get_gaussian_maps(mu_x, mu_y, width, height, inv_std=10.0, mode='rot'):
"""
Generates [B,SHAPE_H,SHAPE_W,NMAPS] tensor of 2D gaussians,
given the gaussian centers: MU [B, NMAPS, 2] tensor.
STD: is the fixed standard dev.
"""
y = tf.to_float(tf.linspace(-1.0, 1.0, width))
x = tf.to_float(tf.linspace(-1.0, 1.0, height))
if mode in ['rot', 'flat']:
mu_y, mu_x = mu_y[...,None,None], mu_x[...,None,None]
y = tf.reshape(y, [1, 1, width, 1])
x = tf.reshape(x, [1, 1, 1, height])
g_y = tf.square(y - mu_y)
g_x = tf.square(x - mu_x)
dist = (g_y + g_x) * inv_std**2
if mode == 'rot':
g_yx = tf.exp(-dist)
else:
g_yx = tf.exp(-tf.pow(dist + 1e-5, 0.25))
elif mode == 'ankush':
y = tf.reshape(y, [1, 1, width])
x = tf.reshape(x, [1, 1, height])
g_y = tf.exp(-tf.sqrt(1e-4 + tf.abs((mu_y[...,None] - y) * inv_std)))
g_x = tf.exp(-tf.sqrt(1e-4 + tf.abs((mu_x[...,None] - x) * inv_std)))
g_y = tf.expand_dims(g_y, axis=3)
g_x = tf.expand_dims(g_x, axis=2)
g_yx = tf.matmul(g_y, g_x) # [B, NMAPS, H, W]
else:
raise ValueError('Unknown mode: ' + str(mode))
g_yx = tf.transpose(g_yx, perm=[0, 2, 3, 1])
return g_yx
with tf.device ('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder (nn.floatx, bgr_shape)
self.target_src = tf.placeholder (nn.floatx, bgr_shape)
# Initializing model classes
#model_archi = nn.DeepFakeArchi(resolution, mod='uhd' if 'uhd' in archi else None)
self.landmarks_count = 512
self.n_ch = 32
with tf.device (models_opt_device):
self.detector = BaseModel(3, self.n_ch, out_ch=self.landmarks_count, name='Detector')
self.extractor = BaseModel(3, self.n_ch, name='Extractor')
self.regressor = Regressor(self.landmarks_count, self.n_ch, 3, name='Regressor')
self.model_filename_list += [ [self.detector, 'detector.npy'],
[self.extractor, 'extractor.npy'],
[self.regressor, 'regressor.npy'] ]
if self.is_training:
# Initialize optimizers
lr=5e-5
lr_dropout = 0.3#0.3 if self.options['lr_dropout'] and not self.pretrain else 1.0
clipnorm = 0.0#1.0 if self.options['clipgrad'] else 0.0
self.model_opt = nn.RMSprop(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='model_opt')
self.model_filename_list += [ (self.model_opt, 'model_opt.npy') ]
self.model_trainable_weights = self.detector.get_weights() + self.extractor.get_weights() + self.regressor.get_weights()
self.model_opt.initialize_variables (self.model_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu)
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices) )
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
self.set_batch_size( gpu_count*bs_per_gpu)
# Compute losses per GPU
gpu_src_rec_list = []
gauss_mu_list = []
gpu_src_losses = []
gpu_G_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
gpu_warped_src = self.warped_src [batch_slice,:,:,:]
gpu_target_src = self.target_src [batch_slice,:,:,:]
# process model tensors
gpu_src_feat = self.extractor(gpu_warped_src)
gpu_src_heatmaps = self.detector(gpu_target_src)
gauss_y, gauss_y_prob = get_coord(gpu_src_heatmaps, 2, gpu_src_heatmaps.shape.as_list()[1] )
gauss_x, gauss_x_prob = get_coord(gpu_src_heatmaps, 1, gpu_src_heatmaps.shape.as_list()[2] )
gauss_mu = tf.stack ( (gauss_x, gauss_y), -1)
dist_loss = []
for i in range(self.landmarks_count):
t = tf.concat( (gauss_mu[:,0:i], gauss_mu[:,i+1:] ), axis=1 )
diff = t - gauss_mu[:,i:i+1]
dist = tf.sqrt( diff[...,0]**2+diff[...,1]**2 )
dist_loss += [ tf.reduce_mean(2.0 - dist,-1) ]
dist_loss = sum(dist_loss) / self.landmarks_count
#import code
#code.interact(local=dict(globals(), **locals()))
gauss_xy = get_gaussian_maps ( gauss_x, gauss_y, 16, 16 )
gpu_src_rec = self.regressor( tf.concat ( (gpu_src_feat, gauss_xy), -1) )
gpu_src_rec_list.append(gpu_src_rec)
gauss_mu_list.append(gauss_mu)
gpu_src_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_src, gpu_src_rec, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += tf.reduce_mean ( 10*tf.square (gpu_target_src - gpu_src_rec), axis=[1,2,3])
gpu_src_loss += dist_loss
gpu_src_losses += [gpu_src_loss]
gpu_G_loss_gvs += [ nn.gradients ( gpu_src_loss, self.model_trainable_weights ) ]
# Average losses and gradients, and create optimizer update ops
with tf.device (models_opt_device):
src_rec = nn.concat(gpu_src_rec_list, 0)
gauss_mu = nn.concat(gauss_mu_list, 0)
src_loss = tf.concat(gpu_src_losses, 0)
loss_gv_op = self.model_opt.get_update_op (nn.average_gv_list (gpu_G_loss_gvs))
# Initializing training and view functions
def ae_train(warped_src, target_src):
s, _ = nn.tf_sess.run ( [ src_loss, loss_gv_op], feed_dict={self.warped_src:warped_src, self.target_src:target_src})
return s
self.ae_train = ae_train
def AE_view(warped_src, target_src):
return nn.tf_sess.run ( [src_rec, gauss_mu], feed_dict={self.warped_src:warped_src, self.target_src:target_src})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device( f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
if 'df' in archi:
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
def AE_merge( warped_dst):
return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
do_init = self.is_first_run()
if not do_init:
do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
training_data_src_path = self.training_data_src_path
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
self.set_training_data_generators ([
SampleGeneratorFace(training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size()*2,
sample_process_options=SampleProcessor.Options(random_flip=False),
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
{'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},
],
generators_count=src_generators_count ),
])