github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-07-07 05:22:06 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

upd loss

Browse source
This commit is contained in:
Colombo 2019-10-06 01:00:34 +04:00
parent d781af3d1f
commit e4a360e5ff
1 changed files with 36 additions and 7 deletions
Download patch file Download diff file Expand all files Collapse all files

43
models/Model_SAEv2/Model.py
View file

@ -11,7 +11,7 @@ from samplelib import *
#SAE - Styled AutoEncoder
class SAEModel(ModelBase):
class SAEv2Model(ModelBase):
#override
def onInitializeOptions(self, is_first_run, ask_override):
@ -141,9 +141,9 @@ class SAEModel(ModelBase):
return SubpixelDownscaler()(LeakyReLU(0.1)(Conv2D(dim // 4, kernel_size=kernel_size, strides=1, dilation_rate=dilation_rate, padding='same')(x)))
return func
def upscale (dim):
def upscale (dim, size=(2,2)):
def func(x):
return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, kernel_size=3, strides=1, padding='same')(x)))
return SubpixelUpscaler(size=size)(LeakyReLU(0.1)(Conv2D(dim * np.prod(size) , kernel_size=3, strides=1, padding='same')(x)))
return func
def enc_flow(e_dims, ae_dims, lowest_dense_res):
@ -188,8 +188,36 @@ class SAEModel(ModelBase):
x = LeakyReLU(0.2)(x)
return x
return func
"""
def func(x):
x0 = upscale(dims, size=(8,8) )(x)
x = upscale(dims*4)(x)
if add_residual_blocks:
x = ResidualBlock(dims*4)(x)
x1 = upscale(dims, size=(4,4) )(x)
x = upscale(dims*2)(x)
if add_residual_blocks:
x = ResidualBlock(dims*2)(x)
x2 = upscale(dims, size=(2,2) )(x)
x = upscale(dims)(x)
if add_residual_blocks:
x = ResidualBlock(dims)(x)
x = Add()([x0,x1,x2,x])
return Conv2D(output_nc, kernel_size=5, padding='same', activation='sigmoid')(x)
"""
def func(x):
x = upscale(dims*8)(x)
if add_residual_blocks:
@ -206,6 +234,7 @@ class SAEModel(ModelBase):
x = ResidualBlock(dims*2)(x)
return Conv2D(output_nc, kernel_size=5, padding='same', activation='sigmoid')(x)
return func
self.encoder = modelify(enc_flow(e_dims, ae_dims, lowest_dense_res)) ( Input(bgr_shape) )
@ -470,7 +499,7 @@ class SAEModel(ModelBase):
if self.is_training_mode:
self.src_dst_opt = RMSprop(lr=5e-5, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
self.src_dst_mask_opt = RMSprop(lr=5e-5, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
self.D_opt = RMSprop(lr=1e-5, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
self.D_opt = RMSprop(lr=5e-5, clipnorm=1.0 if self.options['clipgrad'] else 0.0, tf_cpu_mode=self.options['optimizer_mode']-1)
if not self.options['pixel_loss']:
src_loss = K.mean ( 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_opt, pred_src_src_masked_opt) )
@ -503,7 +532,7 @@ class SAEModel(ModelBase):
src_code_d_zeros = K.zeros_like(src_code_d)
dst_code_d = self.dis( self.model.dst_code )
dst_code_d_ones = K.ones_like(dst_code_d)
opt_D_loss = [ 0.2*DLoss(src_code_d_ones, src_code_d) ]
opt_D_loss = [ DLoss(src_code_d_ones, src_code_d) ]
loss_D = (DLoss(dst_code_d_ones , dst_code_d) + \
DLoss(src_code_d_zeros, src_code_d) ) * 0.5
@ -655,4 +684,4 @@ class SAEModel(ModelBase):
clip_hborder_mask_per=0.0625 if (face_type == FaceType.FULL) else 0,
)
Model = SAEModel
Model = SAEv2Model