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update == 04.20.2019 == (#242)

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* superb improved fanseg

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* added FANseg extractor for src and dst faces to use it in training

* -

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* update to 'partial' func

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* trained FANSeg_256_full_face.h5,
new experimental models: AVATAR, RecycleGAN

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* fix for TCC mode cards(tesla), was conflict with plaidML initialization.

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* update manuals

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iperov 2019-04-20 08:23:08 +04:00 • committed by GitHub
parent 7be2fd67f5
commit 046649e6be
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32 changed files with 1152 additions and 329 deletions
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71
models/Model_SAE/Model.py
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@ -1,3 +1,4 @@
from functools import partial
import numpy as np
from nnlib import nnlib
@ -385,20 +386,20 @@ class SAEModel(ModelBase):
#override
def onGetPreview(self, sample):
test_A = sample[0][1][0:4] #first 4 samples
test_A_m = sample[0][2][0:4] #first 4 samples
test_B = sample[1][1][0:4]
test_B_m = sample[1][2][0:4]
test_S = sample[0][1][0:4] #first 4 samples
test_S_m = sample[0][2][0:4] #first 4 samples
test_D = sample[1][1][0:4]
test_D_m = sample[1][2][0:4]
if self.options['learn_mask']:
S, D, SS, DD, DDM, SD, SDM = [ np.clip(x, 0.0, 1.0) for x in ([test_A,test_B] + self.AE_view ([test_A, test_B]) ) ]
S, D, SS, DD, DDM, SD, SDM = [ np.clip(x, 0.0, 1.0) for x in ([test_S,test_D] + self.AE_view ([test_S, test_D]) ) ]
DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
else:
S, D, SS, DD, SD, = [ np.clip(x, 0.0, 1.0) for x in ([test_A,test_B] + self.AE_view ([test_A, test_B]) ) ]
S, D, SS, DD, SD, = [ np.clip(x, 0.0, 1.0) for x in ([test_S,test_D] + self.AE_view ([test_S, test_D]) ) ]
result = []
st = []
for i in range(0, len(test_A)):
for i in range(0, len(test_S)):
ar = S[i], SS[i], D[i], DD[i], SD[i]
st.append ( np.concatenate ( ar, axis=1) )
@ -406,12 +407,12 @@ class SAEModel(ModelBase):
if self.options['learn_mask']:
st_m = []
for i in range(0, len(test_A)):
ar = S[i], SS[i], D[i], DD[i]*DDM[i], SD[i]*(DDM[i]*SDM[i])
for i in range(0, len(test_S)):
ar = S[i]*test_S_m[i], SS[i], D[i]*test_D_m[i], DD[i]*DDM[i], SD[i]*(DDM[i]*SDM[i])
st_m.append ( np.concatenate ( ar, axis=1) )
result += [ ('SAE masked', np.concatenate (st_m, axis=0 )), ]
return result
def predictor_func (self, face):
@ -485,57 +486,29 @@ class SAEModel(ModelBase):
return x
SAEModel.ResidualBlock = ResidualBlock
def ResidualBlock_pre (**base_kwargs):
def func(*args, **kwargs):
kwargs.update(base_kwargs)
return ResidualBlock(*args, **kwargs)
return func
SAEModel.ResidualBlock_pre = ResidualBlock_pre
def downscale (dim, padding='zero', norm='', act='', **kwargs):
def func(x):
return Norm(norm)( Act(act) (Conv2D(dim, kernel_size=5, strides=2, padding=padding)(x)) )
return func
SAEModel.downscale = downscale
def downscale_pre (**base_kwargs):
def func(*args, **kwargs):
kwargs.update(base_kwargs)
return downscale(*args, **kwargs)
return func
SAEModel.downscale_pre = downscale_pre
def upscale (dim, padding='zero', norm='', act='', **kwargs):
def func(x):
return SubpixelUpscaler()(Norm(norm)(Act(act)(Conv2D(dim * 4, kernel_size=3, strides=1, padding=padding)(x))))
return func
SAEModel.upscale = upscale
def upscale_pre (**base_kwargs):
def func(*args, **kwargs):
kwargs.update(base_kwargs)
return upscale(*args, **kwargs)
return func
SAEModel.upscale_pre = upscale_pre
def to_bgr (output_nc, padding='zero', **kwargs):
def func(x):
return Conv2D(output_nc, kernel_size=5, padding=padding, activation='sigmoid')(x)
return func
SAEModel.to_bgr = to_bgr
def to_bgr_pre (**base_kwargs):
def func(*args, **kwargs):
kwargs.update(base_kwargs)
return to_bgr(*args, **kwargs)
return func
SAEModel.to_bgr_pre = to_bgr_pre
@staticmethod
def LIAEEncFlow(resolution, ch_dims, **kwargs):
exec (nnlib.import_all(), locals(), globals())
upscale = SAEModel.upscale_pre(**kwargs)
downscale = SAEModel.downscale_pre(**kwargs)
upscale = partial(SAEModel.upscale, **kwargs)
downscale = partial(SAEModel.downscale, **kwargs)
def func(input):
dims = K.int_shape(input)[-1]*ch_dims
@ -553,7 +526,7 @@ class SAEModel(ModelBase):
@staticmethod
def LIAEInterFlow(resolution, ae_dims=256, **kwargs):
exec (nnlib.import_all(), locals(), globals())
upscale = SAEModel.upscale_pre(**kwargs)
upscale = partial(SAEModel.upscale, **kwargs)
lowest_dense_res=resolution // 16
def func(input):
@ -568,10 +541,10 @@ class SAEModel(ModelBase):
@staticmethod
def LIAEDecFlow(output_nc,ch_dims, multiscale_count=1, add_residual_blocks=False, padding='zero', norm='', **kwargs):
exec (nnlib.import_all(), locals(), globals())
upscale = SAEModel.upscale_pre(**kwargs)
to_bgr = SAEModel.to_bgr_pre(**kwargs)
upscale = partial(SAEModel.upscale, **kwargs)
to_bgr = partial(SAEModel.to_bgr, **kwargs)
dims = output_nc * ch_dims
ResidualBlock = SAEModel.ResidualBlock_pre(**kwargs)
ResidualBlock = partial(SAEModel.ResidualBlock, **kwargs)
def func(input):
x = input[0]
@ -609,8 +582,8 @@ class SAEModel(ModelBase):
@staticmethod
def DFEncFlow(resolution, ae_dims, ch_dims, padding='zero', **kwargs):
exec (nnlib.import_all(), locals(), globals())
upscale = SAEModel.upscale_pre(padding=padding)
downscale = SAEModel.downscale_pre(padding=padding)
upscale = partial(SAEModel.upscale, padding=padding)
downscale = partial(SAEModel.downscale, padding=padding)
lowest_dense_res = resolution // 16
def func(input):
@ -634,10 +607,10 @@ class SAEModel(ModelBase):
@staticmethod
def DFDecFlow(output_nc, ch_dims, multiscale_count=1, add_residual_blocks=False, padding='zero', **kwargs):
exec (nnlib.import_all(), locals(), globals())
upscale = SAEModel.upscale_pre(padding=padding)
to_bgr = SAEModel.to_bgr_pre(padding=padding)
upscale = partial(SAEModel.upscale, padding=padding)
to_bgr = partial(SAEModel.to_bgr, padding=padding)
dims = output_nc * ch_dims
ResidualBlock = SAEModel.ResidualBlock_pre(padding=padding)
ResidualBlock = partial(SAEModel.ResidualBlock, padding=padding)
def func(input):
x = input[0]