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iperov committed Oct 6, 2019
1 parent 4c2cb44 commit cbc18b2d418ded74ca14f02ff486890dc9524331
Showing with 10 additions and 21 deletions.
  1. +10 −21 facelib/PoseEstimator.py
@@ -43,7 +43,7 @@ def ResamplerFunc(input):
mean_t, logvar_t = input
return mean_t + K.exp(0.5*logvar_t)*K.random_normal(K.shape(mean_t))

self.BVAEResampler = Lambda ( lambda x: x[0] + K.exp(0.5*x[1])*K.random_normal(K.shape(x[0])),
self.BVAEResampler = Lambda ( lambda x: x[0] + K.random_normal(K.shape(x[0])) * K.sqrt(K.exp(0.5*x[1])),
output_shape=K.int_shape(self.encoder.outputs[0])[1:] )

inp_t = Input (self.input_bgr_shape)
@@ -99,24 +99,21 @@ def gather_Conv2D_layers(models_list):
pyr_loss += [ a*K.mean( K.square ( inp_pyrs_t[i] - pyrs_t[i]) ) ]

def BVAELoss(beta=4):
#keep in mind loss per sample, not per minibatch
def func(input):
mean_t, logvar_t = input
return beta * K.mean ( K.sum( -0.5*(1 + logvar_t - K.exp(logvar_t) - K.square(mean_t)), axis=1 ), axis=0, keepdims=True )
return beta * K.mean ( K.sum( 0.5*(K.exp(logvar_t)+ K.square(mean_t)-logvar_t-1), axis=1) )
return func

BVAE_loss = BVAELoss(4)([mean_t, logvar_t])#beta * K.mean ( K.sum( -0.5*(1 + logvar_t - K.exp(logvar_t) - K.square(mean_t)), axis=1 ), axis=0, keepdims=True )


bgr_loss = K.mean(K.square(inp_real_t-bgr_t), axis=0, keepdims=True)

#train_loss = BVAE_loss + bgr_loss
BVAE_loss = BVAELoss()([mean_t, logvar_t])

bgr_loss = K.mean(K.sum(K.abs(inp_real_t-bgr_t), axis=[1,2,3]))

G_loss = BVAE_loss+bgr_loss
pyr_loss = sum(pyr_loss)


self.train = K.function ([inp_t, inp_real_t],
[ K.mean (BVAE_loss)+K.mean(bgr_loss) ], Adam(lr=0.0005, beta_1=0.9, beta_2=0.999).get_updates( [BVAE_loss, bgr_loss], self.encoder.trainable_weights+self.decoder.trainable_weights ) )
[ G_loss ], Adam(lr=0.0005, beta_1=0.9, beta_2=0.999).get_updates( G_loss, self.encoder.trainable_weights+self.decoder.trainable_weights ) )

self.train_l = K.function ([inp_t] + inp_pyrs_t,
[pyr_loss], Adam(lr=0.0001).get_updates( pyr_loss, self.model_l.trainable_weights) )
@@ -140,7 +137,6 @@ def save_weights(self):
Model(inp_t, self.model_l(self.BVAEResampler(self.encoder(inp_t))) ).save_weights (str(self.model_weights_path))

def train_on_batch(self, warps, imgs, pyr_tanh, skip_bgr_train=False):

if not skip_bgr_train:
bgr_loss, = self.train( [warps, imgs] )
pyr_loss = 0
@@ -198,12 +194,9 @@ def BuildModels ( resolution, class_nums, ae_dims=128):
def EncFlow(ae_dims):
exec( nnlib.import_all(), locals(), globals() )

XConv2D = partial(Conv2D, padding='zero')


def downscale (dim, **kwargs):
def func(x):
return ReLU() ( ( XConv2D(dim, kernel_size=4, strides=2)(x)) )
return ReLU() ( Conv2D(dim, kernel_size=5, strides=2, padding='same')(x))
return func


@@ -236,16 +229,14 @@ def func(input):
def DecFlow(resolution, ae_dims):
exec( nnlib.import_all(), locals(), globals() )

XConv2D = partial(Conv2D, padding='zero')

def upscale (dim, strides=2, **kwargs):
def func(x):
return ReLU()( ( Conv2DTranspose(dim, kernel_size=4, strides=strides, padding='same')(x)) )
return ReLU()( ( Conv2DTranspose(dim, kernel_size=3, strides=strides, padding='same')(x)) )
return func

def to_bgr (output_nc, **kwargs):
def func(x):
return XConv2D(output_nc, kernel_size=5, activation='sigmoid')(x)
return Conv2D(output_nc, kernel_size=5, padding='same', activation='sigmoid')(x)
return func

upscale = partial(upscale)
@@ -278,8 +269,6 @@ def func(input):
def LatentFlow(class_nums):
exec( nnlib.import_all(), locals(), globals() )

XConv2D = partial(Conv2D, padding='zero')

def func(latent):
x = latent

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