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added 'sort by vggface': sorting by face similarity using VGGFace model.

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Requires 4GB+ VRAM and internet connection for the first run.
This commit is contained in:
Colombo 2019-10-23 15:06:39 +04:00
parent 0d3b25812d
commit 734d97d729
8 changed files with 186 additions and 43 deletions
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29
nnlib/FUNIT.py
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@ -162,10 +162,6 @@ class FUNIT(object):
for w in weights_list:
K.set_value( w, K.get_value(initer(K.int_shape(w))) )
#if not self.is_first_run():
# self.load_weights_safe(self.get_model_filename_list())
if load_weights_locally:
pass
@ -188,9 +184,6 @@ class FUNIT(object):
[self.D_opt, 'D_opt.h5'],
]
#def save_weights(self):
# self.model.save_weights (str(self.weights_path))
def train(self, xa,la,xb,lb):
D_loss, = self.D_train ([xa,la,xb,lb])
G_loss, = self.G_train ([xa,la,xb,lb])
@ -209,17 +202,17 @@ class FUNIT(object):
def ResBlock(dim):
def func(input):
x = input
x = Conv2D(dim, 3, strides=1, padding='valid')(ZeroPadding2D(1)(x))
x = Conv2D(dim, 3, strides=1, padding='same')(x)
x = InstanceNormalization()(x)
x = ReLU()(x)
x = Conv2D(dim, 3, strides=1, padding='valid')(ZeroPadding2D(1)(x))
x = Conv2D(dim, 3, strides=1, padding='same')(x)
x = InstanceNormalization()(x)
return Add()([x,input])
return func
def func(x):
x = Conv2D (nf, kernel_size=7, strides=1, padding='valid')(ZeroPadding2D(3)(x))
x = Conv2D (nf, kernel_size=7, strides=1, padding='same')(x)
x = InstanceNormalization()(x)
x = ReLU()(x)
for i in range(downs):
@ -237,11 +230,11 @@ class FUNIT(object):
exec (nnlib.import_all(), locals(), globals())
def func(x):
x = Conv2D (nf, kernel_size=7, strides=1, padding='valid', activation='relu')(ZeroPadding2D(3)(x))
x = Conv2D (nf, kernel_size=7, strides=1, padding='same', activation='relu')(x)
for i in range(downs):
x = Conv2D (nf * min ( 4, 2**(i+1) ), kernel_size=4, strides=2, padding='valid', activation='relu')(ZeroPadding2D(1)(x))
x = GlobalAveragePooling2D()(x)
x = Dense(nf)(x)
x = Dense(latent_dim)(x)
return x
return func
@ -250,16 +243,14 @@ class FUNIT(object):
def DecoderFlow(ups, n_res_blks=2, mlp_blks=2, subpixel_decoder=False ):
exec (nnlib.import_all(), locals(), globals())
def ResBlock(dim):
def func(input):
inp, mlp = input
x = inp
x = Conv2D(dim, 3, strides=1, padding='valid')(ZeroPadding2D(1)(x))
x = Conv2D(dim, 3, strides=1, padding='same')(x)
x = FUNITAdain(kernel_initializer='he_normal')([x,mlp])
x = ReLU()(x)
x = Conv2D(dim, 3, strides=1, padding='valid')(ZeroPadding2D(1)(x))
x = Conv2D(dim, 3, strides=1, padding='same')(x)
x = FUNITAdain(kernel_initializer='he_normal')([x,mlp])
return Add()([x,inp])
return func
@ -280,16 +271,16 @@ class FUNIT(object):
for i in range(ups):
if subpixel_decoder:
x = Conv2D (4* (nf // 2**(i+1)), kernel_size=3, strides=1, padding='valid')(ZeroPadding2D(1)(x))
x = Conv2D (4* (nf // 2**(i+1)), kernel_size=3, strides=1, padding='same')(x)
x = SubpixelUpscaler()(x)
else:
x = UpSampling2D()(x)
x = Conv2D (nf // 2**(i+1), kernel_size=5, strides=1, padding='valid')(ZeroPadding2D(2)(x))
x = Conv2D (nf // 2**(i+1), kernel_size=5, strides=1, padding='same')(x)
x = InstanceNormalization()(x)
x = ReLU()(x)
rgb = Conv2D (3, kernel_size=7, strides=1, padding='valid', activation='tanh')(ZeroPadding2D(3)(x))
rgb = Conv2D (3, kernel_size=7, strides=1, padding='same', activation='tanh')(x)
return rgb
return func