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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
commit 734d97d729
8 changed files with 186 additions and 43 deletions
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29
nnlib/FUNIT.py
View file

@ -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

64
nnlib/VGGFace.py Normal file
View file

@ -0,0 +1,64 @@
from nnlib import nnlib
def VGGFace():
exec(nnlib.import_all(), locals(), globals())
img_input = Input(shape=(224,224,3) )
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_1')(
img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_1')(
x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_2')(
x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_1')(
x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_2')(
x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_3')(
x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_1')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_2')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_3')(
x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_1')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_2')(
x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_3')(
x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool5')(x)
# Classification block
x = Flatten(name='flatten')(x)
x = Dense(4096, name='fc6')(x)
x = Activation('relu', name='fc6/relu')(x)
x = Dense(4096, name='fc7')(x)
x = Activation('relu', name='fc7/relu')(x)
x = Dense(2622, name='fc8')(x)
x = Activation('softmax', name='fc8/softmax')(x)
model = Model(img_input, x, name='vggface_vgg16')
weights_path = keras.utils.data_utils.get_file('rcmalli_vggface_tf_vgg16.h5',
'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_vgg16.h5')
model.load_weights(weights_path, by_name=True)
return model

3
nnlib/__init__.py
View file

@ -1,3 +1,4 @@
from .nnlib import nnlib
from .FUNIT import FUNIT
from .TernausNet import TernausNet
from .TernausNet import TernausNet
from .VGGFace import VGGFace

1
nnlib/nnlib.py
View file

@ -63,6 +63,7 @@ UpSampling2D = KL.UpSampling2D
BatchNormalization = KL.BatchNormalization
PixelNormalization = nnlib.PixelNormalization
Activation = KL.Activation
LeakyReLU = KL.LeakyReLU
ELU = KL.ELU
ReLU = KL.ReLU