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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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64
nnlib/VGGFace.py Normal file
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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