def build_model():
home = functions.get_deepface_home()
import dlib #this requirement is not a must that's why imported here
#check required file exists in the home/.deepface/weights folder
if os.path.isfile(
home +
'/.deepface/weights/shape_predictor_5_face_landmarks.dat') != True:
print(
"shape_predictor_5_face_landmarks.dat.bz2 is going to be downloaded"
)
url = "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2"
output = home + '/.deepface/weights/' + url.split("/")[-1]
gdown.download(url, output, quiet=False)
zipfile = bz2.BZ2File(output)
data = zipfile.read()
newfilepath = output[:-4] #discard .bz2 extension
open(newfilepath, 'wb').write(data)
face_detector = dlib.get_frontal_face_detector()
sp = dlib.shape_predictor(
home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat")
detector = {}
detector["face_detector"] = face_detector
detector["sp"] = sp
return detector
def loadModel(
url='https://github.com/serengil/deepface_models/releases/download/v1.0/vgg_face_weights.h5'
):
model = baseModel()
#-----------------------------------
home = functions.get_deepface_home()
output = home + '/.deepface/weights/vgg_face_weights.h5'
if os.path.isfile(output) != True:
print("vgg_face_weights.h5 will be downloaded...")
gdown.download(url, output, quiet=False)
#-----------------------------------
model.load_weights(output)
#-----------------------------------
#TO-DO: why?
vgg_face_descriptor = Model(inputs=model.layers[0].input,
outputs=model.layers[-2].output)
return vgg_face_descriptor
def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/age_model_weights.h5'):
model = VGGFace.baseModel()
#--------------------------
classes = 101
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
age_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = functions.get_deepface_home()
if os.path.isfile(home+'/.deepface/weights/age_model_weights.h5') != True:
print("age_model_weights.h5 will be downloaded...")
output = home+'/.deepface/weights/age_model_weights.h5'
gdown.download(url, output, quiet=False)
age_model.load_weights(home+'/.deepface/weights/age_model_weights.h5')
return age_model
def __init__(self):
#this is not a must dependency
import dlib #19.20.0
self.layers = [DlibMetaData()]
#---------------------
home = functions.get_deepface_home()
weight_file = home+'/.deepface/weights/dlib_face_recognition_resnet_model_v1.dat'
#---------------------
#download pre-trained model if it does not exist
if os.path.isfile(weight_file) != True:
print("dlib_face_recognition_resnet_model_v1.dat is going to be downloaded")
url = "http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2"
output = home+'/.deepface/weights/'+url.split("/")[-1]
gdown.download(url, output, quiet=False)
zipfile = bz2.BZ2File(output)
data = zipfile.read()
newfilepath = output[:-4] #discard .bz2 extension
open(newfilepath, 'wb').write(data)
#---------------------
model = dlib.face_recognition_model_v1(weight_file)
self.__model = model
#---------------------
return None #classes must return None
def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5'):
num_classes = 7
model = Sequential()
#1st convolution layer
model.add(Conv2D(64, (5, 5), activation='relu', input_shape=(48,48,1)))
model.add(MaxPooling2D(pool_size=(5,5), strides=(2, 2)))
#2nd convolution layer
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))
#3rd convolution layer
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))
model.add(Flatten())
#fully connected neural networks
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
#----------------------------
home = functions.get_deepface_home()
if os.path.isfile(home+'/.deepface/weights/facial_expression_model_weights.h5') != True:
print("facial_expression_model_weights.h5 will be downloaded...")
output = home+'/.deepface/weights/facial_expression_model_weights.h5'
gdown.download(url, output, quiet=False)
"""
#google drive source downloads zip
output = home+'/.deepface/weights/facial_expression_model_weights.zip'
gdown.download(url, output, quiet=False)
#unzip facial_expression_model_weights.zip
with zipfile.ZipFile(output, 'r') as zip_ref:
zip_ref.extractall(home+'/.deepface/weights/')
"""
model.load_weights(home+'/.deepface/weights/facial_expression_model_weights.h5')
return model
def loadModel(
url='https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip'
):
base_model = Sequential()
base_model.add(
Convolution2D(32, (11, 11),
activation='relu',
name='C1',
input_shape=(152, 152, 3)))
base_model.add(
MaxPooling2D(pool_size=3, strides=2, padding='same', name='M2'))
base_model.add(Convolution2D(16, (9, 9), activation='relu', name='C3'))
base_model.add(LocallyConnected2D(16, (9, 9), activation='relu',
name='L4'))
base_model.add(
LocallyConnected2D(16, (7, 7), strides=2, activation='relu',
name='L5'))
base_model.add(LocallyConnected2D(16, (5, 5), activation='relu',
name='L6'))
base_model.add(Flatten(name='F0'))
base_model.add(Dense(4096, activation='relu', name='F7'))
base_model.add(Dropout(rate=0.5, name='D0'))
base_model.add(Dense(8631, activation='softmax', name='F8'))
#---------------------------------
home = functions.get_deepface_home()
if os.path.isfile(
home + '/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5'
) != True:
print("VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded...")
output = home + '/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5.zip'
gdown.download(url, output, quiet=False)
#unzip VGGFace2_DeepFace_weights_val-0.9034.h5.zip
with zipfile.ZipFile(output, 'r') as zip_ref:
zip_ref.extractall(home + '/.deepface/weights/')
base_model.load_weights(
home + '/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5')
#drop F8 and D0. F7 is the representation layer.
deepface_model = Model(inputs=base_model.layers[0].input,
outputs=base_model.layers[-3].output)
return deepface_model
def loadModel(
url='https://github.com/serengil/deepface_models/releases/download/v1.0/deepid_keras_weights.h5'
):
myInput = Input(shape=(55, 47, 3))
x = Conv2D(20, (4, 4),
name='Conv1',
activation='relu',
input_shape=(55, 47, 3))(myInput)
x = MaxPooling2D(pool_size=2, strides=2, name='Pool1')(x)
x = Dropout(rate=0.99, name='D1')(x)
x = Conv2D(40, (3, 3), name='Conv2', activation='relu')(x)
x = MaxPooling2D(pool_size=2, strides=2, name='Pool2')(x)
x = Dropout(rate=0.99, name='D2')(x)
x = Conv2D(60, (3, 3), name='Conv3', activation='relu')(x)
x = MaxPooling2D(pool_size=2, strides=2, name='Pool3')(x)
x = Dropout(rate=0.99, name='D3')(x)
x1 = Flatten()(x)
fc11 = Dense(160, name='fc11')(x1)
x2 = Conv2D(80, (2, 2), name='Conv4', activation='relu')(x)
x2 = Flatten()(x2)
fc12 = Dense(160, name='fc12')(x2)
y = Add()([fc11, fc12])
y = Activation('relu', name='deepid')(y)
model = Model(inputs=[myInput], outputs=y)
#---------------------------------
home = functions.get_deepface_home()
if os.path.isfile(home +
'/.deepface/weights/deepid_keras_weights.h5') != True:
print("deepid_keras_weights.h5 will be downloaded...")
output = home + '/.deepface/weights/deepid_keras_weights.h5'
gdown.download(url, output, quiet=False)
model.load_weights(home + '/.deepface/weights/deepid_keras_weights.h5')
return model
def load_model(
url="https://github.com/opencv/opencv_zoo/raw/master/models/face_recognition_sface/face_recognition_sface_2021dec.onnx"
):
home = functions.get_deepface_home()
file_name = home + '/.deepface/weights/face_recognition_sface_2021dec.onnx'
if not os.path.isfile(file_name):
print("sface weights will be downloaded...")
gdown.download(url, file_name, quiet=False)
model = SFaceModel(model_path=file_name)
return model
def loadModel(
url='https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5'
):
model = VGGFace.baseModel()
#--------------------------
classes = 6
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(
model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
race_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = functions.get_deepface_home()
if os.path.isfile(home +
'/.deepface/weights/race_model_single_batch.h5') != True:
print("race_model_single_batch.h5 will be downloaded...")
output = home + '/.deepface/weights/race_model_single_batch.h5'
gdown.download(url, output, quiet=False)
"""
#google drive source downloads zip
output = home+'/.deepface/weights/race_model_single_batch.zip'
gdown.download(url, output, quiet=False)
#unzip race_model_single_batch.zip
with zipfile.ZipFile(output, 'r') as zip_ref:
zip_ref.extractall(home+'/.deepface/weights/')
"""
race_model.load_weights(home +
'/.deepface/weights/race_model_single_batch.h5')
return race_model
def build_gbm():
#this is not a must dependency
import lightgbm as lgb #lightgbm==2.3.1
home = functions.get_deepface_home()
if os.path.isfile(home+'/.deepface/weights/face-recognition-ensemble-model.txt') != True:
print("face-recognition-ensemble-model.txt will be downloaded...")
url = 'https://raw.githubusercontent.com/serengil/deepface/master/deepface/models/face-recognition-ensemble-model.txt'
output = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
gdown.download(url, output, quiet=False)
ensemble_model_path = home+'/.deepface/weights/face-recognition-ensemble-model.txt'
deepface_ensemble = lgb.Booster(model_file = ensemble_model_path)
return deepface_ensemble
def build_model():
home = functions.get_deepface_home()
#model structure
if os.path.isfile(home + '/.deepface/weights/deploy.prototxt') != True:
print("deploy.prototxt will be downloaded...")
url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
output = home + '/.deepface/weights/deploy.prototxt'
gdown.download(url, output, quiet=False)
#pre-trained weights
if os.path.isfile(
home +
'/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel'
) != True:
print("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...")
url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
output = home + '/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel'
gdown.download(url, output, quiet=False)
face_detector = cv2.dnn.readNetFromCaffe(
home + "/.deepface/weights/deploy.prototxt",
home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel")
eye_detector = OpenCvWrapper.build_cascade("haarcascade_eye")
detector = {}
detector["face_detector"] = face_detector
detector["eye_detector"] = eye_detector
return detector
def loadModel(
url='https://github.com/serengil/deepface_models/releases/download/v1.0/arcface_weights.h5'
):
base_model = ResNet34()
inputs = base_model.inputs[0]
arcface_model = base_model.outputs[0]
arcface_model = keras.layers.BatchNormalization(
momentum=0.9, epsilon=2e-5)(arcface_model)
arcface_model = keras.layers.Dropout(0.4)(arcface_model)
arcface_model = keras.layers.Flatten()(arcface_model)
arcface_model = keras.layers.Dense(
512,
activation=None,
use_bias=True,
kernel_initializer="glorot_normal")(arcface_model)
embedding = keras.layers.BatchNormalization(momentum=0.9,
epsilon=2e-5,
name="embedding",
scale=True)(arcface_model)
model = keras.models.Model(inputs, embedding, name=base_model.name)
#---------------------------------------
#check the availability of pre-trained weights
home = functions.get_deepface_home()
file_name = "arcface_weights.h5"
output = home + '/.deepface/weights/' + file_name
if os.path.isfile(output) != True:
print(file_name, " will be downloaded to ", output)
gdown.download(url, output, quiet=False)
#---------------------------------------
model.load_weights(output)
return model
def loadModel(
url='https://github.com/serengil/deepface_models/releases/download/v1.0/facenet512_weights.h5'
):
model = Facenet.InceptionResNetV2(dimension=512)
#-------------------------
home = functions.get_deepface_home()
if os.path.isfile(home +
'/.deepface/weights/facenet512_weights.h5') != True:
print("facenet512_weights.h5 will be downloaded...")
output = home + '/.deepface/weights/facenet512_weights.h5'
gdown.download(url, output, quiet=False)
#-------------------------
model.load_weights(home + '/.deepface/weights/facenet512_weights.h5')
#-------------------------
return model
def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/openface_weights.h5'):
myInput = Input(shape=(96, 96, 3))
x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn1')(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = MaxPooling2D(pool_size=3, strides=2)(x)
x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name='lrn_1')(x)
x = Conv2D(64, (1, 1), name='conv2')(x)
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn2')(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(192, (3, 3), name='conv3')(x)
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn3')(x)
x = Activation('relu')(x)
x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name='lrn_2')(x) #x is equal added
x = ZeroPadding2D(padding=(1, 1))(x)
x = MaxPooling2D(pool_size=3, strides=2)(x)
# Inception3a
inception_3a_3x3 = Conv2D(96, (1, 1), name='inception_3a_3x3_conv1')(x)
inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn1')(inception_3a_3x3)
inception_3a_3x3 = Activation('relu')(inception_3a_3x3)
inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)
inception_3a_3x3 = Conv2D(128, (3, 3), name='inception_3a_3x3_conv2')(inception_3a_3x3)
inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn2')(inception_3a_3x3)
inception_3a_3x3 = Activation('relu')(inception_3a_3x3)
inception_3a_5x5 = Conv2D(16, (1, 1), name='inception_3a_5x5_conv1')(x)
inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn1')(inception_3a_5x5)
inception_3a_5x5 = Activation('relu')(inception_3a_5x5)
inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)
inception_3a_5x5 = Conv2D(32, (5, 5), name='inception_3a_5x5_conv2')(inception_3a_5x5)
inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn2')(inception_3a_5x5)
inception_3a_5x5 = Activation('relu')(inception_3a_5x5)
inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)
inception_3a_pool = Conv2D(32, (1, 1), name='inception_3a_pool_conv')(inception_3a_pool)
inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_pool_bn')(inception_3a_pool)
inception_3a_pool = Activation('relu')(inception_3a_pool)
inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool)
inception_3a_1x1 = Conv2D(64, (1, 1), name='inception_3a_1x1_conv')(x)
inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_1x1_bn')(inception_3a_1x1)
inception_3a_1x1 = Activation('relu')(inception_3a_1x1)
inception_3a = concatenate([inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3)
# Inception3b
inception_3b_3x3 = Conv2D(96, (1, 1), name='inception_3b_3x3_conv1')(inception_3a)
inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn1')(inception_3b_3x3)
inception_3b_3x3 = Activation('relu')(inception_3b_3x3)
inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)
inception_3b_3x3 = Conv2D(128, (3, 3), name='inception_3b_3x3_conv2')(inception_3b_3x3)
inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn2')(inception_3b_3x3)
inception_3b_3x3 = Activation('relu')(inception_3b_3x3)
inception_3b_5x5 = Conv2D(32, (1, 1), name='inception_3b_5x5_conv1')(inception_3a)
inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn1')(inception_3b_5x5)
inception_3b_5x5 = Activation('relu')(inception_3b_5x5)
inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)
inception_3b_5x5 = Conv2D(64, (5, 5), name='inception_3b_5x5_conv2')(inception_3b_5x5)
inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn2')(inception_3b_5x5)
inception_3b_5x5 = Activation('relu')(inception_3b_5x5)
inception_3b_pool = Lambda(lambda x: x**2, name='power2_3b')(inception_3a)
inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool)
inception_3b_pool = Lambda(lambda x: x*9, name='mult9_3b')(inception_3b_pool)
inception_3b_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_3b')(inception_3b_pool)
inception_3b_pool = Conv2D(64, (1, 1), name='inception_3b_pool_conv')(inception_3b_pool)
inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_pool_bn')(inception_3b_pool)
inception_3b_pool = Activation('relu')(inception_3b_pool)
inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool)
inception_3b_1x1 = Conv2D(64, (1, 1), name='inception_3b_1x1_conv')(inception_3a)
inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_1x1_bn')(inception_3b_1x1)
inception_3b_1x1 = Activation('relu')(inception_3b_1x1)
inception_3b = concatenate([inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3)
# Inception3c
inception_3c_3x3 = Conv2D(128, (1, 1), strides=(1, 1), name='inception_3c_3x3_conv1')(inception_3b)
inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_3x3_bn1')(inception_3c_3x3)
inception_3c_3x3 = Activation('relu')(inception_3c_3x3)
inception_3c_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3c_3x3)
inception_3c_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name='inception_3c_3x3_conv'+'2')(inception_3c_3x3)
inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_3x3_bn'+'2')(inception_3c_3x3)
inception_3c_3x3 = Activation('relu')(inception_3c_3x3)
inception_3c_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name='inception_3c_5x5_conv1')(inception_3b)
inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_5x5_bn1')(inception_3c_5x5)
inception_3c_5x5 = Activation('relu')(inception_3c_5x5)
inception_3c_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3c_5x5)
inception_3c_5x5 = Conv2D(64, (5, 5), strides=(2, 2), name='inception_3c_5x5_conv'+'2')(inception_3c_5x5)
inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_5x5_bn'+'2')(inception_3c_5x5)
inception_3c_5x5 = Activation('relu')(inception_3c_5x5)
inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b)
inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool)
inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3)
#inception 4a
inception_4a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name='inception_4a_3x3_conv'+'1')(inception_3c)
inception_4a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_3x3_bn'+'1')(inception_4a_3x3)
inception_4a_3x3 = Activation('relu')(inception_4a_3x3)
inception_4a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3)
inception_4a_3x3 = Conv2D(192, (3, 3), strides=(1, 1), name='inception_4a_3x3_conv'+'2')(inception_4a_3x3)
inception_4a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_3x3_bn'+'2')(inception_4a_3x3)
inception_4a_3x3 = Activation('relu')(inception_4a_3x3)
inception_4a_5x5 = Conv2D(32, (1,1), strides=(1,1), name='inception_4a_5x5_conv1')(inception_3c)
inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_5x5_bn1')(inception_4a_5x5)
inception_4a_5x5 = Activation('relu')(inception_4a_5x5)
inception_4a_5x5 = ZeroPadding2D(padding=(2,2))(inception_4a_5x5)
inception_4a_5x5 = Conv2D(64, (5,5), strides=(1,1), name='inception_4a_5x5_conv'+'2')(inception_4a_5x5)
inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_5x5_bn'+'2')(inception_4a_5x5)
inception_4a_5x5 = Activation('relu')(inception_4a_5x5)
inception_4a_pool = Lambda(lambda x: x**2, name='power2_4a')(inception_3c)
inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool)
inception_4a_pool = Lambda(lambda x: x*9, name='mult9_4a')(inception_4a_pool)
inception_4a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_4a')(inception_4a_pool)
inception_4a_pool = Conv2D(128, (1,1), strides=(1,1), name='inception_4a_pool_conv'+'')(inception_4a_pool)
inception_4a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_pool_bn'+'')(inception_4a_pool)
inception_4a_pool = Activation('relu')(inception_4a_pool)
inception_4a_pool = ZeroPadding2D(padding=(2, 2))(inception_4a_pool)
inception_4a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name='inception_4a_1x1_conv'+'')(inception_3c)
inception_4a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_1x1_bn'+'')(inception_4a_1x1)
inception_4a_1x1 = Activation('relu')(inception_4a_1x1)
inception_4a = concatenate([inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3)
#inception4e
inception_4e_3x3 = Conv2D(160, (1,1), strides=(1,1), name='inception_4e_3x3_conv'+'1')(inception_4a)
inception_4e_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_3x3_bn'+'1')(inception_4e_3x3)
inception_4e_3x3 = Activation('relu')(inception_4e_3x3)
inception_4e_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3)
inception_4e_3x3 = Conv2D(256, (3,3), strides=(2,2), name='inception_4e_3x3_conv'+'2')(inception_4e_3x3)
inception_4e_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_3x3_bn'+'2')(inception_4e_3x3)
inception_4e_3x3 = Activation('relu')(inception_4e_3x3)
inception_4e_5x5 = Conv2D(64, (1,1), strides=(1,1), name='inception_4e_5x5_conv'+'1')(inception_4a)
inception_4e_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_5x5_bn'+'1')(inception_4e_5x5)
inception_4e_5x5 = Activation('relu')(inception_4e_5x5)
inception_4e_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5)
inception_4e_5x5 = Conv2D(128, (5,5), strides=(2,2), name='inception_4e_5x5_conv'+'2')(inception_4e_5x5)
inception_4e_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_5x5_bn'+'2')(inception_4e_5x5)
inception_4e_5x5 = Activation('relu')(inception_4e_5x5)
inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a)
inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool)
inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3)
#inception5a
inception_5a_3x3 = Conv2D(96, (1,1), strides=(1,1), name='inception_5a_3x3_conv'+'1')(inception_4e)
inception_5a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_3x3_bn'+'1')(inception_5a_3x3)
inception_5a_3x3 = Activation('relu')(inception_5a_3x3)
inception_5a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3)
inception_5a_3x3 = Conv2D(384, (3,3), strides=(1,1), name='inception_5a_3x3_conv'+'2')(inception_5a_3x3)
inception_5a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_3x3_bn'+'2')(inception_5a_3x3)
inception_5a_3x3 = Activation('relu')(inception_5a_3x3)
inception_5a_pool = Lambda(lambda x: x**2, name='power2_5a')(inception_4e)
inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool)
inception_5a_pool = Lambda(lambda x: x*9, name='mult9_5a')(inception_5a_pool)
inception_5a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_5a')(inception_5a_pool)
inception_5a_pool = Conv2D(96, (1,1), strides=(1,1), name='inception_5a_pool_conv'+'')(inception_5a_pool)
inception_5a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_pool_bn'+'')(inception_5a_pool)
inception_5a_pool = Activation('relu')(inception_5a_pool)
inception_5a_pool = ZeroPadding2D(padding=(1,1))(inception_5a_pool)
inception_5a_1x1 = Conv2D(256, (1,1), strides=(1,1), name='inception_5a_1x1_conv'+'')(inception_4e)
inception_5a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_1x1_bn'+'')(inception_5a_1x1)
inception_5a_1x1 = Activation('relu')(inception_5a_1x1)
inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3)
#inception_5b
inception_5b_3x3 = Conv2D(96, (1,1), strides=(1,1), name='inception_5b_3x3_conv'+'1')(inception_5a)
inception_5b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_3x3_bn'+'1')(inception_5b_3x3)
inception_5b_3x3 = Activation('relu')(inception_5b_3x3)
inception_5b_3x3 = ZeroPadding2D(padding=(1,1))(inception_5b_3x3)
inception_5b_3x3 = Conv2D(384, (3,3), strides=(1,1), name='inception_5b_3x3_conv'+'2')(inception_5b_3x3)
inception_5b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_3x3_bn'+'2')(inception_5b_3x3)
inception_5b_3x3 = Activation('relu')(inception_5b_3x3)
inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a)
inception_5b_pool = Conv2D(96, (1,1), strides=(1,1), name='inception_5b_pool_conv'+'')(inception_5b_pool)
inception_5b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_pool_bn'+'')(inception_5b_pool)
inception_5b_pool = Activation('relu')(inception_5b_pool)
inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool)
inception_5b_1x1 = Conv2D(256, (1,1), strides=(1,1), name='inception_5b_1x1_conv'+'')(inception_5a)
inception_5b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_1x1_bn'+'')(inception_5b_1x1)
inception_5b_1x1 = Activation('relu')(inception_5b_1x1)
inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3)
av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b)
reshape_layer = Flatten()(av_pool)
dense_layer = Dense(128, name='dense_layer')(reshape_layer)
norm_layer = Lambda(lambda x: K.l2_normalize(x, axis=1), name='norm_layer')(dense_layer)
# Final Model
model = Model(inputs=[myInput], outputs=norm_layer)
#-----------------------------------
home = functions.get_deepface_home()
if os.path.isfile(home+'/.deepface/weights/openface_weights.h5') != True:
print("openface_weights.h5 will be downloaded...")
output = home+'/.deepface/weights/openface_weights.h5'
gdown.download(url, output, quiet=False)
#-----------------------------------
model.load_weights(home+'/.deepface/weights/openface_weights.h5')
#-----------------------------------
return model
