import sys sys.path.append("/Users/muzalevskiy/desktop/facenet") from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate from keras.layers.core import Lambda, Flatten, Dense from keras.layers.normalization import BatchNormalization from keras.layers.pooling import MaxPooling2D, AveragePooling2D from keras.models import Model from keras import backend as K import utils from utils import LRN2D from utils import conv2d_bn def create_model(): 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(LRN2D, 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(LRN2D, name='lrn_2')(x) 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 = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3a) 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_bn(inception_3b, layer='inception_3c_3x3', cv1_out=128, cv1_filter=(1, 1), cv2_out=256, cv2_filter=(3, 3), cv2_strides=(2, 2), padding=(1, 1)) inception_3c_5x5 = .conv2d_bn(inception_3b, layer='inception_3c_5x5', cv1_out=32, cv1_filter=(1, 1), cv2_out=64, cv2_filter=(5, 5), cv2_strides=(2, 2), padding=(2, 2)) 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_bn(inception_3c, layer='inception_4a_3x3', cv1_out=96, cv1_filter=(1, 1), cv2_out=192, cv2_filter=(3, 3), cv2_strides=(1, 1), padding=(1, 1)) inception_4a_5x5 = conv2d_bn(inception_3c, layer='inception_4a_5x5', cv1_out=32, cv1_filter=(1, 1), cv2_out=64, cv2_filter=(5, 5), cv2_strides=(1, 1), padding=(2, 2)) inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3c) inception_4a_pool = conv2d_bn(inception_4a_pool, layer='inception_4a_pool', cv1_out=128, cv1_filter=(1, 1), padding=(2, 2)) inception_4a_1x1 = conv2d_bn(inception_3c, layer='inception_4a_1x1', cv1_out=256, cv1_filter=(1, 1)) inception_4a = concatenate([inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3) #inception4e inception_4e_3x3 = conv2d_bn(inception_4a, layer='inception_4e_3x3', cv1_out=160, cv1_filter=(1, 1), cv2_out=256, cv2_filter=(3, 3), cv2_strides=(2, 2), padding=(1, 1)) inception_4e_5x5 = conv2d_bn(inception_4a, layer='inception_4e_5x5', cv1_out=64, cv1_filter=(1, 1), cv2_out=128, cv2_filter=(5, 5), cv2_strides=(2, 2), padding=(2, 2)) 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_bn(inception_4e, layer='inception_5a_3x3', cv1_out=96, cv1_filter=(1, 1), cv2_out=384, cv2_filter=(3, 3), cv2_strides=(1, 1), padding=(1, 1)) inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4e) inception_5a_pool = conv2d_bn(inception_5a_pool, layer='inception_5a_pool', cv1_out=96, cv1_filter=(1, 1), padding=(1, 1)) inception_5a_1x1 = utils.conv2d_bn(inception_4e, layer='inception_5a_1x1', cv1_out=256, cv1_filter=(1, 1)) inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3) #inception_5b inception_5b_3x3 = conv2d_bn(inception_5a, layer='inception_5b_3x3', cv1_out=96, cv1_filter=(1, 1), cv2_out=384, cv2_filter=(3, 3), cv2_strides=(1, 1), padding=(1, 1)) inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a) inception_5b_pool = conv2d_bn(inception_5b_pool, layer='inception_5b_pool', cv1_out=96, cv1_filter=(1, 1)) inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool) inception_5b_1x1 = conv2d_bn(inception_5a, layer='inception_5b_1x1', cv1_out=256, cv1_filter=(1, 1)) 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) return Model(inputs=[myInput], outputs=norm_layer)