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Inception_block.py
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Inception_block.py
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import tensorflow as tf
import numpy as np
import os
from numpy import genfromtxt
from keras import backend as K
from keras.layers import Conv2D,ZeroPadding2D,Activation,Input,concatenate
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D,AveragePooling2D
from keras.layers.core import Lambda,Flatten,Dense
def inception_block_1a(X):
X_3x3=Conv2D(96,(1,1),data_format='channels_first',name='inception_3a_3x3_conv1')(X)
X_3x3=BatchNormalization(axis=1,epsilon=0.00001,name='inception_3a_3x3_bn1')(X_3x3)
X_3x3=Activation('relu')(X_3x3)
X_3x3 = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_3x3)
X_3x3=Conv2D(128,(3,3),data_format='channels_first',name='inception_3a_3x3_conv2')(X_3x3)
X_3x3=BatchNormalization(axis=1,epsilon=0.00001,name='inception_3a_3x3_bn2')(X_3x3)
X_3x3=Activation('relu') (X_3x3)
X_5x5=Conv2D(16,(1,1),data_format='channels_first',name='inception_3a_5x5_conv1')(X)
X_5x5=BatchNormalization(axis=1,epsilon=0.00001,name='inception_3a_5x5_bn1')(X_5x5)
X_5x5=Activation('relu')(X_5x5)
X_5x5=ZeroPadding2D(padding=(2,2),data_format='channels_first')(X_5x5)
X_5x5=Conv2D(32,(5,5),data_format='channels_first',name='inception_3a_5x5_conv2')(X_5x5)
X_5x5=BatchNormalization(axis=1,epsilon=0.00001,name='inception_3a_5x5_bn2')(X_5x5)
X_5x5=Activation('relu')(X_5x5)
X_pool=MaxPooling2D(pool_size=3,strides=2,data_format='channels_first')(X)
X_pool=Conv2D(32,(1,1),data_format='channels_first',name='inception_3a_pool_conv')(X_pool)
X_pool=BatchNormalization(axis=1,epsilon=0.00001,name='inception_3a_pool_bn')(X_pool)
X_pool=Activation('relu')(X_pool)
X_pool=ZeroPadding2D(padding=((3,4),(3,4)),data_format='channels_first')(X_pool)
X_1x1=Conv2D(64,(1,1),data_format='channels_first',name='inception_3a_1x1_conv')(X)
X_1x1=BatchNormalization(axis=1,epsilon=0.00001,name='inception_3a_1x1_bn')(X_1x1)
X_1x1=Activation('relu')(X_1x1)
inception=concatenate([X_3x3,X_5x5,X_pool,X_1x1],axis=1)
return inception
def inception_block_1b(X):
X_3x3 = Conv2D(96, (1, 1), data_format='channels_first', name='inception_3b_3x3_conv1')(X)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_3x3_bn1')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_3x3 = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_3x3)
X_3x3 = Conv2D(128, (3, 3), data_format='channels_first', name='inception_3b_3x3_conv2')(X_3x3)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_3x3_bn2')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_5x5 = Conv2D(32, (1, 1), data_format='channels_first', name='inception_3b_5x5_conv1')(X)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_5x5_bn1')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_5x5 = ZeroPadding2D(padding=(2, 2), data_format='channels_first')(X_5x5)
X_5x5 = Conv2D(64, (5, 5), data_format='channels_first', name='inception_3b_5x5_conv2')(X_5x5)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_5x5_bn2')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3), data_format='channels_first')(X)
X_pool = Conv2D(64, (1, 1), data_format='channels_first', name='inception_3b_pool_conv')(X_pool)
X_pool = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_pool_bn')(X_pool)
X_pool = Activation('relu')(X_pool)
X_pool = ZeroPadding2D(padding=(4, 4), data_format='channels_first')(X_pool)
X_1x1 = Conv2D(64, (1, 1), data_format='channels_first', name='inception_3b_1x1_conv')(X)
X_1x1 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_1x1_bn')(X_1x1)
X_1x1 = Activation('relu')(X_1x1)
inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=1)
return inception
def inception_block_1c(X):
X_3x3 = Conv2D(128, (1, 1), data_format='channels_first', name='inception_3c_3x3_conv1')(X)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3c_3x3_bn1')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_3x3 = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_3x3)
X_3x3 = Conv2D(256, (3, 3),strides=(2,2), data_format='channels_first', name='inception_3c_3x3_conv2')(X_3x3)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3c_3x3_bn2')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_5x5 = Conv2D(32, (1, 1), data_format='channels_first', name='inception_3c_5x5_conv1')(X)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3c_5x5_bn1')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_5x5 = ZeroPadding2D(padding=(2, 2), data_format='channels_first')(X_5x5)
X_5x5 = Conv2D(64, (5, 5),strides=(2,2), data_format='channels_first', name='inception_3c_5x5_conv2')(X_5x5)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3c_5x5_bn2')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X)
X_pool = ZeroPadding2D(padding=((0,1),(0,1)), data_format='channels_first')(X_pool)
inception = concatenate([X_3x3, X_5x5, X_pool], axis=1)
return inception
def inception_block_2a(X):
X_3x3 = Conv2D(96, (1, 1), data_format='channels_first', name='inception_4a_3x3_conv1')(X)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_4a_3x3_bn1')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_3x3 = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_3x3)
X_3x3 = Conv2D(192, (3, 3),strides=(1,1), data_format='channels_first', name='inception_4a_3x3_conv2')(X_3x3)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_4a_3x3_bn2')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_5x5 = Conv2D(32, (1, 1), data_format='channels_first', name='inception_4a_5x5_conv1')(X)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_4a_5x5_bn1')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_5x5 = ZeroPadding2D(padding=(2, 2), data_format='channels_first')(X_5x5)
X_5x5 = Conv2D(64, (5, 5),strides=(1,1), data_format='channels_first', name='inception_4a_5x5_conv2')(X_5x5)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_4a_5x5_bn2')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3), data_format='channels_first')(X)
X_pool = Conv2D(128, (1, 1), data_format='channels_first', name='inception_4a_pool_conv')(X_pool)
X_pool = BatchNormalization(axis=1, epsilon=0.00001, name='inception_4a_pool_bn')(X_pool)
X_pool = Activation('relu')(X_pool)
X_pool = ZeroPadding2D(padding=(2,2), data_format='channels_first')(X_pool)
X_1x1=Conv2D(256,(1,1),data_format='channels_first',name='inception_4a_1x1_conv')(X)
X_1x1=BatchNormalization(axis=1,epsilon=0.00001,name='inception_4a_1x1_bn')(X_1x1)
X_1x1=Activation('relu')(X_1x1)
inception = concatenate([X_3x3, X_5x5, X_pool,X_1x1], axis=1)
return inception
def inception_block_2b(X):
X_3x3 = Conv2D(160, (1, 1), data_format='channels_first', name='inception_4e_3x3_conv1')(X)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_4e_3x3_bn1')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_3x3 = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_3x3)
X_3x3 = Conv2D(256, (3, 3),strides=(2,2), data_format='channels_first', name='inception_4e_3x3_conv2')(X_3x3)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_4e_3x3_bn2')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_5x5 = Conv2D(64, (1, 1), data_format='channels_first', name='inception_4e_5x5_conv1')(X)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_4e_5x5_bn1')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_5x5 = ZeroPadding2D(padding=(2, 2), data_format='channels_first')(X_5x5)
X_5x5 = Conv2D(128, (5, 5),strides=(2,2), data_format='channels_first', name='inception_4e_5x5_conv2')(X_5x5)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_4e_5x5_bn2')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_pool = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), data_format='channels_first')(X)
X_pool = ZeroPadding2D(padding=((0,1),(0,1)), data_format='channels_first')(X_pool)
inception = concatenate([X_3x3, X_5x5, X_pool], axis=1)
return inception
def inception_block_3a(X):
X_3x3 = Conv2D(96, (1, 1), data_format='channels_first', name='inception_5a_3x3_conv1')(X)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_5a_3x3_bn1')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_3x3 = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_3x3)
X_3x3 = Conv2D(384, (3, 3), data_format='channels_first', name='inception_5a_3x3_conv2')(X_3x3)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_5a_3x3_bn2')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3), data_format='channels_first')(X)
X_pool = Conv2D(96, (1, 1), data_format='channels_first', name='inception_5a_pool_conv')(X_pool)
X_pool = BatchNormalization(axis=1, epsilon=0.00001, name='inception_5a_pool_bn')(X_pool)
X_pool = Activation('relu')(X_pool)
X_pool = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_pool)
X_1x1 = Conv2D(256, (1, 1), data_format='channels_first', name='inception_5a_1x1_conv')(X)
X_1x1 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_5a_1x1_bn')(X_1x1)
X_1x1 = Activation('relu')(X_1x1)
inception = concatenate([X_3x3, X_pool, X_1x1], axis=1)
return inception
def inception_block_3b(X):
X_3x3=Conv2D(96,(1,1),data_format='channels_first',name='inception_5b_3x3_conv1')(X)
X_3x3=BatchNormalization(axis=1,epsilon=0.00001,name='inception_5b_3x3_bn1')(X_3x3)
X_3x3=Activation('relu')(X_3x3)
X_3x3=Conv2D(384,(3,3),data_format='channels_first',name='inception_5b_3x3_conv2')(X_3x3)
X_3x3=BatchNormalization(axis=1,epsilon=0.00001,name='inception_5b_3x3_bn2')(X_3x3)
X_3x3=Activation('relu') (X_3x3)
X_3x3=ZeroPadding2D(padding=(1,1),data_format='channels_first')(X_3x3)
X_pool=MaxPooling2D(pool_size=3,strides=2,data_format='channels_first')(X)
X_pool=Conv2D(96,(1,1),data_format='channels_first',name='inception_5b_pool_conv')(X_pool)
X_pool=BatchNormalization(axis=1,epsilon=0.00001,name='inception_5b_pool_bn')(X_pool)
X_pool=Activation('relu')(X_pool)
X_pool=ZeroPadding2D(padding=(1,1),data_format='channels_first')(X_pool)
X_1x1=Conv2D(256,(1,1),data_format='channels_first',name='inception_5b_1x1_conv')(X)
X_1x1=BatchNormalization(axis=1,epsilon=0.00001,name='inception_5b_1x1_bn')(X_1x1)
X_1x1=Activation('relu')(X_1x1)
inception=concatenate([X_3x3,X_pool,X_1x1],axis=1)
return inception
def faceRecoModel(input_shape):
X_input=Input(input_shape)
X=ZeroPadding2D((3,3))(X_input)
X=Conv2D(64,(7,7),strides=(2,2),name='conv1')(X)
X=BatchNormalization(axis=1, name='bn1')(X)
X=Activation('relu')(X)
X=ZeroPadding2D((1,1))(X)
X=MaxPooling2D((3,3),strides=2)(X)
X = Conv2D(64, (1, 1), strides = (1, 1), name = 'conv2')(X)
X = BatchNormalization(axis = 1, epsilon=0.00001, name = 'bn2')(X)
X = Activation('relu')(X)
X = ZeroPadding2D((1, 1))(X)
X=Conv2D(192,(3,3),strides=(1,1),name='conv3')(X)
X=BatchNormalization(axis=1,epsilon=0.00001,name='bn3')(X)
X=Activation('relu')(X)
X=ZeroPadding2D((1,1))(X)
X=MaxPooling2D(pool_size=3,strides=2)(X)
X=inception_block_1a(X)
X=inception_block_1b(X)
X=inception_block_1c(X)
X=inception_block_2a(X)
X=inception_block_2b(X)
X=inception_block_3a(X)
X=inception_block_3b(X)
X=AveragePooling2D(pool_size=(3,3),strides=(1,1),data_format='channels_first')(X)
X=Flatten()(X)
X=Dense(128,name='dense_layer')(X)
X=Lambda(lambda x: K.l2_normalize(x,axis=1))(X)
model=Model(inputs=X_input,outputs=X,name='FaceRecoModel')
return model