def __func_4e__(self, input_shape): from keras.layers import ZeroPadding2D, Input, concatenate from keras.models import Model from keras.layers.pooling import MaxPooling2D inception_4a = Input(input_shape) # inception4e inception_4e_3x3 = utils.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 = utils.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) model = Model(inputs=inception_4a, outputs=inception_4e) return model
def __func_5b__(self, input_shape): from keras.layers import ZeroPadding2D, Input, concatenate from keras.models import Model from keras.layers.pooling import MaxPooling2D inception_5a = Input(input_shape) # inception_5b inception_5b_3x3 = utils.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 = utils.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 = utils.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) model = Model(inputs=inception_5a, outputs=inception_5b) return model
def __func3__(self, input_shape): from Executer import utils from keras.layers import ZeroPadding2D from keras.layers.pooling import MaxPooling2D from keras.layers import Input, concatenate from keras.models import Model inception_3b = Input(shape=input_shape) inception_3c_3x3 = utils.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 = utils.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) model = Model(inputs=inception_3b, outputs=inception_3c) return model
def __func4__(self, input_shape): from keras.layers import concatenate from keras.layers.pooling import AveragePooling2D from keras.layers.core import Lambda from keras import backend as K from keras.layers import Input from keras.models import Model from Executer import utils inception_3c = Input(shape=input_shape) inception_4a_3x3 = utils.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 = utils.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 = 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 = utils.conv2d_bn(inception_4a_pool, layer='inception_4a_pool', cv1_out=128, cv1_filter=(1, 1), padding=(2, 2)) inception_4a_1x1 = utils.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) model = Model(inputs=inception_3c, outputs=inception_4a) return model
def __func17__(self, input_shapes): from keras.layers import Input from keras.models import Model from keras.layers.pooling import AveragePooling2D from keras.layers.core import Lambda from keras import backend as K from Executer import utils inception_3c = Input(shape=input_shapes) 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 = utils.conv2d_bn(inception_4a_pool, layer='inception_4a_pool', cv1_out=128, cv1_filter=(1, 1), padding=(2, 2)) model = Model(inputs=inception_3c, outputs=inception_4a_pool) return model
def __func13__(self, input_shape): from keras.layers.pooling import AveragePooling2D from keras.layers.core import Lambda, Flatten, Dense from keras import backend as K from keras.layers import concatenate from keras.layers import ZeroPadding2D, Input from keras.models import Model from keras.layers.pooling import MaxPooling2D from Executer import utils inception_5a = Input(shape=input_shape) inception_5b_3x3 = utils.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 = utils.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 = utils.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) model = Model(inputs=inception_5a, outputs=norm_layer) return model
def __func_5a__(self, input_shape): from keras.layers import Input, concatenate from keras.models import Model from keras.layers.pooling import AveragePooling2D from keras.layers.core import Lambda from keras import backend as K inception_4e = Input(shape=input_shape) # inception5a inception_5a_3x3 = utils.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 = 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 = utils.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) model = Model(inputs=inception_4e, outputs=inception_5a) return model
def __func30__(self, input_shape): from keras.layers import Input from keras.models import Model from Executer import utils inception_5a = Input(shape=input_shape) inception_5b_1x1 = utils.conv2d_bn(inception_5a, layer='inception_5b_1x1', cv1_out=256, cv1_filter=(1, 1)) model = Model(inputs=inception_5a, outputs=inception_5b_1x1) return model
def __func29__(self, input_shape): from keras.layers import ZeroPadding2D, Input from keras.models import Model from keras.layers.pooling import MaxPooling2D from Executer import utils inception_5a = Input(shape=input_shape) inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a) inception_5b_pool = utils.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) model = Model(inputs=inception_5a, outputs=inception_5b_pool) return model
def __func11__(self, input_shape): from keras.layers import Input from keras.models import Model from Executer import utils inception_3b = Input(shape=input_shape) inception_3c_3x3 = utils.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)) model = Model(inputs=inception_3b, outputs=inception_3c_3x3) return model
def __func28__(self, input_shape): from keras.layers import Input from keras.models import Model from Executer import utils inception_5a = Input(shape=input_shape) inception_5b_3x3 = utils.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)) model = Model(inputs=inception_5a, outputs=inception_5b_3x3) return model
def __func21__(self, input_shape): from keras.layers import Input from keras.models import Model from Executer import utils inception_4a = Input(shape=input_shape) inception_4e_5x5 = utils.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)) model = Model(inputs=inception_4a, outputs=inception_4e_5x5) return model
def __func0__(self, input_shape): 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 from keras import backend as K from .utils import LRN2D if input_shape == None: input_shape = (96, 96, 3) myInput = Input(shape=input_shape) 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 = 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 = utils.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 = utils.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 = utils.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 = utils.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 = 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 = utils.conv2d_bn(inception_4a_pool, layer='inception_4a_pool', cv1_out=128, cv1_filter=(1, 1), padding=(2, 2)) inception_4a_1x1 = utils.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 = utils.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 = utils.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 = utils.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 = 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 = utils.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 = utils.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 = utils.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 = utils.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) model = Model(inputs=myInput, outputs=x) return model