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posenet.py
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posenet.py
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from tensorflow.keras.layers import Input, Dense, Convolution2D
from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D
from tensorflow.keras.layers import ZeroPadding2D, Dropout, Flatten
from tensorflow.keras.layers import concatenate, Reshape, Activation, BatchNormalization,LSTM, Bidirectional, Layer, ReLU, Convolution2DTranspose
import helper
from tensorflow.keras import backend as K
from tensorflow.keras.models import Model
import tensorflow as tf
import numpy as np
import h5py
import math
import tensorflow
class Geoloss_x(Layer):
def __init__(self, sx):
super(Geoloss_x, self).__init__()
self.sx = K.variable(sx)
def get_vars(self):
return self.sx
def custom_loss(self, y_true, y_pred2):
return tensorflow.math.exp(-self.sx) * K.sqrt(K.sum(K.square(y_true[:,:] - y_pred2[:,:]), axis=1, keepdims=True)) + self.sx
def get_config(self):
config = super().get_config().copy()
config.update({
'sx': self.sx.numpy()
})
return config
def call(self, y_true, y_pred2):
self.add_loss(self.custom_loss(y_true, y_pred2))
self.add_metric(self.sx,"sx")
self.add_metric(self.custom_loss(y_true, y_pred2),"loss_x")
return y_pred2
class Geoloss_q(Layer):
def __init__(self, sq):
super(Geoloss_q, self).__init__()
self.sq = K.variable(sq) # or tf.Variable(var1) etc.
def get_vars(self):
return self.sq
def custom_loss(self, y_true, y_pred):
return tensorflow.math.exp(-self.sq) * K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True)) + self.sq
def get_config(self):
config = super().get_config().copy()
config.update({
'sq': self.sq.numpy()
})
return config
def call(self, y_true, y_pred):
self.add_loss(self.custom_loss(y_true, y_pred))
self.add_metric(self.sq, "sq")
self.add_metric(self.custom_loss(y_true, y_pred),"loss_q")
return y_pred
class max_loss(Layer):
def __init__(self):
super(max_loss, self).__init__()
def custom_loss(self, y_true_x, y_pred_x, y_true_q, y_pred_q):
lq = K.sqrt(K.sum(K.square(y_true_x[:, :] - y_pred_x[:, :]), axis=1, keepdims=True))
q1 = y_true_q[:, :] / tf.norm(y_true_q)
q2 = y_pred_q[:, :] / tf.norm(y_pred_q)
d = tf.math.abs(tf.tensordot(q1, q2,axes=[[1], [1]]))
d=tf.linalg.diag_part(d)
theta = 2 * tf.math.acos(d) * 180 / math.pi
loss = tf.math.maximum(theta, lq)
return loss
def call(self, y_true_x, y_pred_x, y_true_q, y_pred_q):
self.add_loss(self.custom_loss(y_true_x, y_pred_x, y_true_q, y_pred_q))
return y_pred_x, y_true_q
class combined_loss(Layer):
def __init__(self):
super(combined_loss, self).__init__()
def custom_loss(self, y_true_x, y_pred_x, y_true_q, y_pred_q):
lq = K.sqrt(K.sum(K.square(y_true_x[:, :] - y_pred_x[:, :]), axis=1, keepdims=True))
q1 = y_true_q[:, :] / tf.norm(y_true_q)
q2 = y_pred_q[:, :] / tf.norm(y_pred_q)
d = tf.math.abs(tf.tensordot(q1, q2,axes=[[1], [1]]))
d=tf.linalg.diag_part(d)
theta = 2 * tf.math.acos(d) * 180 / math.pi
loss = theta*2+lq
return loss
def lq_loss(self,y_true_x, y_pred_x, y_true_q, y_pred_q):
lq = K.sqrt(K.sum(K.square(y_true_x[:, :] - y_pred_x[:, :]), axis=1, keepdims=True))
return lq
def theta_loss(self,y_true_x, y_pred_x, y_true_q, y_pred_q):
q1 = y_true_q[:, :] / tf.norm(y_true_q)
q2 = y_pred_q[:, :] / tf.norm(y_pred_q)
d = tf.math.abs(tf.tensordot(q1, q2, axes=[[1], [1]]))
d = tf.linalg.diag_part(d)
theta = 2 * tf.math.acos(d) * 180 / math.pi
return theta
def call(self, y_true_x, y_pred_x, y_true_q, y_pred_q):
self.add_loss(self.custom_loss(y_true_x, y_pred_x, y_true_q, y_pred_q))
self.add_metric(self.lq_loss(y_true_x, y_pred_x, y_true_q, y_pred_q),"lq")
self.add_metric(self.theta_loss(y_true_x, y_pred_x, y_true_q, y_pred_q), "theta")
return y_pred_x, y_true_q
def euc_loss1x(y_true, y_pred):
lx = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
return (0.3 * lx)
def euc_loss1q(y_true, y_pred):
lq = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
return (150 * lq)
def euc_loss2x(y_true, y_pred):
lx = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
return (0.3 * lx)
def euc_loss2q(y_true, y_pred):
lq = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
return (150 * lq)
def euc_loss3x(y_true, y_pred):
lx = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
return (1 * lx)
def euc_loss3q(y_true, y_pred):
lq = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
return (500 * lq)
def create_posenet(bayesian,weights_path=None, tune=False):
# creates Posenet from GoogLeNet a.k.a. Inception v1 (Szegedy, 2015)
with tf.device('/gpu:0'):
input = Input(shape=(224, 224, 3))
conv1 = Convolution2D(64,(7,7),strides=(2,2),padding='same',activation='relu',name='conv1')(input)
pool1 = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='same',name='pool1')(conv1)
norm1 = BatchNormalization(axis=3, name='norm1')(pool1)
reduction2 = Convolution2D(64,(1,1),padding='same',activation='relu',name='reduction2')(norm1)
conv2 = Convolution2D(192,(3,3),padding='same',activation='relu',name='conv2')(reduction2)
norm2 = BatchNormalization(axis=3, name='norm2')(conv2)
pool2 = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='valid',name='pool2')(norm2)
icp1_reduction1 = Convolution2D(96,(1,1),padding='same',activation='relu',name='icp1_reduction1')(pool2)
icp1_out1 = Convolution2D(128,(3,3),padding='same',activation='relu',name='icp1_out1')(icp1_reduction1)
icp1_reduction2 = Convolution2D(16,(1,1),padding='same',activation='relu',name='icp1_reduction2')(pool2)
icp1_out2 = Convolution2D(32,(5,5),padding='same',activation='relu',name='icp1_out2')(icp1_reduction2)
icp1_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp1_pool')(pool2)
icp1_out3 = Convolution2D(32,(1,1),padding='same',activation='relu',name='icp1_out3')(icp1_pool)
icp1_out0 = Convolution2D(64,(1,1),padding='same',activation='relu',name='icp1_out0')(pool2)
icp2_in = concatenate([icp1_out0, icp1_out1, icp1_out2, icp1_out3],axis=3,name='icp2_in')
icp2_reduction1 = Convolution2D(128,(1,1),padding='same',activation='relu',name='icp2_reduction1')(icp2_in)
icp2_out1 = Convolution2D(192,(3,3),padding='same',activation='relu',name='icp2_out1')(icp2_reduction1)
icp2_reduction2 = Convolution2D(32,(1,1),padding='same',activation='relu',name='icp2_reduction2')(icp2_in)
icp2_out2 = Convolution2D(96,(5,5),padding='same',activation='relu',name='icp2_out2')(icp2_reduction2)
icp2_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp2_pool')(icp2_in)
icp2_out3 = Convolution2D(64,(1,1),padding='same',activation='relu',name='icp2_out3')(icp2_pool)
icp2_out0 = Convolution2D(128,(1,1),padding='same',activation='relu',name='icp2_out0')(icp2_in)
icp2_out = concatenate([icp2_out0, icp2_out1, icp2_out2, icp2_out3],axis=3,name='icp2_out')
icp3_in = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='same',name='icp3_in')(icp2_out)
icp3_reduction1 = Convolution2D(96,(1,1),padding='same',activation='relu',name='icp3_reduction1')(icp3_in)
icp3_out1 = Convolution2D(208,(3,3),padding='same',activation='relu',name='icp3_out1')(icp3_reduction1)
icp3_reduction2 = Convolution2D(16,(1,1),padding='same',activation='relu',name='icp3_reduction2')(icp3_in)
icp3_out2 = Convolution2D(48,(5,5),padding='same',activation='relu',name='icp3_out2')(icp3_reduction2)
icp3_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp3_pool')(icp3_in)
icp3_out3 = Convolution2D(64,(1,1),padding='same',activation='relu',name='icp3_out3')(icp3_pool)
icp3_out0 = Convolution2D(192,(1,1),padding='same',activation='relu',name='icp3_out0')(icp3_in)
icp3_out = concatenate([icp3_out0, icp3_out1, icp3_out2, icp3_out3],axis=3,name='icp3_out')
cls1_pool = AveragePooling2D(pool_size=(5,5),strides=(3,3),padding='valid',name='cls1_pool')(icp3_out)
cls1_reduction_pose = Convolution2D(128,(1,1),padding='same',activation='relu',name='cls1_reduction_pose')(cls1_pool)
cls1_fc1_flat = Flatten()(cls1_reduction_pose)
cls1_fc1_pose = Dense(1024,activation='relu',name='cls1_fc1_pose')(cls1_fc1_flat)
cls1_fc_pose_xyz = Dense(3,name='cls1_fc_pose_xyz')(cls1_fc1_pose)
cls1_fc_pose_wpqr = Dense(4,name='cls1_fc_pose_wpqr')(cls1_fc1_pose)
icp4_reduction1 = Convolution2D(112,(1,1),padding='same',activation='relu',name='icp4_reduction1')(icp3_out)
icp4_out1 = Convolution2D(224,(3,3),padding='same',activation='relu',name='icp4_out1')(icp4_reduction1)
icp4_reduction2 = Convolution2D(24,(1,1),padding='same',activation='relu',name='icp4_reduction2')(icp3_out)
icp4_out2 = Convolution2D(64,(5,5),padding='same',activation='relu',name='icp4_out2')(icp4_reduction2)
icp4_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp4_pool')(icp3_out)
icp4_out3 = Convolution2D(64,(1,1),padding='same',activation='relu',name='icp4_out3')(icp4_pool)
icp4_out0 = Convolution2D(160,(1,1),padding='same',activation='relu',name='icp4_out0')(icp3_out)
icp4_out = concatenate([icp4_out0, icp4_out1, icp4_out2, icp4_out3],axis=3,name='icp4_out')
icp5_reduction1 = Convolution2D(128,(1,1),padding='same',activation='relu',name='icp5_reduction1')(icp4_out)
icp5_out1 = Convolution2D(256,(3,3),padding='same',activation='relu',name='icp5_out1')(icp5_reduction1)
icp5_reduction2 = Convolution2D(24,(1,1),padding='same',activation='relu',name='icp5_reduction2')(icp4_out)
icp5_out2 = Convolution2D(64,(5,5),padding='same',activation='relu',name='icp5_out2')(icp5_reduction2)
icp5_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp5_pool')(icp4_out)
icp5_out3 = Convolution2D(64,(1,1),padding='same',activation='relu',name='icp5_out3')(icp5_pool)
icp5_out0 = Convolution2D(128,(1,1),padding='same',activation='relu',name='icp5_out0')(icp4_out)
icp5_out = concatenate([icp5_out0, icp5_out1, icp5_out2, icp5_out3],axis=3,name='icp5_out')
icp6_reduction1 = Convolution2D(144,(1,1),padding='same',activation='relu',name='icp6_reduction1')(icp5_out)
icp6_out1 = Convolution2D(288,(3,3),padding='same',activation='relu',name='icp6_out1')(icp6_reduction1)
icp6_reduction2 = Convolution2D(32,(1,1),padding='same',activation='relu',name='icp6_reduction2')(icp5_out)
icp6_out2 = Convolution2D(64,(5,5),padding='same',activation='relu',name='icp6_out2')(icp6_reduction2)
icp6_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp6_pool')(icp5_out)
icp6_out3 = Convolution2D(64,(1,1),padding='same',activation='relu',name='icp6_out3')(icp6_pool)
icp6_out0 = Convolution2D(112,(1,1),padding='same',activation='relu',name='icp6_out0')(icp5_out)
icp6_out = concatenate([icp6_out0, icp6_out1, icp6_out2, icp6_out3],axis=3,name='icp6_out')
cls2_pool = AveragePooling2D(pool_size=(5,5),strides=(3,3),padding='valid',name='cls2_pool')(icp6_out)
cls2_reduction_pose = Convolution2D(128,(1,1),padding='same',activation='relu',name='cls2_reduction_pose')(cls2_pool)
cls2_fc1_flat = Flatten()(cls2_reduction_pose)
cls2_fc1 = Dense(1024,activation='relu',name='cls2_fc1')(cls2_fc1_flat)
cls2_fc_pose_xyz = Dense(3,name='cls2_fc_pose_xyz')(cls2_fc1)
cls2_fc_pose_wpqr = Dense(4,name='cls2_fc_pose_wpqr')(cls2_fc1)
icp7_reduction1 = Convolution2D(160,(1,1),padding='same',activation='relu',name='icp7_reduction1')(icp6_out)
icp7_out1 = Convolution2D(320,(3,3),padding='same',activation='relu',name='icp7_out1')(icp7_reduction1)
icp7_reduction2 = Convolution2D(32,(1,1),padding='same',activation='relu',name='icp7_reduction2')(icp6_out)
icp7_out2 = Convolution2D(128,(5,5),padding='same',activation='relu',name='icp7_out2')(icp7_reduction2)
icp7_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp7_pool')(icp6_out)
icp7_out3 = Convolution2D(128,(1,1),padding='same',activation='relu',name='icp7_out3')(icp7_pool)
icp7_out0 = Convolution2D(256,(1,1),padding='same',activation='relu',name='icp7_out0')(icp6_out)
icp7_out = concatenate([icp7_out0, icp7_out1, icp7_out2, icp7_out3],axis=3,name='icp7_out')
icp8_in = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='same',name='icp8_in')(icp7_out)
icp8_reduction1 = Convolution2D(160,(1,1),padding='same',activation='relu',name='icp8_reduction1')(icp8_in)
icp8_out1 = Convolution2D(320,(3,3),padding='same',activation='relu',name='icp8_out1')(icp8_reduction1)
icp8_reduction2 = Convolution2D(32,(1,1),padding='same',activation='relu',name='icp8_reduction2')(icp8_in)
icp8_out2 = Convolution2D(128,(5,5),padding='same',activation='relu',name='icp8_out2')(icp8_reduction2)
icp8_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp8_pool')(icp8_in)
icp8_out3 = Convolution2D(128,(1,1),padding='same',activation='relu',name='icp8_out3')(icp8_pool)
icp8_out0 = Convolution2D(256,(1,1),padding='same',activation='relu',name='icp8_out0')(icp8_in)
icp8_out = concatenate([icp8_out0, icp8_out1, icp8_out2, icp8_out3],axis=3,name='icp8_out')
icp9_reduction1 = Convolution2D(192,(1,1),padding='same',activation='relu',name='icp9_reduction1')(icp8_out)
icp9_out1 = Convolution2D(384,(3,3),padding='same',activation='relu',name='icp9_out1')(icp9_reduction1)
icp9_reduction2 = Convolution2D(48,(1,1),padding='same',activation='relu',name='icp9_reduction2')(icp8_out)
icp9_out2 = Convolution2D(128,(5,5),padding='same',activation='relu',name='icp9_out2')(icp9_reduction2)
icp9_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp9_pool')(icp8_out)
icp9_out3 = Convolution2D(128,(1,1),padding='same',activation='relu',name='icp9_out3')(icp9_pool)
icp9_out0 = Convolution2D(384,(1,1),padding='same',activation='relu',name='icp9_out0')(icp8_out)
icp9_out = concatenate([icp9_out0, icp9_out1, icp9_out2, icp9_out3],axis=3,name='icp9_out')
cls3_pool = AveragePooling2D(pool_size=(7,7),strides=(1,1),padding='valid',name='cls3_pool')(icp9_out)
if bayesian==True:
cls3_pool=Dropout(0.5)(cls3_pool,training=True)
cls3_fc1_flat = Flatten()(cls3_pool)
cls3_fc1_pose = Dense(2048,activation='relu',name='cls3_fc1_pose')(cls3_fc1_flat)
cls3_fc_pose_xyz = Dense(3,name='cls3_fc_pose_xyz')(cls3_fc1_pose)
cls3_fc_pose_wpqr = Dense(4,name='cls3_fc_pose_wpqr')(cls3_fc1_pose)
posenet = Model(input,[cls1_fc_pose_xyz, cls1_fc_pose_wpqr, cls2_fc_pose_xyz, cls2_fc_pose_wpqr, cls3_fc_pose_xyz, cls3_fc_pose_wpqr])
if tune:
if weights_path:
weights_data = np.load(weights_path, encoding='bytes', allow_pickle=True).item()
for layer in posenet.layers:
if layer.name in weights_data.keys():
layer_weights = weights_data[layer.name]
layer.set_weights((layer_weights[b'weights'], layer_weights[b'biases']))
print("FINISHED SETTING THE WEIGHTS!")
return posenet
def create_posenet_geo(sx_initial,sq_initial):
# creates Posenet from GoogLeNet a.k.a. Inception v1 (Szegedy, 2015)
with tf.device('/gpu:1'):
input = Input(shape=(224, 224, 3))
conv1 = Convolution2D(64, (7, 7), strides=(2, 2), padding='same', activation='relu', name='conv1')(input)
pool1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='pool1')(conv1)
norm1 = BatchNormalization(axis=3, name='norm1')(pool1)
reduction2 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='reduction2')(norm1)
conv2 = Convolution2D(192, (3, 3), padding='same', activation='relu', name='conv2')(reduction2)
norm2 = BatchNormalization(axis=3, name='norm2')(conv2)
pool2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid', name='pool2')(norm2)
icp1_reduction1 = Convolution2D(96, (1, 1), padding='same', activation='relu', name='icp1_reduction1')(pool2)
icp1_out1 = Convolution2D(128, (3, 3), padding='same', activation='relu', name='icp1_out1')(icp1_reduction1)
icp1_reduction2 = Convolution2D(16, (1, 1), padding='same', activation='relu', name='icp1_reduction2')(pool2)
icp1_out2 = Convolution2D(32, (5, 5), padding='same', activation='relu', name='icp1_out2')(icp1_reduction2)
icp1_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp1_pool')(pool2)
icp1_out3 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp1_out3')(icp1_pool)
icp1_out0 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp1_out0')(pool2)
icp2_in = concatenate([icp1_out0, icp1_out1, icp1_out2, icp1_out3], axis=3, name='icp2_in')
icp2_reduction1 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp2_reduction1')(icp2_in)
icp2_out1 = Convolution2D(192, (3, 3), padding='same', activation='relu', name='icp2_out1')(icp2_reduction1)
icp2_reduction2 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp2_reduction2')(icp2_in)
icp2_out2 = Convolution2D(96, (5, 5), padding='same', activation='relu', name='icp2_out2')(icp2_reduction2)
icp2_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp2_pool')(icp2_in)
icp2_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp2_out3')(icp2_pool)
icp2_out0 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp2_out0')(icp2_in)
icp2_out = concatenate([icp2_out0, icp2_out1, icp2_out2, icp2_out3], axis=3, name='icp2_out')
icp3_in = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='icp3_in')(icp2_out)
icp3_reduction1 = Convolution2D(96, (1, 1), padding='same', activation='relu', name='icp3_reduction1')(icp3_in)
icp3_out1 = Convolution2D(208, (3, 3), padding='same', activation='relu', name='icp3_out1')(icp3_reduction1)
icp3_reduction2 = Convolution2D(16, (1, 1), padding='same', activation='relu', name='icp3_reduction2')(icp3_in)
icp3_out2 = Convolution2D(48, (5, 5), padding='same', activation='relu', name='icp3_out2')(icp3_reduction2)
icp3_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp3_pool')(icp3_in)
icp3_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp3_out3')(icp3_pool)
icp3_out0 = Convolution2D(192, (1, 1), padding='same', activation='relu', name='icp3_out0')(icp3_in)
icp3_out = concatenate([icp3_out0, icp3_out1, icp3_out2, icp3_out3], axis=3, name='icp3_out')
cls1_pool = AveragePooling2D(pool_size=(5, 5), strides=(3, 3), padding='valid', name='cls1_pool')(icp3_out)
cls1_reduction_pose = Convolution2D(128, (1, 1), padding='same', activation='relu', name='cls1_reduction_pose')(
cls1_pool)
cls1_fc1_flat = Flatten()(cls1_reduction_pose)
cls1_fc1_pose = Dense(1024, activation='relu', name='cls1_fc1_pose')(cls1_fc1_flat)
cls1_fc_pose_xyz = Dense(3, name='cls1_fc_pose_xyz')(cls1_fc1_pose)
cls1_fc_pose_wpqr = Dense(4, name='cls1_fc_pose_wpqr')(cls1_fc1_pose)
icp4_reduction1 = Convolution2D(112, (1, 1), padding='same', activation='relu', name='icp4_reduction1')(
icp3_out)
icp4_out1 = Convolution2D(224, (3, 3), padding='same', activation='relu', name='icp4_out1')(icp4_reduction1)
icp4_reduction2 = Convolution2D(24, (1, 1), padding='same', activation='relu', name='icp4_reduction2')(icp3_out)
icp4_out2 = Convolution2D(64, (5, 5), padding='same', activation='relu', name='icp4_out2')(icp4_reduction2)
icp4_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp4_pool')(icp3_out)
icp4_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp4_out3')(icp4_pool)
icp4_out0 = Convolution2D(160, (1, 1), padding='same', activation='relu', name='icp4_out0')(icp3_out)
icp4_out = concatenate([icp4_out0, icp4_out1, icp4_out2, icp4_out3], axis=3, name='icp4_out')
icp5_reduction1 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp5_reduction1')(
icp4_out)
icp5_out1 = Convolution2D(256, (3, 3), padding='same', activation='relu', name='icp5_out1')(icp5_reduction1)
icp5_reduction2 = Convolution2D(24, (1, 1), padding='same', activation='relu', name='icp5_reduction2')(icp4_out)
icp5_out2 = Convolution2D(64, (5, 5), padding='same', activation='relu', name='icp5_out2')(icp5_reduction2)
icp5_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp5_pool')(icp4_out)
icp5_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp5_out3')(icp5_pool)
icp5_out0 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp5_out0')(icp4_out)
icp5_out = concatenate([icp5_out0, icp5_out1, icp5_out2, icp5_out3], axis=3, name='icp5_out')
icp6_reduction1 = Convolution2D(144, (1, 1), padding='same', activation='relu', name='icp6_reduction1')(
icp5_out)
icp6_out1 = Convolution2D(288, (3, 3), padding='same', activation='relu', name='icp6_out1')(icp6_reduction1)
icp6_reduction2 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp6_reduction2')(icp5_out)
icp6_out2 = Convolution2D(64, (5, 5), padding='same', activation='relu', name='icp6_out2')(icp6_reduction2)
icp6_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp6_pool')(icp5_out)
icp6_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp6_out3')(icp6_pool)
icp6_out0 = Convolution2D(112, (1, 1), padding='same', activation='relu', name='icp6_out0')(icp5_out)
icp6_out = concatenate([icp6_out0, icp6_out1, icp6_out2, icp6_out3], axis=3, name='icp6_out')
cls2_pool = AveragePooling2D(pool_size=(5, 5), strides=(3, 3), padding='valid', name='cls2_pool')(icp6_out)
cls2_reduction_pose = Convolution2D(128, (1, 1), padding='same', activation='relu', name='cls2_reduction_pose')(
cls2_pool)
cls2_fc1_flat = Flatten()(cls2_reduction_pose)
cls2_fc1 = Dense(1024, activation='relu', name='cls2_fc1')(cls2_fc1_flat)
cls2_fc_pose_xyz = Dense(3, name='cls2_fc_pose_xyz')(cls2_fc1)
cls2_fc_pose_wpqr = Dense(4, name='cls2_fc_pose_wpqr')(cls2_fc1)
icp7_reduction1 = Convolution2D(160, (1, 1), padding='same', activation='relu', name='icp7_reduction1')(
icp6_out)
icp7_out1 = Convolution2D(320, (3, 3), padding='same', activation='relu', name='icp7_out1')(icp7_reduction1)
icp7_reduction2 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp7_reduction2')(icp6_out)
icp7_out2 = Convolution2D(128, (5, 5), padding='same', activation='relu', name='icp7_out2')(icp7_reduction2)
icp7_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp7_pool')(icp6_out)
icp7_out3 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp7_out3')(icp7_pool)
icp7_out0 = Convolution2D(256, (1, 1), padding='same', activation='relu', name='icp7_out0')(icp6_out)
icp7_out = concatenate([icp7_out0, icp7_out1, icp7_out2, icp7_out3], axis=3, name='icp7_out')
icp8_in = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='icp8_in')(icp7_out)
icp8_reduction1 = Convolution2D(160, (1, 1), padding='same', activation='relu', name='icp8_reduction1')(icp8_in)
icp8_out1 = Convolution2D(320, (3, 3), padding='same', activation='relu', name='icp8_out1')(icp8_reduction1)
icp8_reduction2 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp8_reduction2')(icp8_in)
icp8_out2 = Convolution2D(128, (5, 5), padding='same', activation='relu', name='icp8_out2')(icp8_reduction2)
icp8_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp8_pool')(icp8_in)
icp8_out3 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp8_out3')(icp8_pool)
icp8_out0 = Convolution2D(256, (1, 1), padding='same', activation='relu', name='icp8_out0')(icp8_in)
icp8_out = concatenate([icp8_out0, icp8_out1, icp8_out2, icp8_out3], axis=3, name='icp8_out')
icp9_reduction1 = Convolution2D(192, (1, 1), padding='same', activation='relu', name='icp9_reduction1')(
icp8_out)
icp9_out1 = Convolution2D(384, (3, 3), padding='same', activation='relu', name='icp9_out1')(icp9_reduction1)
icp9_reduction2 = Convolution2D(48, (1, 1), padding='same', activation='relu', name='icp9_reduction2')(icp8_out)
icp9_out2 = Convolution2D(128, (5, 5), padding='same', activation='relu', name='icp9_out2')(icp9_reduction2)
icp9_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp9_pool')(icp8_out)
icp9_out3 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp9_out3')(icp9_pool)
icp9_out0 = Convolution2D(384, (1, 1), padding='same', activation='relu', name='icp9_out0')(icp8_out)
icp9_out = concatenate([icp9_out0, icp9_out1, icp9_out2, icp9_out3], axis=3, name='icp9_out')
cls3_pool = AveragePooling2D(pool_size=(7, 7), strides=(1, 1), padding='valid', name='cls3_pool')(icp9_out)
cls3_fc1_flat = Flatten()(cls3_pool)
cls3_fc1_pose = Dense(2048, activation='relu', name='cls3_fc1_pose')(cls3_fc1_flat)
cls3_fc_pose_xyz = Dense(3, name='cls3_fc_pose_xyz')(cls3_fc1_pose)
cls3_fc_pose_wpqr = Dense(4, name='cls3_fc_pose_wpqr')(cls3_fc1_pose)
y_input_x = Input(shape=(3,), name="y_input_x")
y_input_q = Input(shape=(4,), name="y_input_q")
with tf.name_scope("loss_x"):
my_loss_x = Geoloss_x(sx_initial)(y_input_x, cls3_fc_pose_xyz)
with tf.name_scope("loss_q"):
my_loss_q = Geoloss_q(sq_initial)(y_input_q, cls3_fc_pose_wpqr)
posenet = Model(inputs=[input, y_input_x, y_input_q],
outputs=[my_loss_x, my_loss_q])
return posenet
def create_posenet_max(type):
# creates Posenet from GoogLeNet a.k.a. Inception v1 (Szegedy, 2015)
with tf.device('/gpu:1'):
input = Input(shape=(224, 224, 3))
conv1 = Convolution2D(64, (7, 7), strides=(2, 2), padding='same', activation='relu', name='conv1')(input)
pool1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='pool1')(conv1)
norm1 = BatchNormalization(axis=3, name='norm1')(pool1)
reduction2 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='reduction2')(norm1)
conv2 = Convolution2D(192, (3, 3), padding='same', activation='relu', name='conv2')(reduction2)
norm2 = BatchNormalization(axis=3, name='norm2')(conv2)
pool2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid', name='pool2')(norm2)
icp1_reduction1 = Convolution2D(96, (1, 1), padding='same', activation='relu', name='icp1_reduction1')(pool2)
icp1_out1 = Convolution2D(128, (3, 3), padding='same', activation='relu', name='icp1_out1')(icp1_reduction1)
icp1_reduction2 = Convolution2D(16, (1, 1), padding='same', activation='relu', name='icp1_reduction2')(pool2)
icp1_out2 = Convolution2D(32, (5, 5), padding='same', activation='relu', name='icp1_out2')(icp1_reduction2)
icp1_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp1_pool')(pool2)
icp1_out3 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp1_out3')(icp1_pool)
icp1_out0 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp1_out0')(pool2)
icp2_in = concatenate([icp1_out0, icp1_out1, icp1_out2, icp1_out3], axis=3, name='icp2_in')
icp2_reduction1 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp2_reduction1')(icp2_in)
icp2_out1 = Convolution2D(192, (3, 3), padding='same', activation='relu', name='icp2_out1')(icp2_reduction1)
icp2_reduction2 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp2_reduction2')(icp2_in)
icp2_out2 = Convolution2D(96, (5, 5), padding='same', activation='relu', name='icp2_out2')(icp2_reduction2)
icp2_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp2_pool')(icp2_in)
icp2_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp2_out3')(icp2_pool)
icp2_out0 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp2_out0')(icp2_in)
icp2_out = concatenate([icp2_out0, icp2_out1, icp2_out2, icp2_out3], axis=3, name='icp2_out')
icp3_in = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='icp3_in')(icp2_out)
icp3_reduction1 = Convolution2D(96, (1, 1), padding='same', activation='relu', name='icp3_reduction1')(icp3_in)
icp3_out1 = Convolution2D(208, (3, 3), padding='same', activation='relu', name='icp3_out1')(icp3_reduction1)
icp3_reduction2 = Convolution2D(16, (1, 1), padding='same', activation='relu', name='icp3_reduction2')(icp3_in)
icp3_out2 = Convolution2D(48, (5, 5), padding='same', activation='relu', name='icp3_out2')(icp3_reduction2)
icp3_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp3_pool')(icp3_in)
icp3_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp3_out3')(icp3_pool)
icp3_out0 = Convolution2D(192, (1, 1), padding='same', activation='relu', name='icp3_out0')(icp3_in)
icp3_out = concatenate([icp3_out0, icp3_out1, icp3_out2, icp3_out3], axis=3, name='icp3_out')
cls1_pool = AveragePooling2D(pool_size=(5, 5), strides=(3, 3), padding='valid', name='cls1_pool')(icp3_out)
cls1_reduction_pose = Convolution2D(128, (1, 1), padding='same', activation='relu', name='cls1_reduction_pose')(
cls1_pool)
cls1_fc1_flat = Flatten()(cls1_reduction_pose)
cls1_fc1_pose = Dense(1024, activation='relu', name='cls1_fc1_pose')(cls1_fc1_flat)
cls1_fc_pose_xyz = Dense(3, name='cls1_fc_pose_xyz')(cls1_fc1_pose)
cls1_fc_pose_wpqr = Dense(4, name='cls1_fc_pose_wpqr')(cls1_fc1_pose)
icp4_reduction1 = Convolution2D(112, (1, 1), padding='same', activation='relu', name='icp4_reduction1')(
icp3_out)
icp4_out1 = Convolution2D(224, (3, 3), padding='same', activation='relu', name='icp4_out1')(icp4_reduction1)
icp4_reduction2 = Convolution2D(24, (1, 1), padding='same', activation='relu', name='icp4_reduction2')(icp3_out)
icp4_out2 = Convolution2D(64, (5, 5), padding='same', activation='relu', name='icp4_out2')(icp4_reduction2)
icp4_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp4_pool')(icp3_out)
icp4_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp4_out3')(icp4_pool)
icp4_out0 = Convolution2D(160, (1, 1), padding='same', activation='relu', name='icp4_out0')(icp3_out)
icp4_out = concatenate([icp4_out0, icp4_out1, icp4_out2, icp4_out3], axis=3, name='icp4_out')
icp5_reduction1 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp5_reduction1')(
icp4_out)
icp5_out1 = Convolution2D(256, (3, 3), padding='same', activation='relu', name='icp5_out1')(icp5_reduction1)
icp5_reduction2 = Convolution2D(24, (1, 1), padding='same', activation='relu', name='icp5_reduction2')(icp4_out)
icp5_out2 = Convolution2D(64, (5, 5), padding='same', activation='relu', name='icp5_out2')(icp5_reduction2)
icp5_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp5_pool')(icp4_out)
icp5_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp5_out3')(icp5_pool)
icp5_out0 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp5_out0')(icp4_out)
icp5_out = concatenate([icp5_out0, icp5_out1, icp5_out2, icp5_out3], axis=3, name='icp5_out')
icp6_reduction1 = Convolution2D(144, (1, 1), padding='same', activation='relu', name='icp6_reduction1')(
icp5_out)
icp6_out1 = Convolution2D(288, (3, 3), padding='same', activation='relu', name='icp6_out1')(icp6_reduction1)
icp6_reduction2 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp6_reduction2')(icp5_out)
icp6_out2 = Convolution2D(64, (5, 5), padding='same', activation='relu', name='icp6_out2')(icp6_reduction2)
icp6_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp6_pool')(icp5_out)
icp6_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp6_out3')(icp6_pool)
icp6_out0 = Convolution2D(112, (1, 1), padding='same', activation='relu', name='icp6_out0')(icp5_out)
icp6_out = concatenate([icp6_out0, icp6_out1, icp6_out2, icp6_out3], axis=3, name='icp6_out')
cls2_pool = AveragePooling2D(pool_size=(5, 5), strides=(3, 3), padding='valid', name='cls2_pool')(icp6_out)
cls2_reduction_pose = Convolution2D(128, (1, 1), padding='same', activation='relu', name='cls2_reduction_pose')(
cls2_pool)
cls2_fc1_flat = Flatten()(cls2_reduction_pose)
cls2_fc1 = Dense(1024, activation='relu', name='cls2_fc1')(cls2_fc1_flat)
cls2_fc_pose_xyz = Dense(3, name='cls2_fc_pose_xyz')(cls2_fc1)
cls2_fc_pose_wpqr = Dense(4, name='cls2_fc_pose_wpqr')(cls2_fc1)
icp7_reduction1 = Convolution2D(160, (1, 1), padding='same', activation='relu', name='icp7_reduction1')(
icp6_out)
icp7_out1 = Convolution2D(320, (3, 3), padding='same', activation='relu', name='icp7_out1')(icp7_reduction1)
icp7_reduction2 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp7_reduction2')(icp6_out)
icp7_out2 = Convolution2D(128, (5, 5), padding='same', activation='relu', name='icp7_out2')(icp7_reduction2)
icp7_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp7_pool')(icp6_out)
icp7_out3 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp7_out3')(icp7_pool)
icp7_out0 = Convolution2D(256, (1, 1), padding='same', activation='relu', name='icp7_out0')(icp6_out)
icp7_out = concatenate([icp7_out0, icp7_out1, icp7_out2, icp7_out3], axis=3, name='icp7_out')
icp8_in = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='icp8_in')(icp7_out)
icp8_reduction1 = Convolution2D(160, (1, 1), padding='same', activation='relu', name='icp8_reduction1')(icp8_in)
icp8_out1 = Convolution2D(320, (3, 3), padding='same', activation='relu', name='icp8_out1')(icp8_reduction1)
icp8_reduction2 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp8_reduction2')(icp8_in)
icp8_out2 = Convolution2D(128, (5, 5), padding='same', activation='relu', name='icp8_out2')(icp8_reduction2)
icp8_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp8_pool')(icp8_in)
icp8_out3 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp8_out3')(icp8_pool)
icp8_out0 = Convolution2D(256, (1, 1), padding='same', activation='relu', name='icp8_out0')(icp8_in)
icp8_out = concatenate([icp8_out0, icp8_out1, icp8_out2, icp8_out3], axis=3, name='icp8_out')
icp9_reduction1 = Convolution2D(192, (1, 1), padding='same', activation='relu', name='icp9_reduction1')(
icp8_out)
icp9_out1 = Convolution2D(384, (3, 3), padding='same', activation='relu', name='icp9_out1')(icp9_reduction1)
icp9_reduction2 = Convolution2D(48, (1, 1), padding='same', activation='relu', name='icp9_reduction2')(icp8_out)
icp9_out2 = Convolution2D(128, (5, 5), padding='same', activation='relu', name='icp9_out2')(icp9_reduction2)
icp9_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp9_pool')(icp8_out)
icp9_out3 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp9_out3')(icp9_pool)
icp9_out0 = Convolution2D(384, (1, 1), padding='same', activation='relu', name='icp9_out0')(icp8_out)
icp9_out = concatenate([icp9_out0, icp9_out1, icp9_out2, icp9_out3], axis=3, name='icp9_out')
cls3_pool = AveragePooling2D(pool_size=(7, 7), strides=(1, 1), padding='valid', name='cls3_pool')(icp9_out)
cls3_fc1_flat = Flatten()(cls3_pool)
cls3_fc1_pose = Dense(2048, activation='relu', name='cls3_fc1_pose')(cls3_fc1_flat)
cls3_fc_pose_xyz = Dense(3, name='cls3_fc_pose_xyz')(cls3_fc1_pose)
cls3_fc_pose_wpqr = Dense(4, name='cls3_fc_pose_wpqr')(cls3_fc1_pose)
y_input_x = Input(shape=(3,), name="y_input_x")
y_input_q = Input(shape=(4,), name="y_input_q")
with tf.name_scope("loss_x"):
if type=="max":
my_loss_x,my_loss_q = max_loss()(y_input_x, cls3_fc_pose_xyz,y_input_q,cls3_fc_pose_wpqr)
elif type=="combined":
my_loss_x, my_loss_q = combined_loss()(y_input_x, cls3_fc_pose_xyz, y_input_q, cls3_fc_pose_wpqr)
posenet = Model(inputs=[input, y_input_x, y_input_q],
outputs=[my_loss_x, my_loss_q])
return posenet
def create_poselstm():
# creates Posenet from GoogLeNet a.k.a. Inception v1 (Szegedy, 2015)
if True:
input = Input(shape=(224, 224, 3))
conv1 = Convolution2D(64, (7, 7), strides=(2, 2), padding='same', activation='relu', name='conv1')(input)
pool1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='pool1')(conv1)
norm1 = BatchNormalization(axis=3, name='norm1')(pool1)
reduction2 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='reduction2')(norm1)
conv2 = Convolution2D(192, (3, 3), padding='same', activation='relu', name='conv2')(reduction2)
norm2 = BatchNormalization(axis=3, name='norm2')(conv2)
pool2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='valid', name='pool2')(norm2)
icp1_reduction1 = Convolution2D(96, (1, 1), padding='same', activation='relu', name='icp1_reduction1')(pool2)
icp1_out1 = Convolution2D(128, (3, 3), padding='same', activation='relu', name='icp1_out1')(icp1_reduction1)
icp1_reduction2 = Convolution2D(16, (1, 1), padding='same', activation='relu', name='icp1_reduction2')(pool2)
icp1_out2 = Convolution2D(32, (5, 5), padding='same', activation='relu', name='icp1_out2')(icp1_reduction2)
icp1_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp1_pool')(pool2)
icp1_out3 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp1_out3')(icp1_pool)
icp1_out0 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp1_out0')(pool2)
icp2_in = concatenate([icp1_out0, icp1_out1, icp1_out2, icp1_out3], axis=3, name='icp2_in')
icp2_reduction1 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp2_reduction1')(icp2_in)
icp2_out1 = Convolution2D(192, (3, 3), padding='same', activation='relu', name='icp2_out1')(icp2_reduction1)
icp2_reduction2 = Convolution2D(32, (1, 1), padding='same', activation='relu', name='icp2_reduction2')(icp2_in)
icp2_out2 = Convolution2D(96, (5, 5), padding='same', activation='relu', name='icp2_out2')(icp2_reduction2)
icp2_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp2_pool')(icp2_in)
icp2_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp2_out3')(icp2_pool)
icp2_out0 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp2_out0')(icp2_in)
icp2_out = concatenate([icp2_out0, icp2_out1, icp2_out2, icp2_out3], axis=3, name='icp2_out')
icp3_in = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='icp3_in')(icp2_out)
icp3_reduction1 = Convolution2D(96, (1, 1), padding='same', activation='relu', name='icp3_reduction1')(icp3_in)
icp3_out1 = Convolution2D(208, (3, 3), padding='same', activation='relu', name='icp3_out1')(icp3_reduction1)
icp3_reduction2 = Convolution2D(16, (1, 1), padding='same', activation='relu', name='icp3_reduction2')(icp3_in)
icp3_out2 = Convolution2D(48, (5, 5), padding='same', activation='relu', name='icp3_out2')(icp3_reduction2)
icp3_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp3_pool')(icp3_in)
icp3_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp3_out3')(icp3_pool)
icp3_out0 = Convolution2D(192, (1, 1), padding='same', activation='relu', name='icp3_out0')(icp3_in)
icp3_out = concatenate([icp3_out0, icp3_out1, icp3_out2, icp3_out3], axis=3, name='icp3_out')
icp4_reduction1 = Convolution2D(112, (1, 1), padding='same', activation='relu', name='icp4_reduction1')(
icp3_out)
icp4_out1 = Convolution2D(224, (3, 3), padding='same', activation='relu', name='icp4_out1')(icp4_reduction1)
icp4_reduction2 = Convolution2D(24, (1, 1), padding='same', activation='relu', name='icp4_reduction2')(icp3_out)
icp4_out2 = Convolution2D(64, (5, 5), padding='same', activation='relu', name='icp4_out2')(icp4_reduction2)
icp4_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp4_pool')(icp3_out)
icp4_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp4_out3')(icp4_pool)
icp4_out0 = Convolution2D(160, (1, 1), padding='same', activation='relu', name='icp4_out0')(icp3_out)
icp4_out = concatenate([icp4_out0, icp4_out1, icp4_out2, icp4_out3], axis=3, name='icp4_out')
icp5_reduction1 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp5_reduction1')(
icp4_out)
icp5_out1 = Convolution2D(256, (3, 3), padding='same', activation='relu', name='icp5_out1')(icp5_reduction1)
icp5_reduction2 = Convolution2D(24, (1, 1), padding='same', activation='relu', name='icp5_reduction2')(icp4_out)
icp5_out2 = Convolution2D(64, (5, 5), padding='same', activation='relu', name='icp5_out2')(icp5_reduction2)
icp5_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='icp5_pool')(icp4_out)
icp5_out3 = Convolution2D(64, (1, 1), padding='same', activation='relu', name='icp5_out3')(icp5_pool)
icp5_out0 = Convolution2D(128, (1, 1), padding='same', activation='relu', name='icp5_out0')(icp4_out)