--- a
+++ b/modelQW.py
@@ -0,0 +1,154 @@
+import numpy as np
+import os
+import skimage.io as io
+import skimage.transform as trans
+import numpy as np
+from keras.models import *
+from keras.layers import *
+from keras.optimizers import *
+from keras.callbacks import ModelCheckpoint, LearningRateScheduler
+from keras import backend as keras
+import tensorflow as tf
+from keras import initializers
+from keras import regularizers
+
+
+def colearning(pretrained_weights=None, input_size=(256, 256, 3)):
+    inputs = Input(input_size)
+    paddings = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0], [0, 0]])  # only pads dim 2 and 3 (h and w)
+
+    [ inputtemp, inputspet,inputsct] = Lambda(tf.split, arguments={'axis': 3, 'num_or_size_splits': 3})(inputs)
+
+    conv1ct = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputsct)
+    conv1ct = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1ct)
+    pool1ct = MaxPooling2D(pool_size=(2, 2))(conv1ct)
+    conv2ct = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1ct)
+    conv2ct = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2ct)
+    pool2ct = MaxPooling2D(pool_size=(2, 2))(conv2ct)
+    conv3ct = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2ct)
+    conv3ct = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3ct)
+    pool3ct = MaxPooling2D(pool_size=(2, 2))(conv3ct)
+    conv4ct = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3ct)
+    conv4ct = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4ct)
+    drop4ct = Dropout(0.5)(conv4ct)
+    pool4ct = MaxPooling2D(pool_size=(2, 2))(conv4ct)
+
+    conv1pet = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputspet)
+    conv1pet = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1pet)
+    pool1pet = MaxPooling2D(pool_size=(2, 2))(conv1pet)
+    conv2pet = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1pet)
+    conv2pet = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2pet)
+    pool2pet = MaxPooling2D(pool_size=(2, 2))(conv2pet)
+    conv3pet = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2pet)
+    conv3pet = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3pet)
+    pool3pet = MaxPooling2D(pool_size=(2, 2))(conv3pet)
+    conv4pet = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3pet)
+    conv4pet = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4pet)
+    drop4pet = Dropout(0.5)(conv4pet)
+    pool4pet = MaxPooling2D(pool_size=(2, 2))(conv4pet)
+
+
+    comerge1_temp = concatenate([pool1ct, pool1pet], axis=3)
+    poolctexp_temp = Lambda(expand_dim_backend, arguments={'dim': (4)})(pool1ct)
+    poolpetexp_temp = Lambda(expand_dim_backend, arguments={'dim': (4)})(pool1pet)
+
+    comerge2_temp = concatenate([poolctexp_temp, poolpetexp_temp], axis=4)
+    input_mm = Lambda(tf.pad, arguments={'paddings': (paddings), 'mode': ("CONSTANT")})(comerge2_temp)
+    input_mm = Lambda(tf.transpose, arguments={'perm': ([0, 4, 1, 2, 3])})(input_mm)
+    comerge2con_temp = Conv3D(filters=128, kernel_size=[2,3,3],
+                         kernel_initializer=initializers.VarianceScaling(scale=1.0, mode='fan_in', distribution='normal', seed=None),
+                         kernel_regularizer=regularizers.l2(0.1),
+                         bias_initializer='zeros', padding='valid', activation='relu')(input_mm)
+    colearn_out_temp = Lambda(tf.squeeze, arguments={'squeeze_dims': (1)})(comerge2con_temp)
+    conj1 = Lambda(tf.multiply, arguments={'y': (comerge1_temp)})(colearn_out_temp)
+
+    comerge1_temp = concatenate([pool2ct, pool2pet], axis=3)
+    poolctexp_temp = Lambda(expand_dim_backend, arguments={'dim': (4)})(pool2ct)
+    poolpetexp_temp = Lambda(expand_dim_backend, arguments={'dim': (4)})(pool2pet)
+
+    comerge2_temp = concatenate([poolctexp_temp, poolpetexp_temp], axis=4)
+    input_mm = Lambda(tf.pad, arguments={'paddings': (paddings), 'mode': ("CONSTANT")})(comerge2_temp)
+    input_mm = Lambda(tf.transpose, arguments={'perm': ([0, 4, 1, 2, 3])})(input_mm)
+
+    comerge2con_temp = Conv3D(filters=128, kernel_size=[2,3,3],
+                         kernel_initializer=initializers.VarianceScaling(scale=1.0, mode='fan_in', distribution='normal', seed=None),
+                         kernel_regularizer=regularizers.l2(0.1),
+                         bias_initializer='zeros', padding='valid', activation='relu')(input_mm)
+    colearn_out_temp = Lambda(tf.squeeze, arguments={'squeeze_dims': (1)})(comerge2con_temp)
+    conj2 = Lambda(tf.multiply, arguments={'y': (comerge1_temp)})(colearn_out_temp)
+
+
+    comerge1_temp = concatenate([pool3ct, pool3pet], axis=3)
+    poolctexp_temp = Lambda(expand_dim_backend, arguments={'dim': (4)})(pool3ct)
+    poolpetexp_temp = Lambda(expand_dim_backend, arguments={'dim': (4)})(pool3pet)
+
+    comerge2_temp = concatenate([poolctexp_temp, poolpetexp_temp], axis=4)
+
+    input_mm = Lambda(tf.pad, arguments={'paddings': (paddings), 'mode': ("CONSTANT")})(comerge2_temp)
+    input_mm = Lambda(tf.transpose, arguments={'perm': ([0, 4, 1, 2, 3])})(input_mm)
+    comerge2con_temp = Conv3D(filters=128, kernel_size=[2,3,3],
+                         kernel_initializer=initializers.VarianceScaling(scale=1.0, mode='fan_in', distribution='normal', seed=None),
+                         kernel_regularizer=regularizers.l2(0.1),
+                         bias_initializer='zeros', padding='valid', activation='relu')(input_mm)
+    colearn_out_temp = Lambda(tf.squeeze, arguments={'squeeze_dims': (1)})(comerge2con_temp)
+    conj3 = Lambda(tf.multiply, arguments={'y': (comerge1_temp)})(colearn_out_temp)
+
+
+    comerge1_temp = concatenate([pool4ct, pool4pet], axis=3)
+    poolctexp_temp = Lambda(expand_dim_backend, arguments={'dim': (4)})(pool4ct)
+    poolpetexp_temp = Lambda(expand_dim_backend, arguments={'dim': (4)})(pool4pet)
+
+    comerge2_temp = concatenate([poolctexp_temp, poolpetexp_temp], axis=4)
+    input_mm = Lambda(tf.pad, arguments={'paddings': (paddings), 'mode': ("CONSTANT")})(comerge2_temp)
+    input_mm = Lambda(tf.transpose, arguments={'perm': ([0, 4, 1, 2, 3])})(input_mm)
+
+    comerge2con_temp = Conv3D(filters=128, kernel_size=[2,3,3],
+                         kernel_initializer=initializers.VarianceScaling(scale=1.0, mode='fan_in', distribution='normal', seed=None),
+                         kernel_regularizer=regularizers.l2(0.1),
+                         bias_initializer='zeros', padding='valid', activation='relu')(input_mm)
+    colearn_out_temp = Lambda(tf.squeeze, arguments={'squeeze_dims': (1)})(comerge2con_temp)
+    conj4 = Lambda(tf.multiply, arguments={'y': (comerge1_temp)})(colearn_out_temp)
+
+    up5 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
+        UpSampling2D(size=(2, 2))(conj4))
+    conv5 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up5)
+    conv5 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
+    merge5 = concatenate([conj3, conv5], axis=3)
+
+    up6 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
+        UpSampling2D(size=(2, 2))(merge5))
+    conv6 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up6)
+    conv6 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
+    merge6 = concatenate([conj2, conv6], axis=3)
+
+    up7 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
+        UpSampling2D(size=(2, 2))(merge6))
+
+    conv7 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up7)
+    conv7 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
+    merge7 = concatenate([conj1, conv7], axis=3)
+
+    up8 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
+        UpSampling2D(size=(2, 2))(merge7))
+
+    conv8 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up8)
+    conv8 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
+
+    conv9 = Conv2D(4, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
+    conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
+
+
+    model = Model(input=inputs, output=conv10)
+
+    model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
+
+
+
+    if (pretrained_weights):
+        model.load_weights(pretrained_weights)
+
+    return model
+
+def expand_dim_backend(x,dim):
+    xe = K.expand_dims(x, dim)
+    return xe