how to apply multi input image to Unet for segmentation? - tensorflow

Question

I am a newbie in machine learning.
Actually, I used my unet code for image segmentation using one input image slice (192x912) and one output mask image (192x192)
My Unet code is contained several CNN layer and I usually used one input image (192x912) and one its corresponding mask binary image for training.
Code related with above explanation is as below.
def get_unet():
inputs = Input((img_rows, img_cols, 1))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
drop1 = Dropout(0.2)(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(drop1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
drop2 = Dropout(0.2)(pool2)
'''''''
return model
model.fit(imgs_train, imgs_mask_train, batch_size=32, epochs=100, verbose=2, shuffle=True, validation_split=0.1, callbacks=[model_checkpoint])
it works well. But, now, I would like to use multi input image when I train my network. So, I add another train data and edit my code like below.
def get_unet():
inputs = Input((img_rows, img_cols, 1))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
drop1 = Dropout(0.2)(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(drop1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
drop2 = Dropout(0.2)(pool2)
'''''''
return model
model.fit([imgs_train, imgs_other_train], imgs_mask_train, batch_size=32, epochs=100, verbose=2, shuffle=True, validation_split=0.1, callbacks=[model_checkpoint])
but when I run the train code, I got error message as below.
"ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 array(s), but instead got the following list of 2 arrays: "
I think my U net needs to be changed for multi input, but I don't know where I have to change.
Please help and give me any comments.
Thanks.

Answer 1

This is actually rather easy. All you need to do would adjust your input size I believe.
inputs = Input((img_rows, img_cols, size)) would have worked. Or you could have used concat like:
inputs = []
for _ in range(num_inputs):
inputs.append(Input((self.input_height, self.input_width, self.input_features)))
x = concatenate(inputs)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
You can check something similar I implemented here