def get_segm_depth_model(input, output_segm, output_depth):
img_input = input
o_shape = Model(img_input, output_segm).output_shape
i_shape = Model(img_input, output_segm).input_shape
n_classes = 0
output_height = 0
output_width = 0
input_height = 0
input_width = 0
if IMAGE_ORDERING == 'channels_first':
output_height = o_shape[2]
output_width = o_shape[3]
input_height = i_shape[2]
input_width = i_shape[3]
output_segm = (Reshape((-1, output_height * output_width)))(output_segm)
output_segm = (Permute((2, 1)))(output_segm)
n_classes = o_shape[1]
elif IMAGE_ORDERING == 'channels_last':
output_height = o_shape[1]
output_width = o_shape[2]
input_height = i_shape[1]
input_width = i_shape[2]
n_classes = o_shape[3]
output_segm = (Reshape((output_height * output_width, -1)))(output_segm)
output_segm = (Activation('softmax', name="segm_pred"))(output_segm)
output_depth = (Activation('sigmoid', name="depth_pred"))(output_depth)
model = Model(inputs=img_input, outputs=[output_segm, output_depth])
model.n_classes = n_classes
model.input_height = input_height
model.input_width = input_width
model.output_height = output_height
model.output_width = output_width
model.train = MethodType(train, model)
model.predict_segmentation = MethodType(predict, model)
#model.predict_multiple = MethodType(predict_multiple, model)
#model.evaluate_segmentation = MethodType(evaluate, model)
return model
