Doing inference on provided model

[mirceamt]

I read the paper, downloaded the model and I have some questions:
(I am talking about the "motionSegmenter_fullModel.t7")

1. How do you provide data for inference? I understand that there is the 'trunk' which is the modified AlexNet and then there are different heads. I managed to feed it an image and then to feed the maskBranch and scoreBranch with the output from the trunk. I could figure out that only the maskBranch and scoreBranch are used, by following the execution flow which leads me to the next question:
2. How can I make the model use the colorBranch? And what is the flowBranch used for? It seems that the model in that file just has the scoreBranch in it.
3. How to interpret the numbers that the scoreBranch and maskBranch compute? I could see that maskBranch outputs a feature map with 3136 channels, but what should it be used for?
4. I had to modify the line with "model:float()" from load_motionmodel.lua to "model = model:float()" and did the same for cuda as well as the float and cuda functions in DeepMask.