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[NiftyNet] Improve model freezing and deployment for TensorFlow Serving

The user is unable to freeze NiftyNet trained models using 'freeze_graph.py' and finds it difficult to identify input/output tensor names for deployment with TensorFlow Serving. Request is for easier methods to freeze and deploy models for inference outside the NiftyNet framework.

Original Source

@@yuanpeng5 | 1 pts | 2/27/2019

I am trying to deploy niftynet trained model in tensorflow serving. But I have encountered the following problems: (1) I cannot freeze the model with freeze_graph.py provided in tensorflow. (2) It is really hard to find the input tensors name and output tensors name in the graph. Is there anyway I can easily freeze niftynet trained model and deploy it? Right now I can only evaluate the trained model under niftynet framework, it is hard to inference it in other places. I have also attached my model files in the issue. If anyone can tell me how can I find the correct input and output tensors and deploy the model, it will be very much appreciated. Thanks in advance. [new_malig.zip](https://github.com/NifTK/NiftyNet/files/2908184/new_malig.zip)

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