Added performance flags for better inference performance on Xeon

[vivek-rane]

I'm a part of the TensorFlow optimization team at Intel and we're working on improving performance of YoloV2/darkflow on Xeon. As a first step, I'm adding the ability to measure and tweak performance. We will also contribute changes to TensorFlow and MKL-DNN to speed up YoloV2/DarkFlow.

• Added flags for getting better performance during inference when running on TensorFlow built with MKL-DNN
• Added a script to generate reasonable defaults for these flags depending on the system being used
• Added ability to generate timeline to find performance bottlenecks

With the right settings, inference performance on a 28 core Xeon Scalable Processor goes up by more than 60%

[vivek-rane]

Hang on - I tested this with python 2.7�. Let me update the code for 3.5.
Edit: done

[vivek-rane]

Could someone help me with the failure in the CI? If I change the offset in darkflow/utils/loader.py from 16 to 20, it works. Not sure why.