* Supported any feature map size for average pool.
* Different models may have different feature map size.

* Used registry to register keypoint and mask heads.

* Passing in/out channels between modules when creating the model.

Passing in/out channels between modules when creating the model. This simplifies the code to compute the input channels for feature extractors and makes the predictors independent of the backbone architectures.
* Passed in_channels to rpn and head builders.
* Set out_channels to model modules including backbone and feature extractors.
* Moved cfg.MODEL.BACKBONE.OUT_CHANNELS to cfg.MODEL.RESNETS.BACKBONE_OUT_CHANNELS as it is not used by all architectures. Updated config files accordingly.

For new architecture modules, the return module needs to contain a field called 'out_channels' to indicate the output channel size.

* Added unit test for box_coder and nms.

* Added FBNet architecture.

* FBNet is a general architecture definition to support efficient architecture search and MaskRCNN2GO.
* Included various efficient building blocks (inverted residual, shuffle, separate dw conv, dw upsampling etc.)
* Supported building backbone, rpn, detection, keypoint and mask heads using efficient building blocks.
* Architecture could be defined in `fbnet_modeldef.py` or in `cfg.MODEL.FBNET.ARCH_DEF` directly.
* A few baseline architectures are included.

* Added various unit tests.

* build and run backbones.
* build and run feature extractors.
* build and run predictors.

* Added a unit test to verify all config files are loadable.