* factor out data related constants to own file
* move data related config helpers to own file
* add a variant of RandomResizeCrop that randomizes interpolation method
* remove old Numpy version of RandomErasing
* cleanup torch version of RandomErasing and use it in either GPU loader batch mode or single image cpu Transform

* initial impl w/ label smoothing converging, but needs more testing

Morph mnasnet impl into a generic mobilenet that covers Mnasnet, MobileNetV1/V2, ChamNet, FBNet, and related
* add an alternate RMSprop opt that applies eps like TF
* add bn params for passing through alternates and changing defaults to TF style

* create one resolve fn to pull together model defaults + cmd line args
* update attribution comments in some models
* test update train/validation/inference scripts

* All models have 'default_cfgs' dict
* load/resume/pretrained helpers factored out
* pretrained load operates on state_dict based on default_cfg
* test all models in validate
* schedule, optim factor factored out
* test time pool wrapper applied based on default_cfg

Cleanup CheckpointSaver, add support for increasing or decreasing metric, switch to prec1 metric in train loop

Improve creation of data pipeline with prefetch enabled vs disabled, fixup inception_res_v2 and dpn models

Add some Nvidia performance enhancements (prefetch loader, fast collate), and refactor some of training and model fact/transforms