Load optimizer from checkpoint (BREAKING - see details) (#182)

* Fixes issues #70, #145 and replaces PR #74
* checkpoint.py
  * save_checkpoint will now save the optimizer type in addition to
    its state
  * load_checkpoint will now instantiate an optimizer based on the
    saved type and load its state
* config.py: file/dict_config now accept the resumed epoch to pass to
  LR schedulers
* policy.py: LRPolicy now passes the current epoch to the LR scheduler
* Classifier compression sample
  * New flag '--resume-from' for properly resuming a saved training
    session, inc. optimizer state and epoch #
  * Flag '--reset-optimizer' added to allow discarding of a loaded
    optimizer.
  * BREAKING:
    * Previous flag '--resume' is deprecated and is mapped to
      '--resume-from' + '--reset-optimizer'. 
    * But, old resuming behavior had an inconsistency where the epoch
      count would continue from the saved epoch, but the LR scheduler
      was setup as if we were starting from epoch 0.
    * Using '--resume-from' + '--reset-optimizer' now will simply
      RESET the epoch count to 0 for the whole environment.
    * This means that scheduling configurations (in YAML or code)
      which assumed use of '--resume' might need to be changed to
      reflect the fact that the epoch count now starts from 0
    * All relevant YAML files under 'examples' modified to reflect
      this change
* Initial support for ReduceLROnPlateau (#161):
  * Allow passing **kwargs to policies via the scheduler
  * Image classification now passes the validation loss to the
    scheduler, to be used yo ReduceLROnPlateau
  * The current implementation is experimental and subject to change