Added support for saving the randomly initialized network before
starting training; and added an implmentation showing how to extract
a (winning) lottery ticket from the prestine network, and the
pruned network.

*An implementation of AMC (the previous implementation
 code has moved to a new location under 
/distiller/examples/auto_compression/amc.  AMC is aligned
with the ‘master’ branch of Coach.
*compress_classifier.py is refactored.  The base code moved
to /distiller/apputils/image_classifier.py.  Further refactoring
will follow.
We want to provide a simple and small API to the basic features of
a classifier-compression application.
This will help applications that want to use the make features of a
classifier-compression application, without the standard training
regiment.
AMC is one example of a stand-alone application that needs to leverage
the capabilities of a classifier-compression application, but is currently
coupled to `compress_classifier.py`.
`multi-finetune.py` is another example.
* ranked_structures_pruner.py:
** Added support for grouping channels/filters
Sometimes we want to prune a group of structures: e.g. groups of
8-channels.  This feature does not force the groups to be adjacent,
so it is more like a set of structures.  E.g. in the case of pruning
channels from a 64-channels convolution, grouped by 8 channels, we 
will prune exactly one of 0/8/16/24/32/40/48/56 channels.  I.e. 
always a multiple of 8-channels, excluding the set of all 64 channels.
** Added FMReconstructionChannelPruner – this is channel
pruning using L1-magnitude to rank and select channels to
remove, and feature-map reconstruction to improve the
resilience to the pruning.
* Added a script to run multiple instances of an 
experiment, in different processes:
 examples/classifier_compression/multi-run.py
* Set the seed value even when not specified by the command-line
arguments, so that we can try and recreate the session.
* Added pruning ranking noise -
Ranking noise introduces Gaussian noise when ranking channels/filters
using Lp-norm.  The noise is introduced using the epsilon-greedy
methodology, where ranking using exact Lp-norm is considered greedy.
* Added configurable rounding of pruning level: choose whether to 
Round up/down when rounding the number of structures to prune 
(rounding is always to an integer).  

Added set_seed() to Distiller and added support for seeding the PRNG when setting --deterministic mode (prior to this change, the seed is always set to zero when running in deterministic mode.
The PRNGs of Pytorch (CPU & Cuda devices), numpy and Python are set.
Added support for ```--seed``` to classifier_compression.py.

Introduced a new utility function to export image-classifiers
to ONNX: export_img_classifier_to_onnx.
The functionality is not new, just refactored.

In the sample application compress_classifier.py added 
--export-onnx as a stand-alone cmd-line flag for specifically exporting 
ONNX models.
This new flag can take an optional argument which is used to name the
exported onnx model file.
The option to export models was removed from the –summary argument.
Now we allow multiple --summary options be called together.

Added a basic test for exporting ONNX.

Added a collector for activation histograms (sub-class of
ActivationStatsCollector). It is stats-based, meaning it requires
pre-computed min/max stats per tensor. This is done in order to prevent
the need to save all of the activation tensors throughout the run.
The stats are expected in the format generated by
QuantCalibrationStatsCollector.

Details:

* Implemented ActivationHistogramsCollector
* Added Jupyter notebook showcasing activation histograms
* Implemented helper function that performs the stats collection pass
  and histograms pass in one go
* Also added separate helper function just for quantization stats
  collection
* Integrated in image classification sample
* data_loaders.py: Added option to have a fixed subset throughout
  within the same session. Using it to keep the same subset between
  the stats collection and histograms collection phases.
* Other changes:
  * Calling assign_layer_fq_names in base-class of collectors. We do
    this since the collectors, as implemented so far, assume this is
    done. So makes sense to just do it in the base class instead of
    expecting the user to do it.
  * Enforcing a non-parallel model for quantization stats and
    histograms collectors
  * Jupyter notebooks - add utility function to enable loggers in
    notebooks. This allows us to see any logging done by Distiller
    APIs called from notebooks.

Also:
* Single worker limitation not needed anymore, been fixed in PyTorch
  since v0.4.0 (https://github.com/pytorch/pytorch/pull/4640)
* compress_classifier.py: If run in evaluation mode (--eval), enable
  deterministic mode.
* Call utils.set_deterministic at data loaders creation if
  deterministic argument is set (don't assume user calls it outside)
* Disable CUDNN benchmark mode in utils.set_deterministic
  (https://pytorch.org/docs/stable/notes/randomness.html#cudnn)

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

Not backward compatible - re-installation is required

* Fixes for PyTorch==1.0.0
* Refactoring folder structure
* Update installation section in docs

Merging the 'amc' branch with 'master'.
This updates the automated compression code in 'master', and adds a greedy filter-pruning algorithm.

Summary of changes:
(1) Post-train quantization based on pre-collected statistics
(2) Quantized concat, element-wise addition / multiplication and embeddings
(3) Move post-train quantization command line args out of sample code
(4) Configure post-train quantization from YAML for more fine-grained control

(See PR #136 for more detailed changes descriptions)

* For CIFAR-10 / ImageNet only
* Refactor data_loaders.py, reduce code duplication
* Implemented custom sampler
* Integrated in image classification sample
* Since we now shuffle the test set, had to update expected results
  in 2 full_flow_tests that do evaluation

* Support for multi-phase activations logging

Enable logging activation both durning training and validation at
the same session.

* Refactoring: Move parser to its own file

* Parser is moved from compress_classifier into its own file.
* Torch version check is moved to precede main() call.
* Move main definition to the top of the file.
* Modify parser choices to case-insensitive