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.

Also added tests

Not backward compatible - re-installation is required

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

This is a merge of the ADC branch and master.
ADC (using a DDPG RL agent to compress image classifiers) is still WiP and requires
An unreleased version of Coach (https://github.com/NervanaSystems/coach).

Small features in this commit:
-Added model_find_module() - find module object given its name
- Add channel ranking and pruning: pruning/ranked_structures_pruner.py
- Add a CIFAR10 VGG16 model: models/cifar10/vgg_cifar.py
- Thinning: change the level of some log messages – some of the messages were
moved to ‘debug’ level because they are not usually interesting.
- Add a function to print nicely formatted integers - distiller/utils.py
- Sensitivity analysis for channels-removal
- compress_classifier.py – handle keyboard interrupts
- compress_classifier.py – fix re-raise of exceptions, so they maintain call-stack

-Added tests:
-- test_summarygraph.py: test_simplenet() - Added a regression test to target a bug that occurs when taking the predecessor of the first node in a graph
-- test_ranking.py - test_ch_ranking, test_ranked_channel_pruning
-- test_model_summary.py - test_png_generation, test_summary (sparsity/ compute/model/modules)

- Bug fixes in this commit:
-- Thinning bug fix: handle zero-sized 'indices' tensor
During the thinning process, the 'indices' tensor can become zero-sized,
and will have an undefiend length. Therefore, we need to check for this
situation when assessing the number of elements in 'indices'
-- Language model: adjust main.py to new distiller.model_summary API