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.

Thanks to @haim-barad for notifying us about this.

This short notebook performs a forward-pass on a single ImageNet image,
and saves the intermediate results to a file for later inspection

A notebook to visualize the data dependencies in non-trivial networks
such as ResNet.
This is meant to help in planning filter/channel pruning of such networks.

This notebook provides visualizations of ResNet50 models pay attention
to when they classify. In other words, what are the models looking at.
This information can be used for localization, but here it is provided
merely to build our intuition.

Based on:
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Learning
Deep Features for Discriminative Localization. CVPR'16
(arXiv:1512.04150, 2015).

https://alexisbcook.github.io/2017/global-average-pooling-layers-for-object-localization/

This is a simple application of Truncated SVD, just to get a feeling of
what happens to the accuracy if we use TruncatedSVD w/o fine-tuning.

We apply Truncated SVD on the linear layer found at the end of ResNet50,
and run a test over the validation dataset to measure the impact on the
classification accuracy.

Activation statistics can be leveraged to make pruning and quantization decisions, and so
We added support to collect these data.
- Two types of activation statistics are supported: summary statistics, and detailed records 
per activation.
Currently we support the following summaries: 
- Average activation sparsity, per layer
- Average L1-norm for each activation channel, per layer
- Average sparsity for each activation channel, per layer

For the detailed records we collect some statistics per activation and store it in a record.  
Using this collection method generates more detailed data, but consumes more time, so
Beware.

* You can collect activation data for the different training phases: training/validation/test.
* You can access the data directly from each module that you chose to collect stats for.  
* You can also create an Excel workbook with the stats.

To demonstrate use of activation collection we added a sample schedule which prunes 
weight filters by the activation APoZ according to:
"Network Trimming: A Data-Driven Neuron Pruning Approach towards 
Efficient Deep Architectures",
Hengyuan Hu, Rui Peng, Yu-Wing Tai, Chi-Keung Tang, ICLR 2016
https://arxiv.org/abs/1607.03250

We also refactored the AGP code (AutomatedGradualPruner) to support structure pruning,
and specifically we separated the AGP schedule from the filter pruning criterion.  We added
examples of ranking filter importance based on activation APoZ (ActivationAPoZRankedFilterPruner),
random (RandomRankedFilterPruner), filter gradients (GradientRankedFilterPruner), 
and filter L1-norm (L1RankedStructureParameterPruner)

Showing various details about the performance of ResNet50

Also show fitting of the histograms to the Gaussian and Laplacian
distributions.

Sometimes the gmin/gmax in group color-normalization ends up with a zero
dimensional tensor, which needs to be accessed using .item()

Sometimes the gmin/gmax in group color-normalization ends up with a zero
dimensional tensor, which needs to be accessed using .item()

- Raise IOError instead of crude exit() when file is not found in the file-system
- Test that the correct exception is raised when opening a non-existent
checkpoint file

PyTorch 0.4 now fully supports the ONNX export features that are needed
in order to create a SummaryGraph, which is sort of a "shadow graph" for
PyTorch models.

The big advantage of SummaryGraph is that it gives us information about
the connectivity of nodes.  With connectivity information we can compute
per-node MAC (compute) and BW, and better yet, we can remove channels,
filters, and layers (more on this in future commits).

In this commit we (1) replace the long and overly-verbose ONNX node names,
with PyTorch names; and (2) move MAC and BW attributes from the Jupyter
notebook to the SummaryGraph.

Various small changes due to the chamnges in the semantics and syntax of the
PyTorch 0.4 API.

Note that currently distiller.model_performance_summary() returns wrong results
on graphs containing torch.nn.DataParallel layers.

fix path to the resnet20 checkpoint in one of the examples