Pruning with virtual Batch-norm statistics folding (#415)

* pruning: add an option to virtually fold BN into Conv2D for ranking

PruningPolicy can be configured using a new control argument fold_batchnorm: when set to `True`, the weights of BatchNorm modules are folded into the weights of Conv-2D modules (if Conv2D->BN edges exist in the model graph).  Each weights filter is attenuated using a different pair of (gamma, beta) coefficients, so `fold_batchnorm` is relevant for fine-grained and filter-ranking pruning methods.  We attenuate using the running values of the mean and variance, as is done in quantization.
This control argument is only supported for Conv-2D modules (i.e. other convolution operation variants and Linear operations are not supported).
e.g.:
policies:
  - pruner:
      instance_name : low_pruner
      args:
        fold_batchnorm: True
    starting_epoch: 0
    ending_epoch: 30
    frequency: 2

* AGP: non-functional refactoring

distiller/pruning/automated_gradual_pruner.py – change `prune_to_target_sparsity`
to `_set_param_mask_by_sparsity_target`, which is a more appropriate function
name as we don’t really prune in this function

* Simplify GEMM weights input-channel ranking logic

Ranking weight-matrices by input channels is similar to ranking 4D
Conv weights by input channels, so there is no need for duplicate logic.

distiller/pruning/ranked_structures_pruner.py
-change `prune_to_target_sparsity` to `_set_param_mask_by_sparsity_target`,
which is a more appropriate function name as we don’t really prune in this
function
-remove the code handling ranking of matrix rows

distiller/norms.py – remove rank_cols.

distiller/thresholding.py – in expand_binary_map treat `channels` group_type
the same as the `cols` group_type when dealing with 2D weights

* AGP: add example of ranking filters with virtual BN-folding

Also update resnet20 AGP examples