Group-wise convolutions with num_groups == num_in_channels, as
configured in MobileNet for example, create attribute and shape dependency
chains that are longer than convolutions with num_groups == 1.
For example in the sequence below, changing the number of filters of the
weights of Conv1, triggers changes in BN1, Conv2, BN2, and Conv3
(g indicates the number of groups):

Conv1(g=1) => BN1 ==> Conv2(g=32) ==> BN2 ==> Conv3(g=1)

Changing the number of filters used in Conv1 affects the parameters and
attributes of BN1 and Conv2 - The number of input channels of Conv2 is
changed.as explained in
https://nervanasystems.github.io/distiller/tutorial-struct_pruning.html.

However, since Conv2 has num_groups == num_in_channels, we have to change
num_groups, which triggers a change in num_out_channels.  This is akin to
changing the number of filters of Conv2, which triggers a change in BN2
and Conv3.

models/mobilenet.py:
Changed the code that flattens the output of the
features-extractors and prepares it as input to the classifier.
The code was written using hard-coded shape values, which made it
impossible to use in thinned models (where dimensions are changed).

tests/test_pruning.py:
Added test for thinning MobileNet (grouped convolutions)