Bsconv

* BSConv for PyTorch:
* added support for more model architectures (see `bsconv.pytorch.get_model`)
* added result tables and plots for ResNets, WRNs, MobileNets on CIFAR datasets
* removed script `bin/bsconv_pytorch_list_architectures.py`, because `bsconv.pytorch.get_model` is more flexible now (see the BSConv PyTorch usage readme for available architectures)

* BSConv for PyTorch:
* added ready-to-use model definitions (MobileNetV1, MobileNetV2, MobileNetsV3, ResNets and WRNs and their BSConv variants for CIFAR and ImageNet/fine-grained datasets)
* added training script for CIFAR and ImageNet/fine-grained datasets
* added class for the StanfordDogs dataset

* BSConv for PyTorch:
* removed activation and added option for normalization of PW layers in BSConv-S (issue 1) (**API change**)
* added option for normalization of PW layers in BSConv-U (**API change**)
* ensure that BSConv-S never uses more mid channels (= M') than input channels (M) and added parameter `min_mid_channels` (= M'_min) (**API change**)
* added model profiler for parameter and FLOP counting
* replacer now shows number of old and new model parameters

* first public version
* BSConv for PyTorch:
* modules `BSConvU` and `BSConvS`
* replacers `BSConvU_Replacer` and `BSConvS_Replacer`