This repository provides code and trained models for the CVPR 2020 paper:

Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets
Daniel Haase*, Manuel Amthor*
CVPR 2020
arXiv:2003.13549

Please note that the code provided here is work-in-progress. Therefore, many features are still missing or may change between versions.

• Python>=3.6
• PyTorch>=1.0.0 (support for other frameworks might be added later)

pip install --upgrade bsconv

See here for PyTorch usage details.

• 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
• includes modules BSConvU and BSConvS for PyTorch
• includes replacers BSConvU_Replacer and BSConvS_Replacer for PyTorch