Version 0.3.0

[0.3.0] - 2023/01/13

Added

• new models:
  • MeliusNet
  • BinaryDenseNet
  • QuickNet
• simple example script for MNIST
• support for integration of bitorch's inference engine for the following layers
  • QLinear
  • QConv
• a quantized DLRM version, derived from this implementation
• example code for training the quantized DLRM model
• new quantization function: Progressive Sign
• new features in PyTorch Lightning example:
  • training with Knowledge Distillation
  • improved logging
  • callback to update Progressive Sign module
• option to integrate custom models, datasets, quantization functions
• a quantization scheduler which lets you change quantization methods during training
• a padding layer

Changed

• requirements changed:
  • code now depends on torch 1.12.x and torchvision 0.13.x
  • requirements for examples are now stored at their respective folders
  • optional requirements now install everything needed to run all examples
• code is now formatted with the black code formatter
• using PyTorch's implementation of RAdam
• renamed the bitwidth attribute of quantization functions to bit_width
• moved the image datasets out of the bitorch core package into the image classification example

Fixed

• fix error from updated protobuf package