Quantizations

Post-Training Quantization for dummy

Following papers are implemented in PyTorch (DFQ) Quantizing deep convolutional networks forefficient inference: A whitepaper
Data-Free Quantization Through Weight Equalization and Bias Correction

Quantization with min-max	Reproduced result	Paper result
8 bitwidth - Batch Norm folding	0.1%	0.1%
8 bitwidth - Cross Layer Scaling	69.59%	69.91%
8 bitwidth - CLS + High bias Absortion	70.02%	70.92%

Quantization with Mean Squared Error	Reproduced result	Paper result
8 bitwidth - Batch Norm folding	0.1%	0.11%
8 bitwidth - Cross Layer Scaling	70.12%	69.91%
8 bitwidth - CLS + High bias Absortion	70.36%	70.92%

Note

BatchNorm layer has been folded
Convolution layer and Activations are fused as one operation
Cross Layer scaling & High bias fold are implemented

python main.py --images-dir <data-path> --ptq cle hba

Packages

torch : 1.4.0+cu100
torchvision  : 0.5.0+cu100

reference

MobileNet pretrained model : https://github.com/tonylins/pytorch-mobilenet-v2