LSQuantization

The PyTorch implementation of Learned Step size Quantization (LSQ) in ICLR2020 (unofficial)

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The related project with training code: https://github.com/hustzxd/EfficientPyTorch (sorry for late.)

The project is working in progress, and experimental results on ImageNet are not as good as shown in the paper.

ImageNet

LSQ	fp32	w4a4	w3a3	w2a2	w8a8(1epoch, quantize data)
AlexNet	56.55, 79.09	56.96, 79.46 √	55.31, 78.59	51.18, 75.38
ResNet18	69.76, 89.08	70.26, 89.34 √	69.45, 88.85		69.68 88.92 √

Hyper-parameter

Hyper-parameter	LR	LR-scheduler	epochs	batch-size	wd
AlexNet-w4a4	0.01	CosineAnnealingLR	90	2048	1e-4
ResNet18-w4a4	0.01	CosineAnnealingLR	90	512	1e-4

Experimental Results

====VGGsmall + Cifar10=======

	VGGsmall
fp32	93.34
w4a4	94.26
w3a3	93.89
w2a2	93.42