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Repository Details

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

LSQuantization

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


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