Fibonacci Incremental Network Quantization

A Pytorch enhancement to the "Incremental Network Quantization" project that will be able to manage and test Fibonacci code quantization, using the same structure as in the original project.

Incremental Network Quantization

A PyTorch implementation of "Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights"

@inproceedings{zhou2017,
title={Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights},
author={Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, Yurong Chen},
booktitle={International Conference on Learning Representations,ICLR2017},
year={2017},
}

[Paper]

Official Caffe implementation is available [here] (Code in this repo is based on the paper and not on the official Caffe implementation)

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Installation

The code is implemented in Python 3.7 and PyTorch 1.1

pip install -e .

Usage

inq.SGD(...) implements SGD that only updates weights according to the weight partitioning scheme of INQ.

inq.INQScheduler(...) handles the the weight partitioning and group-wise quantization stages of the incremental network quantization procedure.

reset_lr_scheduler(...) resets the learning rate scheduler.

Examples

The INQ training procedure looks like the following:

optimizer = inq.SGD(...)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, ...)
inq_scheduler = inq.INQScheduler(optimizer, [0.5, 0.75, 0.82, 1.0], strategy="pruning")
for inq_step in range(3): # Iteration for accumulated quantized weights of 50% 75% and 82% 
    inq.reset_lr_scheduler(scheduler)
    inq_scheduler.step()
    for epoch in range(5):
        scheduler.step()
        train(...)
inq_scheduler.step() # quantize all weights, further training is useless
validate(...)

examples/imagenet_quantized.py is a modified version of the official PyTorch imagenet example. Using this example you can quantize a pretrained model on imagenet. Compare the file to the original example to see the differences.

Results

Results using this code differ slightly from the results reported in the paper.

Network	Strategy	Bit-width	Top-1 Accuracy	Top-5 Accuracy
resnet18	ref	32	69.758%	89.078%
resnet18	pruning	5	69.64%	89.07%