EasyQuant(EQ) is an efficient and simple post-training quantization method via effectively optimizing the scales of weights and activations.

EasyQuant: Post-training Quantization via Scale Optimization

EasyQuant(EQ) is an efficient and simple post-training quantization method via effectively optimizing the scales of weights and activations. Our paper is available on arXiv

Requirements

eq-ncnn

caffe

pip install requirements.txt

Updates:

• 06/25/2020: We have released EasyQuant.pdf paper and eq-ncnn.
• 06/24/2020: We have released VGG16 example.

Data Preparation

First, for ImageNet1k classification task, please download ImageNet2012. We random sampled 3000 calibration images from ImageNet val set to data/calib.txt for KLD quantization and select 50 samples from calib to data/list50.txt for EasyQuant scale finetuning. Then we will evaluate quantified models on val set.

How to Run

1. Get caffe and ncnn ready
2. Build python_ncnn

# 
cd python_ncnn
# modify ncnn build path in Makefile
make -j8

3. Run VGG16 example

cd ..
sh example/vgg16/run.sh

This following 6 steps will be performed in run.sh.

# download vgg16 model and upgrade proto and caffemodel files
sh model/vgg16/net_file_upgrade.sh

# generate scale of weight and activation use quantation tools
sh example/vgg16/run_scale_quantation.sh

# get ncnn param bin from scale table
sh example/vgg16/run_caffe2ncnn.sh

# infer layer blob shape
sh example/vgg16/run_infer_shape.sh

# run scale fine tuning
sh example/vgg16/run_scale_fine_tuning.sh

# run validtion on imagenet val
sh example/vgg16/run_validation.sh

Results

Validation Results

1. Classification on ImageNet2012 validation dataset for different convolutional models in context of both INT8 and INT7 post-training quantization.

2. Object detection on VOC2007 task for SSD models with bachbone SqueezeNet and MobileNet V1.

3. Verification performance for InsightFace model MobileFaceNet on 7 most common validation dataset

4. Compare our method with more complex QAT(quantization aware trainin) approach in 8 bit width.

Speed Tests

INT7 Post-training Inference VS INT8 on real devices. We implement our efficient designs on INT7 post-training inference which well be released in eq-ncnn

1. The latency (ms) performance on RK3399, whose inside is a 1.5 GHz 64-bit Quad-core ARM Cortex-A53. #k means k threads.

2. The latency (ms) performance on RK3399, which inside is a 1.8 GHz 64-bit Dual-core ARM Cortex-A72. #k means k threads.

Contributing

PRs accepted.

License and Citation

BSD3 © DeepGlint

@inproceedings{easyquant,
    title={EasyQuant: Post-training Quantization via Scale Optimization},
    author={Di Wu, Qi Tang, Yongle Zhao, Ming Zhang, Debing Zhang, Ying Fu},
    year={2020}
}