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 -r 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.

Models	FP32	INT8-TRT	INT8-EQ	INT7-TRT	INT7-EQ
SqueezeNetV1.1	56.56	56.24	56.28	54.88	56.08
MobileNetV1	69.33	68.74	68.84	66.97	68.26
VGG16	70.97	70.95	70.97	70.92	70.96
ResNet50	75.20	75.04	75.13	72.78	75.04

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

Models	FP32	INT8-TRT	INT8-EQ	INT7-TRT	INT7-EQ
SqueezeNet-SSD	62.00	61.45	62.05	60.01	61.62
MobileNet-SSD	72.04	69.79	71.39	63.88	68.79

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

Models	FP32	INT8-TRT	INT8-EQ	INT7-TRT	INT7-EQ
lfw	99.45	99.36	99.48	99.28	99.36
agedb_30	95.78	95.23	95.38	95.03	95.73
calfw	95.05	94.76	94.88	94.75	94.68
cfp_ff	99.50	99.50	99.61	99.44	99.60
cfp_fp	89.77	89.17	90.04	88.47	89.87
cplfw	86.45	85.58	86.03	85.91	86.76
vgg2_fp	90.64	89.70	90.50	89.64	90.44

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

Methods	MobileNetV1-FP32	MobileNetV1-INT8	ResNet50-FP32	ResNet50-INT8
EQ	69.33	68.84	75.20	75.13
QAT	70.90	70.70	75.20	75.00

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.

Models	TRT-INT8(#1)	EQ-INT7(#1)	TRT-INT8(#4)	EQ-INT7(#4)
SqueezeNetV1.1	180	120	66	44
MobileNetV1	234	189	65	57
VGG16	3326	2873	1423	1252
ResNet50	1264	993	415	300

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.

Models	TRT-INT8(#1)	EQ-INT7(#1)	TRT-INT8(#2)	EQ-INT7(#2)
SqueezeNetV1.1	79	57	54	37
MobileNetV1	105	84	56	46
VGG16	1659	1385	1034	849
ResNet50	559	463	338	262

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}
}