Caffe-Int8-Convert-Tools

This convert tools is base on TensorRT 2.0 Int8 calibration tools,which use the KL algorithm to find the suitable threshold to quantize the activions from Float32 to Int8(-128 - 127).

We provide the Classification(SqueezeNet_v1.1) and Detection(MobileNet_v1 SSD 300) demos based on ncnn(It is a high-performance neural network inference framework optimized for the mobile platform),and the community ready to support this implment.

The pull request in ncnn

Reference

For details, please read the following PDF:

8-bit Inference with TensorRT

MXNet quantization implement:

Quantization module for generating quantized (INT8) models from FP32 models

An introduction to the principles of a Chinese blog written by my friend:

The implement of Int8 quantize base on TensorRT

HowTo

New version

The purpose of this tool(caffe-int8-convert-tool-dev.py) is to test new features,such as mulit-channels quantization depend on group num.

This format is already supported in the ncnn latest version.I will do my best to transform some common network models into classification-dev

python caffe-int8-convert-tool-dev.py -h
usage: caffe-int8-convert-tool-dev.py [-h] [--proto PROTO] [--model MODEL]
                                  [--mean MEAN MEAN MEAN] [--norm NORM]
                                  [--images IMAGES] [--output OUTPUT]
                                  [--group GROUP] [--gpu GPU]

find the pretrained caffemodel int8 quantize scale value

optional arguments:
  -h, --help            show this help message and exit
  --proto PROTO         path to deploy prototxt.
  --model MODEL         path to pretrained caffemodel
  --mean MEAN           value of mean
  --norm NORM           value of normalize(scale value)
  --images IMAGES       path to calibration images
  --output OUTPUT       path to output calibration table file
  --group GROUP         enable the group scale(0:disable,1:enable,default:0)
  --gpu GPU             use gpu to forward(0:disable,1:enable,default:0)
python caffe-int8-convert-tool-dev.py --proto=test/models/mobilenet_v1.prototxt --model=test/models/mobilenet_v1.caffemodel --mean 103.94 116.78 123.68 --norm=0.017 --images=test/images/ output=mobilenet_v1.table --gpu=1

How to use the output file(calibration-dev.table)

For example in MobileNet_v1_dev.table

conv1_param_0 156.639840
conv2_1/dw_param_0 0 72.129143 149.919382 // the convdw layer's weight scale every group is 0.0 72.129 149.919 ......
......
conv1 49.466518
conv2_1/dw 123.720796 // the convdw layer's bottom blobchannel scale is 123.720
......

Three steps to implement the conv1 layer int8 convolution:

1. Quantize the bottom_blob and weight:

   bottom_blob_int8 = bottom_blob_float32 * data_scale(49.466518)
   weight_int8 = weight_float32 * weight_scale(156.639840)
   

2. Convolution_Int8:

   top_blob_int32 = bottom_blob_int8 * weight_int8
   

3. Dequantize the TopBlob_Int32 and add the bias:

   top_blob_float32 = top_blob_int32 / [data_scale(49.466518) * weight_scale(156.639840)] + bias_float32
   

How to use with ncnn

quantized int8 inference

Accuracy and Performance

We used ImageNet2012 Dataset to complete some classification test.

Type	Detail
Calibration Dataset	ILSVRC2012_img_test   1k
Test Dataset	ILSVRC2012_img_val     5k
Framework	ncnn
Support Layer	Convolution3x3,Convolution1x1,ConvolutionDepthwise3x3

The following table show the Top1 and Top5 different between Float32 and Int8 inference.

Models	FP32		INT8		Loss
	Top1	Top5	Top1	Top5	Diff Top1	Diff Top5
SqueezeNet v1.1	57.86%	79.86%	57.36%	79.84%	-0.50%	-0.02%
MobileNet v1	67.78%	87.62%	64.92%	85.22%	-2.86%	-2.40%
MobileNet v2	70.20%	89.20%	69.00%	88.04%	-1.06%	-1.16%
GoogleNet v1	67.70%	88.32%	67.64%	88.26%	-0.06%	-0.06%
ResNet-18	65.50%	86.46%	65.48%	86.44%	-0.02%	-0.02%
ResNet-50	71.68%	89.94%	71.38%	89.52%	-0.30%	-0.32%

We used VOC0712,MSCOCO Dataset to complete some detection test.

Type	Detail
Test Dataset	VOC2007
Unit	mAP (Class 20)

Models	FP32	INT8	Loss
SqueezeNet SSD	61.80	60.40	-1.40
MobileNet_v1 SSD	70.49	68.85	-1.64

Speed up

The following table show the speedup between Float32 and Int8 inference.It should be noted that the winograd algorithm is not used in the Float32 inference.The Hardware Platform is Hisi3519(Cortex-A17@1.2GHz)

Uint(ms)	SqueezeNet v1.1	MobileNet v1	MobileNet v2	GoogleNet	ResNet18	MobileNetv1 SSD
Float32	382	568	392	1662	1869	1120
Int8	242	369	311	1159	1159	701
Ratio	x1.30	x1.41	x1.28	x1.43	x1.61	x1.47

Sparse Connection Tool(experimental stage)

I tried to analyze the sparse connection of the CNN model.Using this tool,I've got some data(sparse-connection), and I hope you'll use it.

Contributor

Thanks to our company SenseNets to support the open source project,and NVIDIA for providing the principle of correlation entropy,and ncnn's author nihui sharing his neural network inference framework.

Thanks to the help from the following friends:

Algorithm : xupengfeixupf, JansonZhu, wangxinwei, lengmm

Python : daquexian

License

BSD 3 Clause