RetinaFace C++ Reimplement

source

Reference resources RetinaFace in insightface with python code.

model transformation tool

MXNet2Caffe

you need to add some layers yourself, and in caffe there is not upsample,you can replace with deconvolution,and maybe slight accuracy loss.

the origin model reference from mobilenet25，and I have retrain it.

Demo

$ mkdir build
$ cd build/
$ cmake ../
$ make

you need to modify dependency path in CmakeList file.

Speed

test hardware：1080Ti

test1:

model	speed	input size	preprocess time	inference	postprocess time
mxnet	44.8ms	1280ｘ896	19.0ms	8.0ms	16.0ms
caffe	46.9ms	1280ｘ896	5.8ms	24.1ms	16.0ms
tensorrt	29.3ms	1280ｘ896	6.9ms	5.4ms	15.0ms

test2:

model	speed	inputsize	preprocess time	inference	postprocess time
mxnet	6.4ms	320x416	1.3ms	0.1ms	4.2ms
caffe	30.8ms	320x416	1.2ms	27ms	2.3ms
tensorrt	4.7ms	320x416	0.7ms	1.9ms	1.8ms

tensorrt batch test:

batchsize	inputsize	maxbatchsize	preprocess time	inference	postprocess time	all	GPU
1	448x448	8	1.0ms	2.3ms	2.6ms	6.7ms	35%
2	448x448	8	2.5ms	3.3ms	5.2ms	11.8ms	33%
4	448x448	8	4.1ms	4.6ms	10.0ms	21.8ms	28%
8	448x448	8	8.7ms	7.0ms	20.3ms	40.7ms	23%
16	448x448	32	28.1	14.7	38.7ms	92.0ms	-
32	448x448	32	36.2ms	26.3	75.7ms	163.5ms	-

note: batch size have some advantage in inference but can't speed up preprocess and postprocess.

optimize post process：

batchsize	inputsize	maxbatchsize	preprocess time	inference	postprocess time	all	GPU
1	448x448	8	1.0ms	2.3ms	0.09ms	3.5ms	70%
2	448x448	8	2.2ms	2.8ms	0.2ms	5.3ms	60%
4	448x448	8	3.7ms	5.0ms	0.3ms	8.4ms	55%
8	448x448	8	7.5ms	6.5ms	0.67ms	14.9ms	50%
16	448x448	32	26ms	13ms	1.3ms	41ms	40%
32	448x448	32	32ms	22ms	2.7ms	56.6ms	50%

use nvidia npp library to speed up preprocess：

batchsize	inputsize	maxbatchsize	preprocess time	inference	postprocess time	all	GPU
1	448x448	8	0.2ms	2.3ms	0.1ms	2.6ms	91%
2	448x448	8	0.3ms	3.0ms	0.2ms	3.5ms	85%
4	448x448	8	0.5ms	4.1ms	0.32ms	5.0ms	82%
8	448x448	8	1.2ms	6.3ms	0.77ms	8.3ms	79%
16	448x448	32	2.2ms	14ms	1.3ms	16.7ms	80%
32	448x448	32	5.0ms	22ms	2.8ms	29.3ms	77%

INT8 inference

INT8 calibration table can generate by INT8-Calibration-Tool.

Accuracy