RetinaFace C++ Reimplement
source
Reference resources RetinaFace in insightface with python code.
model transformation tool
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
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