arcface

arcface

TensortRT 8

The mxnet implementation is from deepinsight/insightface.

Updated Pretrained Weights: ArcFace-R100 Insight Face Google Drive

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Previous Pre-trained models: The pretrained models are from LResNet50E-IR,ArcFace@ms1m-refine-v1, LResNet100E-IR,ArcFace@ms1m-refine-v2 and MobileFaceNet,ArcFace@ms1m-refine-v1

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The two input images used in this project are joey0.ppm and joey1.ppm, download them from Google Drive.. The input image is 112x112, and generated from get_input() in insightface/deploy/face_model.py, which is cropped and aligned face image.

Config

• FP16/FP32 can be selected by the macro USE_FP16 in arcface-r50/r100/mobilefacenet.cpp
• GPU id can be selected by the macro DEVICE in arcface-r50/r100/mobilefacenet.cpp

Run

1.Generate .wts file from mxnet implementation of pretrained model. The following example described how to generate arcface-r100.wts from mxnet implementation of LResNet100E-IR,ArcFace@ms1m-refine-v1.

git clone https://github.com/deepinsight/insightface
cd insightface
git checkout 3866cd77a6896c934b51ed39e9651b791d78bb57
cd deploy
// copy tensorrtx/arcface/gen_wts.py to here(insightface/deploy)
// download model-r100-ii.zip and unzip here(insightface/deploy)
python gen_wts.py
// a file 'arcface-r100.wts' will be generated.
// the master branch of insightface should work, if not, you can checkout 94ad870abb3203d6f31b049b70dd080dc8f33fca
// arcface-r50.wts/arcface-mobilefacenet.wts can be generated in similar way from mxnet implementation of LResNet50E-IR,ArcFace@ms1m-refine-v1/MobileFaceNet,ArcFace@ms1m-refine-v1 pretrained model.

2.Put .wts file into tensorrtx/arcface, build and run

cd tensorrtx/arcface
// download joey0.ppm and joey1.ppm, and put here(tensorrtx/arcface)
mkdir build
cd build
cmake ..
make
sudo ./arcface-r100 -s    // serialize model to plan file i.e. 'arcface-r100.engine'
sudo ./arcface-r100 -d    // deserialize plan file and run inference

or

sudo ./arcface-r50 -s   // serialize model to plan file i.e. 'arcface-r50.engine'
sudo ./arcface-r50 -d   // deserialize plan file and run inference


or

sudo ./arcface-mobilefacenet -s   // serialize model to plan file i.e. 'arcface-mobilefacenet.engine'
sudo ./arcface-mobilefacenet -d   // deserialize plan file and run inference

3.Check the output log, latency and similarity score.

More Information

See the readme in home page.