Add: arcface-mobilefacenet (wang-xinyu#545)

* add arcface-mobilefacenet * Update arcface-mobilefacenet.cpp * modify CMmakeLists.txt, add arcface-r100.cpp * fix typos,resolve conflicts,update README.md Co-authored-by: Admin <[email protected]>
lijie2160 · May 17, 2021 · 53ba7e9 · 53ba7e9
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diff --git a/arcface/CMakeLists.txt b/arcface/CMakeLists.txt
@@ -35,5 +35,17 @@ target_link_libraries(arcface-r50 cudart)
 target_link_libraries(arcface-r50 myplugins)
 target_link_libraries(arcface-r50 ${OpenCV_LIBS})
 
+add_executable(arcface-mobilefacenet ${PROJECT_SOURCE_DIR}/arcface-mobilefacenet.cpp)
+target_link_libraries(arcface-mobilefacenet nvinfer)
+target_link_libraries(arcface-mobilefacenet cudart)
+target_link_libraries(arcface-mobilefacenet myplugins)
+target_link_libraries(arcface-mobilefacenet ${OpenCV_LIBS})
+
+add_executable(arcface-r100 ${PROJECT_SOURCE_DIR}/arcface-r100.cpp)
+target_link_libraries(arcface-r100 nvinfer)
+target_link_libraries(arcface-r100 cudart)
+target_link_libraries(arcface-r100 myplugins)
+target_link_libraries(arcface-r100 ${OpenCV_LIBS})
+
 add_definitions(-O2 -pthread)
 
diff --git a/arcface/README.md b/arcface/README.md
@@ -2,7 +2,7 @@
 
 The mxnet implementation is from [deepinsight/insightface.](https://github.com/deepinsight/insightface)
 
-The pretrained model is [LResNet50E-IR,ArcFace@ms1m-refine-v1.](https://github.com/deepinsight/insightface/wiki/Model-Zoo#32-lresnet50e-irarcfacems1m-refine-v1)
+The pretrained models are from [LResNet50E-IR,ArcFace@ms1m-refine-v1](https://github.com/deepinsight/insightface/wiki/Model-Zoo#32-lresnet50e-irarcfacems1m-refine-v1), [LResNet100E-IR,ArcFace@ms1m-refine-v2](https://github.com/deepinsight/insightface/wiki/Model-Zoo#31-lresnet100e-irarcfacems1m-refine-v2) and [MobileFaceNet,ArcFace@ms1m-refine-v1](https://github.com/deepinsight/insightface/wiki/Model-Zoo#34-mobilefacenetarcfacems1m-refine-v1)
 
 The two input images used in this project are joey0.ppm and joey1.ppm, download them from [Google Drive.](https://drive.google.com/drive/folders/1ctqpkRCRKyBZRCNwo9Uq4eUoMRLtFq1e). The input image is 112x112, and generated from `get_input()` in `insightface/deploy/face_model.py`, which is cropped and aligned face image.
 
@@ -12,13 +12,12 @@ The two input images used in this project are joey0.ppm and joey1.ppm, download
 
 ## Config
 
-- FP16/FP32 can be selected by the macro `USE_FP16` in arcface-r50.cpp
-- GPU id can be selected by the macro `DEVICE` in arcface-r50.cpp
+- 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 arcface-r50.wts from mxnet implementation with LResNet50E-IR,ArcFace@ms1m-refine-v1 pretrained model
-
+1.Generate .wts file from mxnet implementation of pretrained model. The following example described how to generate arcface-r50.wts from mxnet implementation of LResNet50E-IR,ArcFace@ms1m-refine-v1.
 ```
 git clone https://github.com/deepinsight/insightface
 cd insightface
@@ -29,9 +28,10 @@ cd deploy
 python gen_wts.py
 // a file 'arcface-r50.wts' will be generated.
 // the master branch of insightface should work, if not, you can checkout 94ad870abb3203d6f31b049b70dd080dc8f33fca
-```
+// arcface-r100.wts/arcface-mobilefacenet.wts can be generated in similar way from mxnet implementation of LResNet100E-IR,ArcFace@ms1m-refine-v1/MobileFaceNet,ArcFace@ms1m-refine-v1 pretrained model.
 
-2. put arcface-r50.wts into tensorrtx/arcface, build and run
+```
+2.Put .wts file into tensorrtx/arcface, build and run
 
 ```
 cd tensorrtx/arcface
@@ -42,9 +42,20 @@ cmake ..
 make
 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-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-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.
+3.Check the output log, latency and similarity score.
 
 ## More Information