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products:sbc:vim4:npu:demos:facenet [2023/09/18 01:53]
sravan [Build virtual environment] 		products:sbc:vim4:npu:demos:facenet [2025/06/11 22:05] (current)
louis
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 ~~tag> NPU FaceNet VIM4 PyTorch~~ ~~tag> NPU FaceNet VIM4 PyTorch~~
 +
 +**Doc for version ddk-3.4.7.7**
 +
 ====== FaceNet PyTorch VIM4 Demo - 6 ====== ====== FaceNet PyTorch VIM4 Demo - 6 ======
  
 {{indexmenu_n>6}} {{indexmenu_n>6}}
 +
 +===== Introduction =====
 +
 +FaceNet is a face recognition model. It will convert a face image into a feature map. Compare the feature map between image and face database. Here are two judgment indicators, cosine similarity and Euclidean distance. The closer the cosine similarity is to 1 and the closer the Euclidean distance is to 0, the more similar is between two faces.
 +
 +Here takes **lin_1.jpg** as example. Inference results on VIM4.
 +
 +{{:products:sbc:vim4:npu:demos:facenet-demo-output.webp?400|}}
 +
 ===== Get Source Code ===== ===== Get Source Code =====
  
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 Follow Docker official documentation to install Docker: [[https://docs.docker.com/engine/install/ubuntu/|Install Docker Engine on Ubuntu]]. Follow Docker official documentation to install Docker: [[https://docs.docker.com/engine/install/ubuntu/|Install Docker Engine on Ubuntu]].
  
-Then fetch the prebuilt NPU Docker container and run it.+Follow the script below to get Docker image:
  
 ```shell ```shell
-$ docker pull yanwyb/npu:v1 +docker pull numbqq/npu-vim4
-$ docker run -it --name vim4-npu1 -v $(pwd):/home/khadas/npu \ +
- -v /etc/localtime:/etc/localtime:ro \ +
- -v /etc/timezone:/etc/timezone:ro \ +
- yanwyb/npu:v1+
 ``` ```
  
-==== Get conversion tool ====+==== Get Convert Tool ====
  
-Download Tool from [[gl>khadas/vim4_npu_sdk]].+Download Tool from [[gh>khadas/vim4_npu_sdk]].
  
 ```shell ```shell
-$ git clone https://gitlab.com/khadas/vim4_npu_sdk+$ git lfs install 
 +$ git lfs clone https://github.com/khadas/vim4_npu_sdk 
 +$ cd vim4_npu_sdk 
 +$ ls 
 +adla-toolkit-binary  adla-toolkit-binary-3.1.7.4  convert-in-docker.sh  Dockerfile  docs  README.md
 ``` ```
 +
 +  * ''adla-toolkit-binary/docs'' - SDK documentations
 +  * ''adla-toolkit-binary/bin'' - SDK tools required for model conversion
 +  * ''adla-toolkit-binary/demo'' - Conversion examples
 +
 +<WRAP important>
 +If your kernel is older than 241129, please use branch npu-ddk-1.7.5.5.
 +</WRAP>
 +
 +==== Convert ====
  
 After training model, modify ''facenet-pytorch/nets/facenet.py'' as follows. After training model, modify ''facenet-pytorch/nets/facenet.py'' as follows.
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 ``` ```
  
-Create a python file written as follows and run to convert model to ONNX.+Create a Python file written as follows and run to convert the model to ONNX.
  
 ```python export.py ```python export.py
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         --dtypes "float32" \         --dtypes "float32" \
         --inference-input-type float32 \         --inference-input-type float32 \
- --inference-output-type float32 \+ --inference-output-type float32 \
         --quantize-dtype int8 --outdir onnx_output  \         --quantize-dtype int8 --outdir onnx_output  \
         --channel-mean-value "0,0,0,255"  \         --channel-mean-value "0,0,0,255"  \
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 ``` ```
  
-Run ''convert_adla.sh'' to generate VIM4 model. The converted model is ''xxx.adla'' in ''onnx_output''.+Run ''convert_adla.sh'' to generate the VIM4 model. The converted model is ''xxx.adla'' in ''onnx_output''.
  
 ```shell ```shell
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 $ git clone https://github.com/khadas/vim4_npu_applications $ git clone https://github.com/khadas/vim4_npu_applications
 ``` ```
 +
 +<WRAP important>
 +If your kernel is older than 241129, please use version before tag ddk-3.4.7.7.
 +</WRAP>
  
 ==== Install dependencies ==== ==== Install dependencies ====
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 === Picture input demo === === Picture input demo ===
  
-There are two modes of this demo. One is converting face images into feature vectors and saving vectors in face library. Another is comparing input face image with faces in library and outputting Euclidean distance and cosine similarity.+There are two modes of this demo. One is converting face images into feature vectors and saving vectors in the face library. Another is comparing input face image with faces in the library and outputting Euclidean distance and cosine similarity.
  
 Put ''facenet_int8.adla'' in ''vim4_npu_applications/facenet/data/''. Put ''facenet_int8.adla'' in ''vim4_npu_applications/facenet/data/''.
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 ```shell ```shell
 # Compile # Compile
-$ cd vim4_npu_applications/retinaface+$ cd vim4_npu_applications/facenet
 $ mkdir build $ mkdir build
 $ cd build $ cd build
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 # Run mode 1 # Run mode 1
-$ sudo ./facenet -m ../data/facenet_int8.adla -p 1+$ ./facenet -m ../data/facenet_int8.adla -p 1
 ``` ```
  
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 ```shell ```shell
 # Run mode 2 # Run mode 2
-$ sudo ./facenet -m ../data/facenet_int8.adla -p ../data/img/lin_2.jpg+$ ./facenet -m ../data/model/facenet_int8.adla -p ../data/img/lin_2.jpg
 ``` ```
  
 +Here are two comparison methods, **Euclidean distance** and **cosine similarity**.
 +
 +**Euclidean distance** is smaller, more similar between two faces.
 +
 +**Cosine similarity** is closer to 1, more similar between two faces.
Last modified: 2023/09/18 01:53 by sravan

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