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products:sbc:vim4:npu:demos:retinaface [2024/01/04 05:16]
louis 		products:sbc:vim4:npu:demos:retinaface [2025/01/08 22:29] (current)
louis
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 ~~tag> NPU RetinaFace VIM4 PyTorch~~ ~~tag> NPU RetinaFace VIM4 PyTorch~~
 +
 +**Doc for version ddk-3.4.7.7**
  
 ====== RetinaFace PyTorch VIM4 Demo - 5 ====== ====== RetinaFace PyTorch VIM4 Demo - 5 ======
Line 34: Line 36:
 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
 ``` ```
  
-==== Convert ====+  * ''adla-toolkit-binary/docs'' - SDK documentations 
 +  * ''adla-toolkit-binary/bin'' - SDK tools required for model conversion 
 +  * ''adla-toolkit-binary/demo'' - Conversion examples
  
-After training the model, we should convert the PyTorch model into an ONNX model.+<WRAP important> 
 +If your kernel is older than 241129, please use branch npu-ddk-1.7.5.5. 
 +</WRAP>
  
-Copy ''nets/retinaface.py'' and rename ''retinaface_export.py''. Modify ''retinaface_export.py'' as follows. +==== Convert ====
- +
-```diff +
-class ClassHead(nn.Module): +
-    def __init__(self,inchannels=512,num_anchors=2): +
-        super(ClassHead,self).__init__() +
-        self.num_anchors = num_anchors +
-        self.conv1x1 = nn.Conv2d(inchannels,self.num_anchors*2,kernel_size=(1,1),stride=1,padding=0) +
- +
-    def forward(self,x): +
-        out = self.conv1x1(x) +
--       out = out.permute(0,2,3,1).contiguous() +
-+       out = out.contiguous() +
-         +
--       return out.view(out.shape[0], -1, 2) +
-+       return out.view(out.shape[0], 4, -1) +
- +
-class BboxHead(nn.Module): +
-    def __init__(self,inchannels=512,num_anchors=2): +
-        super(BboxHead,self).__init__() +
-        self.conv1x1 = nn.Conv2d(inchannels,num_anchors*4,kernel_size=(1,1),stride=1,padding=0) +
- +
-    def forward(self,x): +
-        out = self.conv1x1(x) +
--       out = out.permute(0,2,3,1).contiguous() +
-+       out = out.contiguous() +
- +
--       return out.view(out.shape[0], -1, 4) +
-+       return out.view(out.shape[0], 8, -1) +
- +
-class LandmarkHead(nn.Module): +
-    def __init__(self,inchannels=512,num_anchors=2): +
-        super(LandmarkHead,self).__init__() +
-        self.conv1x1 = nn.Conv2d(inchannels,num_anchors*10,kernel_size=(1,1),stride=1,padding=0) +
- +
-    def forward(self,x): +
-        out = self.conv1x1(x) +
--       out = out.permute(0,2,3,1).contiguous() +
-+       out = out.contiguous() +
- +
--       return out.view(out.shape[0], -1, 10) +
-+       return out.view(out.shape[0], 20, -1) +
-``` +
- +
-```diff +
--       bbox_regressions    = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1) +
--       classifications     = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1) +
--       ldm_regressions     = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1) +
-+       bbox_regressions    = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=2) +
-+       classifications     = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=2) +
-+       ldm_regressions     = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=2) +
- +
-        if self.mode == 'train': +
-            output = (bbox_regressions, classifications, ldm_regressions) +
-        else: +
--           output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions) +
-+           output = (bbox_regressions, classifications, ldm_regressions) +
-        return output +
-```+
  
-Create the Python conversion script as follows and run.+After training the model, we should convert the PyTorch model into an ONNX model. Create the Python conversion script as follows and run.
  
 ```python export.py ```python export.py
 import torch import torch
 import numpy as np import numpy as np
-from nets.retinaface_export import RetinaFace+from nets.retinaface import RetinaFace
 from utils.config import cfg_mnet, cfg_re50 from utils.config import cfg_mnet, cfg_re50
  
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         --inputs "images" \         --inputs "images" \
         --input-shapes  "3,640,640"  \         --input-shapes  "3,640,640"  \
 +        --dtypes "float32" \
         --inference-input-type float32 \         --inference-input-type float32 \
  --inference-output-type float32 \  --inference-output-type float32 \
-        --dtypes "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"  \
-        --source-file ./dataset.txt  \+        --source-file ./retinaface_dataset.txt  \
         --iterations 500 \         --iterations 500 \
         --disable-per-channel False \         --disable-per-channel False \
         --batch-size 1 --target-platform PRODUCT_PID0XA003         --batch-size 1 --target-platform PRODUCT_PID0XA003
 ``` ```
- 
-<WRAP important > 
-Please prepare about 500 pictures for quantification. If the pictures size is smaller than model input size, please resize pictures to input size before quantification. 
-</WRAP> 
  
 Run ''convert_adla.sh'' to generate the 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''.
Line 182: Line 126:
 ``` ```
  
-<WRAP important > +<WRAP important> 
-If your kernel version is 5.4 or earlier, please use tag ''ddk-1.7.5.5''. Tag ''ddk-2.3.6.7'' is for 5.15.+If your kernel is older than 241129, please use version before tag ddk-3.4.7.7.
 </WRAP> </WRAP>
  
Line 208: Line 152:
  
 # Run # Run
-$ sudo ./retinaface -m ../data/retinaface_int8.adla -p ../data/timg.jpg+$ ./retinaface -m ../data/retinaface_int8.adla -p ../data/timg.jpg
 ``` ```
 +
 +{{:products:sbc:vim4:npu:demos:retinaface-demo-output.webp?800|}}
  
 === Camera input demo === === Camera input demo ===
  
 Put ''retinaface_int8.adla'' in ''vim4_npu_applications/retinaface_cap/data/''. Put ''retinaface_int8.adla'' in ''vim4_npu_applications/retinaface_cap/data/''.
 +
 +== Compile ==
  
 ```shell ```shell
-# Compile +$ cd vim4_npu_applications/face_recognition_cap
-$ cd vim4_npu_applications/retinaface_cap+
 $ mkdir build $ mkdir build
 $ cd build $ cd build
 $ cmake .. $ cmake ..
 $ make $ make
 +```
  
-# Run +== Run== 
-$ sudo ./retinaface_cap -m ../data/retinaface_int8.adla -d 0 -w 1920 -h 1080+ 
 +**MIPI Camera** 
 + 
 +``` 
 +$ ./retinaface_cap -m ../data/retinaface_int8.adla -t mipi 
 +``` 
 + 
 +**USB Camera** 
 +``` 
 +$ cd build 
 +$ ./retinaface_cap -m ../data/retinaface_int8.adla -t usb -d 0
 ``` ```
  
-''0'' is the camera device index.+**TIP**: Replace 0 as the number for your camera device. Such as ''/dev/video5'', it should be ''-d 5''.
  
Last modified: 2024/01/04 05:16 by louis

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