YOLOv5-ncnn-arm

An ncnn implementation of YOLOv5 on ARM devices, capable of using GPU to accelerate inference

Environment

• Ubuntu 18.04 (x86_64)
• Ubuntu 16.04 (aarch64)
• OpenCV 3.2.0
• CMake 3.10.0

Getting Started with CPU version

1. The compilation of the project should be on the ARM device.

2. Install OpenCV.

   sudo apt-get install libopencv-dev
   

3. cd YOLOv5ncnn
   

4. Edit "CMakeLists.txt" to configure correctly.

5. Compile and run.

   cd build
   cmake ..
   make
   ./../bin/YOLOv5ncnn
   

Compile ncnn-ARM by yourself

1. The compilation of ncnn should be on the x86 device.

2. Install OpenCV.

   sudo apt-get install libopencv-dev
   

3. Install protobuf.

   sudo apt install protobuf-compiler libprotobuf-dev 
   

4. Download source code of ncnn from https://github.com/Tencent/ncnn/releases.

   unzip ncnn-master.zip
   

5. Download gcc-arm-toolchain and add to environment variables.

   tar -zxvf gcc-arm-8.2-2018.11-x86_64-aarch64-linux-gnu.tar.xz
   gedit ~/.bashrc
   export PATH=$PATH:/home/username/gcc-arm-8.2-2018.11-x86_64-aarch64-linux-gnu/bin
   source ~/.bashrc
   

6. Compile ncnn.

   cd ncnn
   mkdir -p build-aarch64-linux
   cd build-aarch64-linux
   cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/aarch64-linux-gnu.toolchain.cmake –DANDROID=ON ..
   make -j8
   make install
   

Getting Started with GPU version

1. The compilation of the project should be on the ARM device.

2. Install OpenCV.

   sudo apt-get install libopencv-dev
   

3. cd YOLOv5ncnn-vulkan
   

4. Edit "CMakeLists.txt" to configure correctly.

5. Compile and run.

   cd build
   cmake ..
   make
   ./../bin/YOLOv5ncnn-vulkan
   

Compile ncnn-vulkan-ARM by yourself

1. The compilation of ncnn-vulkan should be on the x86 device.

2. Install protobuf.

   sudo apt install protobuf-compiler libprotobuf-dev 
   

3. Install OpenCV.

   sudo apt-get install libopencv-dev
   

4. Download vulkan-sdk from https://vulkan.lunarg.com/sdk/home#sdk/downloadConfirm/1.2.148.0/linux/vulkansdk-linux-x86_64-1.2.148.0.tar.gz. and add to environment variables (reboot may needed).

   export VULKAN_SDK=~/vulkan-sdk-1.2.148.0/x86_64
   export PATH=$PATH:$VULKAN_SDK/bin
   export LIBRARY_PATH=$LIBRARY_PATH$:VULKAN_SDK/lib
   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$VULKAN_SDK/lib
   export VK_LAYER_PATH=$VULKAN_SDK/etc/vulkan/explicit_layer.d
   

5. Download source code of ncnn from https://github.com/Tencent/ncnn/releases.

   unzip ncnn-master.zip
   

6. Download gcc-arm-toolchain and add to environment variables.

   tar -zxvf gcc-arm-8.2-2018.11-x86_64-aarch64-linux-gnu.tar.xz
   gedit ~/.bashrc
   export PATH=$PATH:/home/username/gcc-arm-8.2-2018.11-x86_64-aarch64-linux-gnu/bin
   source ~/.bashrc
   

7. Compile ncnn-vulkan.

   cd ncnn
   mkdir -p build-aarch64-linux-vulkan
   cd build-aarch64-linux-vulkan
   cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/aarch64-linux-gnu.toolchain.cmake –DANDROID=ON -DNCNN_VULKAN=ON ..
   make -j8
   make install
   

8. In order to compile the project correctly on ARM devices, additional static link libraries (libvulkan-sdk.a and libvulkan-stub.a) are needed and can be obtained from [here](ARM-software/vulkan-sdk: Github repository for the Vulkan SDK).

Get your own Yolov5 ncnn model

We train a model in Pytorch and first convert to onnx and then to ncnn.

1. For how to train in Pytorch and export to onnx, see https://github.com/ultralytics/yolov5.

2. Because ncnn has limited support for operators, the network definition needs to be modified before training, please modify "common.py".

   from

   class Focus(nn.Module):
       def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
           super(Focus, self).__init__()
           self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
   
       def forward(self, x):
           return self.conv(torch.cat([x[..., ::2, ::2], x[..., ::2, ::2], x[..., ::2, ::2], x[..., ::2, ::2]], 1))
   

   to

   class Focus(nn.Module):
       def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
           super(Focus, self).__init__()
           self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
   
       def forward(self, x):
           return self.conv(torch.cat([torch.nn.functional.interpolate(x, scale_factor=0.5),
                                       torch.nn.functional.interpolate(x, scale_factor=0.5),
                                       torch.nn.functional.interpolate(x, scale_factor=0.5),
                                       torch.nn.functional.interpolate(x, scale_factor=0.5)], 1))
   

3. When export to onnx, Detect layer should be removed from the graph, please modify "export.py".

   model.model[-1].export = True
   

4. Simplify the onnx model by onnx-simplifier.

   pip3 install onnx-simplifier
   python3 -m onnxsim yolov5s.onnx yolov5s.onnx
   

5. Convert onnx to ncnn

   ./onnx2ncnn yolov5s.onnx yolov5s.param yolov5s.bin