- Learning
ncnnandcppwithyolo. My device iscpu, so feel easy to run - Folder Tree:
|__ cpp_root
| |__ ncnn
| |__ eigen-3.3.9
| |__ opencv_setup
| |__ yolo_ncnn_cpp --> this repository
| |__ export_to_ncnn
To install ncnn, please follow build tutorial of ncnn to build on your own device or follow me below
- Install dependencies:
sudo apt update
sudo apt install build-essential git cmake libprotobuf-dev protobuf-compiler libvulkan-dev vulkan-utils libopencv-dev
sudo apt-get install libprotobuf-dev protobuf-compiler
sudo apt-get install libopencv-dev- Install eigen-3.3.9 [google], [baidu(code:ueq4)].
unzip eigen-3.3.9.zip
cd eigen-3.3.9
mkdir build
cd build
cmake ..
sudo make install- Install Opencv if needed (quick install opencv)
mkdir opencv_setup && cd opencv_setup
sudo apt update && sudo apt install -y cmake g++ wget unzip
wget -O opencv.zip https://github.com/opencv/opencv/archive/4.x.zip
unzip opencv.zip
mkdir -p build && cd build
cmake ../opencv-4.x
cmake --build .- If you want to install ncnn with cpu (ubuntu) (after installing all required dependencies in
ncnnrepo):
git clone https://github.com/Tencent/ncnn.git
cd ncnn
git submodule update --init
mkdir -p build
cd build
# cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_VULKAN=OFF -DNCNN_CUDA=OFF -DNCNN_RUNTIME_CPU=ON -DNCNN_SYSTEM_GLSLANG=ON -DNCNN_SHARED_LIB=ON ..
cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_VULKAN=OFF -DNCNN_RUNTIME_CPU=ON -DNCNN_SYSTEM_GLSLANG=ON -DNCNN_BUILD_EXAMPLES=OFF -DNCNN_SHARED_LIB=ON ..
make
make installFollow this tutorial or follow me bellow:
- Clone Ultralytics:
cd export_to_ncnn
git clone https://github.com/ultralytics/ultralytics.git # clone
cd ultralytics
git checkout d99e04daa1290c226a3fae825401361b17ce164c # switch to commit id
pip install -r requirements.txt # install
pip install -e . # install ultralytics as package- Update code in
ultralytics/ultralytics/nn/modules.pyat line 398-411:
# def forward(self, x):
# shape = x[0].shape # BCHW
# for i in range(self.nl):
# x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
# if self.training:
# return x
# elif self.dynamic or self.shape != shape:
# self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
# self.shape = shape
#
# box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1)
# dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides
# y = torch.cat((dbox, cls.sigmoid()), 1)
# return y if self.export else (y, x)
def forward(self, x):
z = [] # inference output
for i in range(self.nl):
boxes = self.cv2[i](x[i]).permute(0, 2, 3, 1)
scores = self.cv3[i](x[i]).sigmoid().permute(0, 2, 3, 1)
feat = torch.cat((boxes, scores), -1)
z.append(feat)
return tuple(z)- Export model to torchscript:
cd ..
yolo export model=yolov8s.pt format=torchscript- Download pnnx to export from onnx to ncnn:
wget https://github.com/pnnx/pnnx/releases/download/20230217/pnnx-20230217-ubuntu.zip
unzip pnnx-20230217-ubuntu.zip./pnnx-20230217-ubuntu/pnnx yolov8s.torchscript inputshape=[1,3,640,640] inputshape2=[1,3,320,320]- Move your <model_name>.param and <model_name>.bin to anywhere
- To build yolov5:
cd yolov5
mkdir build
cd build
cmake ..
make- To build yolov8:
cd yolov8
mkdir build
cd build
cmake ..
make- To infer yolov5:
cd build
./yolov5 ../../test.jpg- To infer yolov8:
cd build
./yolov8 ../../test.jpg- build ncnn with
-DNCNN_CUDA=ONto test working with GPU - step by step to export .pt to ncnn
- check this line
yolov5.opt.use_vulkan_compute = true;
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent