python wrapper of ncnn with pybind11, only support python3.x now.

ncnn is available as wheel packages for macOS, Windows and Linux distributions, you can install with pip:

python -m pip install -U pip
python -m pip install -U ncnn

If you want to build ncnn with some options not as default, or just like to build everything yourself, it is not difficult to build ncnn from source.

On Unix (Linux, OS X)

• A compiler with C++11 support
• CMake >= 3.4

On Mac

• A compiler with C++11 support
• CMake >= 3.4

On Windows

• Visual Studio 2015 or higher
• CMake >= 3.4

1. clone ncnn and init submodule.

cd /pathto/ncnn
git submodule init && git submodule update

2. build and install.

python setup.py install

If you want to use a custom toolchain, you can install with the CMAKE_TOOLCHAIN_FILE environment variable, like this:

CMAKE_TOOLCHAIN_FILE="../../toolchains/power9le-linux-gnu-vsx.clang.toolchain.cmake" python setup.py install

if you want to enable the usage of vulkan, you can install as following:

python setup.py install --vulkan=on

Attention:

To enable Vulkan support, you must first install the Vulkan SDK.

For Windows or Linux Users:

Ensure that the VULKAN_SDK environment variable is set to the path of the Vulkan SDK.

For MacOS Users:

On MacOS, you will need to specify additional environment variables. For guidance on setting these variables, please refer to lines 279-286 in the following file: ncnn/.github/workflows/release-python.yml at master · Tencent/ncnn.

1. clone ncnn and init submodule.

cd /pathto/ncnn
git submodule init && git submodule update

2. build.

mkdir build
cd build
cmake -DNCNN_PYTHON=ON ..
make

3. install

cd /pathto/ncnn
pip install .

if you use conda or miniconda, you can also install as following:

cd /pathto/ncnn
python3 setup.py install

test

cd /pathto/ncnn/python
python3 tests/test.py

benchmark

cd /pathto/ncnn/python
python3 tests/benchmark.py

ncnn.Mat->numpy.array, with no memory copy

mat = ncnn.Mat(...)
mat_np = np.array(mat)

numpy.array->ncnn.Mat, with no memory copy

mat_np = np.array(...)
mat = ncnn.Mat(mat_np)

install requirements

pip install -r requirements.txt

then you can import ncnn.model_zoo and get model list as follow:

import ncnn
import ncnn.model_zoo as model_zoo

print(model_zoo.get_model_list())

models now in model zoo are as list below:

mobilenet_yolov2
mobilenetv2_yolov3
yolov4_tiny
yolov4
yolov5s
yolact
mobilenet_ssd
squeezenet_ssd
mobilenetv2_ssdlite
mobilenetv3_ssdlite
squeezenet
faster_rcnn
peleenet_ssd
retinaface
rfcn
shufflenetv2
simplepose
nanodet

all model in model zoo has example in ncnn/python/examples folder

custom layer demo is in ncnn/python/ncnn/model_zoo/yolov5.py:23