Menoh is DNN inference library with C API.

Menoh is released under MIT License.

Document

This codebase contains C API and C++ API.

See also

• Chainer model to ONNX : onnx-chainer

• C# wrapper : menoh-sharp

• Haskell wrapper : menoh-haskell

• DNN Inference with CPU
• ONNX support
• Easy to use.

• MKL-DNN Library (0.14 or later)
• ProtocolBuffers (3.5.1 is checked)

Execute below commands in root directory.

python retrieve_data.py
mkdir build && cd build
cmake ..
make

Execute below command in build directory created at Build section.

make install

Execute below command in root directory.

./example/vgg16_example_in_cpp

Result is below

vgg16 example
-22.3708 -34.4082 -10.218 24.2962 -0.252342 -8.004 -27.0804 -23.0728 -7.05607 16.1343
top 5 categories are
8 0.96132 n01514859 hen
7 0.0369939 n01514668 cock
86 0.00122795 n01807496 partridge
82 0.000225824 n01797886 ruffed grouse, partridge, Bonasa umbellus
97 3.83677e-05 n01847000 drake

Please give --help option for details

./example/vgg16_example --help

Setup chainer

Then, execute below commands in root directory.

python gen_test_data.py
cd build
cmake -DENABLE_TEST=ON ..
make
./test/menoh_test.out

• Elu
• LeakyRelu
• Relu
• Softmax
• Tanh

• Concat

• Conv
• ConvTranspose
• FC

• Abs
• Add
• Sqrt

• BatchNormalization

• AveragePool
• GlobalAveragePool
• GlobalMaxPool
• MaxPool

Menoh is released under MIT License. Please see the LICENSE file for details.

Note: retrieve_data.py downloads data/VGG16.onnx. data/VGG16.onnx is generated by onnx-chainer from pre-trained model which is uploaded at http://www.robots.ox.ac.uk/%7Evgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel

That pre-trained model is released under Creative Commons Attribution License.