Menoh
Menoh is DNN inference library with C API.
Menoh is released under MIT License.
DISCLAIMER: Menoh is still experimental. Use it at your own risk. In particular not all operators in ONNX are supported, so please check whether the operators used in your model are supported. We have checked that VGG16 and ResNet50 models converted by onnx-chainer work fine.
This codebase contains C API and C++ API.
Goal
- DNN Inference with CPU
- ONNX support
- Easy to use.
Related Projects
- Chainer model to ONNX : onnx-chainer
- C# wrapper : menoh-sharp
- Go wrapper : go-menoh
- (unofficial wrapper gomenoh by kou-m san has been merged)
- Haskell wrapper : menoh-haskell
- Node.js wrapper : node-menoh
- Ruby wrapper : menoh-ruby
- Rust wrapper : menoh-rs
- There is also unofficial Rust wrapper by Y-Nak san
- [Unofficial] ROS interface by Akio Ochiai san : menoh_ros
- [Unofficial] OCaml wrapper by wkwkes san : Menohcaml
Installation using package manager or binary packages
- For Windows users, prebuild libraries are available (see release) and Nuget package is available.
- For macOS user, Homebrew tap repository is available.
- For Ubuntu user, binary packages are available.
If you are using Ubuntu 18.04, please replace
$ curl -LO https://github.com/pfnet-research/menoh/releases/download/v1.0.3/ubuntu1604_mkl-dnn_0.16-1_amd64.deb $ curl -LO https://github.com/pfnet-research/menoh/releases/download/v1.0.3/ubuntu1604_menoh_1.0.3-1_amd64.deb $ curl -LO https://github.com/pfnet-research/menoh/releases/download/v1.0.3/ubuntu1604_menoh-dev_1.0.3-1_amd64.deb $ sudo apt install ./ubuntu1604_*_amd64.deb
1604
with1804
.
Installation from source
Requirements
- MKL-DNN Library (0.14 or later)
- Protocol Buffers (2.6.1 or later)
Build
Execute following commands in root directory.
python scripts/retrieve_data.py
mkdir build && cd build
cmake ..
make
See BUILDING.md for details.
Installation
Execute following command in build directory created at Build section.
make install
Run VGG16 example
Execute following command in root directory.
./example/vgg16_example_in_cpp
Result is here
vgg16 example
-18.1883 -26.5022 -20.0474 13.5325 -0.107129 0.76102 -23.9688 -24.218 -21.6314 14.2164
top 5 categories are
8 0.885836 n01514859 hen
7 0.104591 n01514668 cock
86 0.00313584 n01807496 partridge
82 0.000934658 n01797886 ruffed grouse, partridge, Bonasa umbellus
97 0.000839487 n01847000 drake
Please give --help
option for details
./example/vgg16_example_in_cpp --help
Run test
Setup chainer
Then, execute following commands in root directory.
python scripts/gen_test_data.py
cd build
cmake -DENABLE_TEST=ON ..
make
./test/menoh_test.out
Current supported operators
Activation functions
- Elu
- LeakyRelu
- Relu
- Softmax
- Tanh
Array manipulations
- Concat
Neural network connections
- Conv
- ConvTranspose
- FC
Mathematical functions
- Abs
- Add
- Sqrt
- Sum
Normalization functions
- BatchNormalization
- LRN
Spatial pooling
- AveragePool
- GlobalAveragePool
- GlobalMaxPool
- MaxPool
License
Menoh is released under MIT License. Please see the LICENSE file for details.
Note: scripts/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.