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https://github.com/pfnet-research/menoh
Menoh: fast DNN inference library with multiple programming language support
https://github.com/pfnet-research/menoh
deep-learning mkl-dnn neural-network
Last synced: 1 day ago
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Menoh: fast DNN inference library with multiple programming language support
- Host: GitHub
- URL: https://github.com/pfnet-research/menoh
- Owner: pfnet-research
- License: mit
- Created: 2018-06-21T01:48:20.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-01-02T15:39:29.000Z (almost 4 years ago)
- Last Synced: 2024-08-31T21:35:45.944Z (2 months ago)
- Topics: deep-learning, mkl-dnn, neural-network
- Language: C++
- Homepage:
- Size: 1.4 MB
- Stars: 279
- Watchers: 31
- Forks: 34
- Open Issues: 34
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
**Notice: Menoh is no longer maintained. Part of its functionality is inherited by [chainer-compiler](https://github.com/pfnet-research/chainer-compiler).**
# Menoh
[](https://travis-ci.org/pfnet-research/menoh) [](https://ci.appveyor.com/project/pfnet-research/menoh/branch/master)
[](https://scan.coverity.com/projects/pfnet-research-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.
[Document](https://pfnet-research.github.io/menoh/)
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](https://github.com/chainer/onnx-chainer)
- C# wrapper : [menoh-sharp](https://github.com/pfnet-research/menoh-sharp)
- Go wrapper : [go-menoh](https://github.com/pfnet-research/go-menoh)
- (unofficial wrapper [gomenoh](https://github.com/kou-m/gomenoh) by kou-m san has been merged)
- Haskell wrapper : [menoh-haskell](https://github.com/pfnet-research/menoh-haskell)
- Node.js wrapper : [node-menoh](https://github.com/pfnet-research/node-menoh)
- Ruby wrapper : [menoh-ruby](https://github.com/pfnet-research/menoh-ruby)
- Rust wrapper : [menoh-rs](https://github.com/pfnet-research/menoh-rs)
- There is also [unofficial Rust wrapper by Y-Nak san](https://github.com/Y-Nak/menoh-rs)
- Java wrapper : [menoh-java](https://github.com/pfnet-research/menoh-java)
- [Unofficial] ROS interface by Akio Ochiai san : [menoh_ros](https://github.com/akio/menoh_ros)
- [Unofficial] OCaml wrapper by wkwkes san : [Menohcaml](https://github.com/wkwkes/Menohcaml)
# Installation using package manager or binary packages
- For Windows users, prebuild libraries are available (see [release](https://github.com/pfnet-research/menoh/releases)) and [Nuget package](https://www.nuget.org/packages/Menoh/) is available.
- For macOS user, [Homebrew tap repository](https://github.com/pfnet-research/homebrew-menoh) is available.
- For Ubuntu user, binary packages are available.
```
$ curl -LO https://github.com/pfnet-research/menoh/releases/download/v1.1.1/ubuntu1604_mkl-dnn_0.16-1_amd64.deb
$ curl -LO https://github.com/pfnet-research/menoh/releases/download/v1.1.1/ubuntu1604_menoh_1.1.1-1_amd64.deb
$ curl -LO https://github.com/pfnet-research/menoh/releases/download/v1.1.1/ubuntu1604_menoh-dev_1.1.1-1_amd64.deb
$ sudo apt install ./ubuntu1604_*_amd64.deb
```
If you are using Ubuntu 18.04, please replace `1604` with `1804`.
# 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](BUILDING.md) for details.
## Installation
Execute following command in build directory created at Build section.
```
make install
```
# Run VGG16 example (it can run ResNet-50 as well)
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
```
You can also run ResNet-50
```
./example/vgg16_example_in_cpp -m ../data/resnet50.onnx
```
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.
Pre-trained models downloaded via `retrieve_data.py` were converted by onnx-chainer. The original models were downloaded via [ChainerCV](https://github.com/chainer/chainercv).
Check `scripts/generate_vgg16_onnx.py` and `scripts/generate_resnet50_onnx.py` and see [the LICENSE of ChainerCV](https://chainercv.readthedocs.io/en/stable/license.html) about each terms of use of the pre-trained models.