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Author
Last Commit
May. 27, 2018
Created
Aug. 16, 2017

MMdnn MMdnn

License

A comprehensive, cross-framework solution to convert, visualize and diagnosis deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.

Basically, it converts many DNN models that trained by one framework into others. The major features include:

  • Model File Converter Converting DNN models between frameworks
  • Model Code Snippet Generator Generating training or inference code snippet for frameworks
  • Model Visualization Visualizing DNN network architecture and parameters for frameworks
  • Model compatibility testing (On-going)

This project is designed and developed by Microsoft Research (MSR). We also encourage researchers and students leverage this project to analysis DNN models and we welcome any new ideas to extend this project.

Installation

You can get stable version of MMdnn by

pip install mmdnn

or you can try the newest version by

pip install -U git+https://github.com/Microsoft/[email protected]

Features

Model Conversion

Across the industry and academia, there are a number of existing frameworks available for developers and researchers to design a model, where each framework has its own network structure definition and saving model format. The gaps between frameworks impede the inter-operation of the models.

We provide a model converter to help developers convert models between frameworks, through an intermediate representation format.

Support frameworks

[Note] You can click the links to get detail README of each framework

Tested models

The model conversion between currently supported frameworks is tested on some ImageNet models.

Models Caffe Keras Tensorflow CNTK MXNet PyTorch CoreML
Inception V1 x (No LRN)
Inception V3 ×
Inception V4 o
ResNet V1 × o
ResNet V2
VGG 19
MobileNet_v1 × o × ×
Xception × o × ×
SqueezeNet
DenseNet
NASNet o
ResNext
voc FCN
Yolo3

Usage

One command to achieve the conversion. Use a TensorFlow ResNet V2 152 to PyTorch as our example.

$ mmdownload -f tensorflow -n resnet_v2_152 -o ./
$ mmconvert -sf tensorflow -in imagenet_resnet_v2_152.ckpt.meta -iw imagenet_resnet_v2_152.ckpt --dstNode MMdnn_Output -df pytorch -om tf_resnet_to_pth.pth

Done.

On-going frameworks

  • PyTorch (Source)
  • Torch7 (Source)
  • Chainer (help wants)

On-going Models

  • Semantic Segmentation
  • Image Style Transfer
  • Object Detection
  • RNN

Model Visualization

You can use the MMdnn model visualizer and submit your IR json file to visualize your model. In order to run the commands below, you will need to install requests, keras, and Tensorflow using your favorite package manager.

Use the Keras "inception_v3" model as an example again.

  1. Download the pre-trained models
$ mmdownload -f keras -n inception_v3
  1. Convert the pre-trained model files into intermediate representation
$ mmtoir -f keras -w imagenet_inception_v3.h5 -o keras_inception_v3
  1. Open the MMdnn model visualizer and choose file keras_inception_v3.json

vismmdnn


Examples

Official Tutorial

Users' Examples


Contributing

Intermediate Representation

The intermediate representation stores the network architecture in protobuf binary and pre-trained weights in NumPy native format.

[Note!] Currently the IR weights data is in NHWC (channel last) format.

Details are in ops.txt and graph.proto. New operators and any comments are welcome.

Frameworks

We are working on other frameworks conversion and visualization, such as PyTorch, CoreML and so on. And more RNN related operators are investigating. Any contributions and suggestions are welcome! Details in Contribution Guideline

License

Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Acknowledgements

Thanks to Saumitro Dasgupta, the initial code of caffe -> IR converting is references to his project caffe-tensorflow.