A comprehensive, cross-framework solution to convert, visualize and diagnose deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.
Typically, people use deep neural networks with following steps:
-------------- -------------- | Find model | --------------------------------------------> | Deployment | -------------- | -------------- | ^ ^ | -------------- | | -------> | Conversion | ---------------------- | -------------- | | | | ----------- | ----------> | Retrain | ------------ -----------
In MMdnn, we focus on helping users handle their work better.
- We implement an universal convertor to convert DNN models between frameworks, which means you can train on one framework and deploy on another.
- In convertor, we can generate some training/inference code snippet to simplify the retrain/evaluate work.
This project is designed and developed by Microsoft Research (MSR). We also encourage researchers and students leverage this project to analyse DNN models. We welcome any new ideas to extend this project.
You can get stable version of MMdnn by
pip install mmdnn
And make sure to have Python installed or you can try the newest version by
pip install -U git+https://github.com/Microsoft/[email protected]
Install with docker image
MMdnn provides a docker image, which packages MMdnn, deep learning frameworks we support and other dependencies in one image. You can easily get the image in several steps:
Install Docker Community Edition(CE)
Pull MMdnn docker image
docker pull mmdnn/mmdnn:cpu.small
Run image in interactive mode
docker run -it mmdnn/mmdnn:cpu.small
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.
[Note] You can click the links to get detail README of each framework
- Microsoft Cognitive Toolkit (CNTK)
- ONNX (Destination only)
- TensorFlow (Experimental) (We highly recommend you read the README of TensorFlow first)
- DarkNet (Source only, Experiment)
The model conversion between currently supported frameworks is tested on some ImageNet models.
One command to achieve the conversion. Using 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 --dstNodeName MMdnn_Output -df pytorch -om tf_resnet_to_pth.pth
- Torch7 (help wanted)
- Chainer (help wanted)
- Face Detection
- Semantic Segmentation
- Image Style Transfer
- Object Detection
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.
- Download the pre-trained models
$ mmdownload -f keras -n inception_v3
- Convert the pre-trained model files into intermediate representation
$ mmtoir -f keras -w imagenet_inception_v3.h5 -o keras_inception_v3
- Open the MMdnn model visualizer and choose file keras_inception_v3.json
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.
We are working on other frameworks conversion and visualization, such as PyTorch, CoreML and so on. We're investigating more RNN related operators. Any contributions and suggestions are welcome! Details in Contribution Guideline.
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.
Cheng CHEN (Microsoft Research Asia): Project Manager; Caffe, CNTK, CoreML Emitter, Keras, MXNet, TensorFlow
Jiahao YAO (Peking University): CoreML, MXNet Emitter, PyTorch Parser; HomePage
Ru ZHANG (Chinese Academy of Sciences): CoreML Emitter, DarkNet Parser, Keras, TensorFlow frozen graph Parser; Yolo and SSD models; Tests
Yuhao ZHOU (Shanghai Jiao Tong University): MXNet
Tingting QIN (Microsoft Research Asia): Caffe Emitter
Tong ZHAN (Microsoft): ONNX Emitter
Qianwen WANG (Hong Kong University of Science and Technology): Visualization