MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
Initial support for PyTorch1.5.1 .
We recommand Python 3.6 since Python 2 has retired. If you experience incompatibility with other Python 3.x versions, please submit an issue.
Numpy 1.14.5 => 1.15.4
Tensorflow 1.9.0 => 1.13.1
Keras 2.1.6=>2.2.4
CoreML 0.8=> 2.1.0
Cntk 2.5.1=>2.6
MXNet 1.1.0 post0 => 1.2.0
Onnx 1.2.1=>1.4.1
Onnx-tf =>1.2.1(python3), 1.1.2(python2)
Initial implementation for RNN operators including GRU (Gated Recurrent Unit) cell, LSTM (Long Short-Term Memory) cell (no support for peephole yet) by matching the cell's corresponding pattern and fold them into a fuction or class in target framework.
Various fixes and stability improvements.
Support for for ssd and fastrcnn in tensorflow frozen parser.
Initial support for resnet-v2 , inception_resnet-v2 and facenet.
Add some ops which include Pack, Shape, Scale, StrideSlice, Prelu, Mul, Abs, Sub, Squeeze.
Formal release of CoreML parser and Caffe/Onnx/CoreML emitter. darknet parser initial release.