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MXNet README

MXNet pre-trained model

We tested some MXNet pre-trained models to others, get more detail from this file

Models Caffe Keras Tensorflow CNTK MXNet PyTorch CoreML ONNX
Vgg19
Inception_bn
ResNet 18
ResNet 152
ResNext 50
ResNext 101
squeezenet_v1

- Correctness tested

o - Some difference after conversion

space - not tested


Usage

Download MXNET pre-trained model

$ mmdownload -f mxnet

Supported models : ['imagenet1k-resnet-152', 'vgg19', 'imagenet1k-resnet-101', 'imagenet1k-resnet-50', 'vgg16', 'imagenet1k-inception-bn', 'imagenet1k-resnext-101', 'imagenet11k-resnet-152', 'imagenet1k-resnext-50', 'imagenet1k-resnext-101-64x4d', 'imagenet1k-resnet-18', 'imagenet11k-place365ch-resnet-152', 'imagenet1k-resnet-34', 'squeezenet_v1.1', 'imagenet11k-place365ch-resnet-50', 'squeezenet_v1.0']

$ mmdownload -f mxnet -n imagenet1k-resnet-50 -o ./

Downloading file [./resnet-50-symbol.json] from [http://data.mxnet.io/models/imagenet/resnet/50-layers/resnet-50-symbol.json]
progress: 80.0 KB downloaded, 100%
Downloading file [./resnet-50-0000.params] from [http://data.mxnet.io/models/imagenet/resnet/50-layers/resnet-50-0000.params]
progress: 100000.0 KB downloaded, 100%
MXNet Model imagenet1k-resnet-50 saved as [./resnet-50-symbol.json] and [./resnet-50-0000.params].

One-step conversion

Above MMdnn@0.1.4, we provide one command to achieve the conversion

$  mmconvert -sf mxnet -in resnet-50-symbol.json -iw resnet-50-0000.params -df cntk -om mxnet_resnet50.dnn --inputShape 3,224,224
.
.
.
CNTK model file is saved as [mxnet_resnet50.dnn], generated by [4c616299273a42e086b30c6c4d1c64c0.py] and [4c616299273a42e086b30c6c4d1c64c0.npy].

Then you get the CNTK original model mxnet_resnet152.dnn converted from MXNet. Temporal files are removed automatically.


Step-by-step conversion (for debugging)

Convert architecture from MXNET to IR (MXNET -> IR)

You can use following bash command to convert the network architecture [mxnet/models/resnet-50-symbol.json] to IR architecture file [resnet50.pb], [resnet50.json]. You can convert only network structure to IR for visualization or training in other frameworks.

$ mmtoir -f mxnet -n mxnet/models/resnet-50-symbol.json -d resnet50 --inputShape 3,224,224
.
.
.
IR network structure is saved as [resnet50.json].
IR network structure is saved as [resnet50.pb].
Warning: weights are not loaded.

Convert models (including architecture and weights) from MXNet to IR (MXNET -> IR)

You can use following bash command to convert the network architecture [mxnet/models/resnet-50-symbol.json] with weights [mxnet/models/resnet-50-0000.params] to IR architecture file [resnet50.pb], [resnet50.json], [resnet50.npy].

The input data shape is not in the architecture description of MXNet, we need to specify the data shape in conversion command.

$ mmtoir -f mxnet -n mxnet/models/resnet-50-symbol.json -w mxnet/models/resnet-50-0000.params -d resnet50 --inputShape 3,224,224
.
.
.
IR network structure is saved as [resnet50.json].
IR network structure is saved as [resnet50.pb].
IR weights are saved as [resnet50.npy].

Convert models from IR to MXNet code snippet and weights (IR -> MXNet)

We need to generate both MXNet architecture code snippet and weights file to build the MXNet network.

[Note!] Argument 'dw' is used to specify the converted MXNet model file name for next step use.

$ mmtocode -f mxnet --IRModelPath inception_v3.pb --dstModelPath mxnet_inception_v3.py --IRWeightPath inception_v3.npy -dw mxnet_inception_v3-0000.params

Parse file [inception_v3.pb] with binary format successfully.
Detect input layer [input_1] using infer batch size, set it as default value [1]
Target network code snippet is saved as [mxnet_inception_v3.py].

Convert models from IR to MXNet checkpoint file

After generating the MXNet code snippet and weights, you can take a further step to generate an original MXNet checkpoint file.

$ python -m mmdnn.conversion.examples.mxnet.imagenet_test -n mxnet_inception_v3 -w mxnet_inception_v3-0000.params --dump inception_v3
.
.
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MXNet checkpoint file is saved as [inception_v3], generated by [mxnet_inception_v3.py] and [mxnet_inception_v3-0000.params].

Then the output files inception_v3-symbol.json and inception_v3-0000.params can be loaded by MXNet directly.


Develop version

Ubuntu 16.04 with

  • MXNet 0.11.0

@ 11/22/2017

Limitation

  • Currently no RNN related operations support