Converting an MXNet* Model¶
Convert an MXNet* Model¶
To convert an MXNet* model, run Model Optimizer with a path to the input model
mo --input_model model-file-0000.params
Using MXNet*-Specific Conversion Parameters¶
The following list provides the MXNet*-specific parameters.
Symbol file (for example, "model-symbol.json") that contains a topology structure and layer attributes
Prefix name for args.nd and argx.nd files
Name of a pre-trained MXNet model without extension and epoch
number. This model will be merged with args.nd and argx.nd
Enable saving built parameters file from .nd files
Enable MXNet loader to make a model compatible with the latest MXNet version.
Use only if your model was trained with MXNet version lower than 1.0.0
Enable transformation for converting the gluoncv ssd topologies.
Use only if your topology is one of ssd gluoncv topologies
By default, the Model Optimizer does not use the MXNet loader, as it transforms the topology to another format, which is compatible with the latest version of MXNet, but it is required for models trained with lower version of MXNet. If your model was trained with MXNet version lower than 1.0.0, specify the
--legacy_mxnet_model key to enable the MXNet loader. However, the loader does not support models with custom layers. In this case, you must manually recompile MXNet with custom layers and install it to your environment.
Custom Layer Definition¶
Internally, when you run the Model Optimizer, it loads the model, goes through the topology, and tries to find each layer type in a list of known layers. Custom layers are layers that are not included in the list of known layers. If your topology contains any layers that are not in this list of known layers, the Model Optimizer classifies them as custom.
Supported MXNet* Layers¶
Refer to Supported Framework Layers for the list of supported standard layers.
Frequently Asked Questions (FAQ)¶
The Model Optimizer provides explanatory messages if it is unable to run to completion due to issues like typographical errors, incorrectly used options, or other issues. The message describes the potential cause of the problem and gives a link to the Model Optimizer FAQ. The FAQ has instructions on how to resolve most issues. The FAQ also includes links to relevant sections in the Model Optimizer Developer Guide to help you understand what went wrong.
In this document, you learned:
Basic information about how the Model Optimizer works with MXNet* models
Which MXNet* models are supported
How to convert a trained MXNet* model using the Model Optimizer with both framework-agnostic and MXNet-specific command-line options