Converting MindSpore Lite Models

Overview

Creating your MindSpore Lite(Train on Device) model is a two step procedure:

• In the first step the model is defined and the layers that should be trained must be declared. This is being done on the server, using a MindSpore-based Python code. The model is then exported into a protobuf format, which is called MINDIR.
• In the seconde step this .mindir model is converted into a .ms format that can be loaded onto an embedded device and can be trained using the MindSpore Lite framework. The converted .ms models can be used for both training and inference.

Linux Environment

Environment Preparation

MindSpore Lite model transfer tool (only suppot Linux OS) has provided multiple parameters. The procedure is as follows:

• Compile or download model transfer tool.

• Add the path of dynamic library required by the conversion tool to the environment variables LD_LIBRARY_PATH.

  export LD_LIBRARY_PATH=${PACKAGE_ROOT_PATH}/tools/converter/lib:${LD_LIBRARY_PATH}

  ${PACKAGE_ROOT_PATH} is the decompressed package path obtained by compiling or downloading.

Parameters Description

The table below shows the parameters used in the MindSpore Lite model training transfer tool.

The parameter name and parameter value are separated by an equal sign (=) and no space is allowed between them.

The calibration dataset configuration file uses the key=value mode to define related parameters. For the configuration parameters related to quantization, please refer to post training quantization.

If running the conversion command is failed, an errorcode will be output.

Example

Suppose the file to be converted is my_model.mindir and run the following command:

./converter_lite --fmk=MINDIR --trainModel=true --modelFile=my_model.mindir --outputFile=my_model

If the command executes successfully, the model.ms target file will be obtained and the console will print as follows:

CONVERT RESULT SUCCESS:0

If running the conversion command is failed, an errorcode will be output.