Device-side Training Model Conversion

Overview

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

In the first step, create a network model based on the MindSpore architecture using Python and export it as a .mindir file. See saving model in the cloud.
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.

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.

Parameters	required	Parameter Description	Value Range	Default Value
`--help`	no	Prints all the help information.	-	-
`--fmk=<FMK>`	yes	Original format of the input model.	MINDIR	-
`--modelFile=<MODELFILE>`	yes	Path of the input model.	-	-
`--outputFile=<OUTPUTFILE>`	yes	Path of the output model. The suffix `.ms` can be automatically generated.	-	-
`--trainModel=true`	no	If the original model is a training model, the value must be true.	true, false	false
`--configFile=<CONFIGFILE>`	No	1. Configure quantization parameter; 2. Profile path for extension.	-	-

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 quantization.

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

The output of successful conversion is as follows:

CONVERT RESULT SUCCESS:0

This indicates that the MindSpore model is successfully converted to a MindSpore end-side model and a new file my_model.ms is generated. If the output of conversion failure is as follows:

CONVERT RESULT FAILED:

The program returns error codes and error messages.