Converter Tool

Overview

MindSpore Lite provides a tool for offline model conversion. It supports conversion of multiple types of models. The converted models can be used for inference. The command line parameters contain multiple personalized options, providing a convenient conversion method for users.

Currently, the following input formats are supported: MindSpore, TensorFlow Lite, Caffe, and ONNX.

Linux Environment Instructions

Environment Preparation

To use the MindSpore Lite model conversion tool, you need to prepare the environment as follows:

• Compilation: Install basic and additional build dependencies and perform build. The build version is x86_64. The code of the model conversion tool is stored in the mindspore/lite/tools/converter directory of the MindSpore source code. For details about the build operations, see the Environment Requirements and Compilation Example in the build document.

• Run: Obtain the converter tool and configure environment variables by referring to Output Description in the build document.

Parameter Description

You can use ./converter_lite to complete the conversion. In addition, you can set multiple parameters as required. You can enter ./converter_lite --help to obtain help information in real time.

The following describes the parameters in detail.

Parameter	Mandatory or Not	Parameter Description	Value Range	Default Value
--help	No	Prints all help information.	-	-
--fmk=<FMK>	Yes	Original format of the input model.	MS, CAFFE, TFLITE, or ONNX	-
--modelFile=<MODELFILE>	Yes	Path of the input model.	-	-
--outputFile=<OUTPUTFILE>	Yes	Path of the output model. (If the path does not exist, a directory will be automatically created.) The suffix .ms can be automatically generated.	-	-
--weightFile=<WEIGHTFILE>	Yes (for Caffe models only)	Path of the weight file of the input model.	-	-
--quantType=<QUANTTYPE>	No	Sets the quant type of the model.	PostTraining: quantization after training AwareTraining: perceptual quantization	-
--inputInferenceType=<INPUTRINFERENCETYPE>	No(supported by aware quant models only)	Sets the input data type of the converted model. If the type is different from the origin model, the convert tool will insert data type convert op before the model to make sure the input data type is same as the input of origin model.	FLOAT or INT8	FLOAT
--inferenceType=<INFERENCETYPE>	No(supported by aware quant models only)	Sets the output data type of the converted model. If the type is different from the origin model, the convert tool will insert data type convert op before the model to make sure the output data type is same as the input of origin model.	FLOAT or INT8	FLOAT
--stdDev=<STDDEV>	No(supported by aware quant models only)	Sets the standard deviation of the input data.	（0，+∞）	128
--mean=<MEAN>	No(supported by aware quant models only)	Sets the mean value of the input data.	[-128, 127]	-0.5

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

• The Caffe model is divided into two files: model structure *.prototxt, corresponding to the --modelFile parameter; model weight *.caffemodel, corresponding to the --weightFile parameter

Example

First, in the root directory of the source code, run the following command to perform compilation. For details, see build.md.

bash build.sh -I x86_64

Currently, the model conversion tool supports only the x86_64 architecture.

The following describes how to use the conversion command by using several common examples.

• Take the Caffe model LeNet as an example. Run the following conversion command:

  ./converter_lite --fmk=CAFFE --modelFile=lenet.prototxt --weightFile=lenet.caffemodel --outputFile=lenet
  

  In this example, the Caffe model is used. Therefore, the model structure and model weight files are required. Two more parameters fmk and outputFile are also required.

  The output is as follows:

  INFO [converter/converter.cc:190] Runconverter] CONVERTER RESULT: SUCCESS!
  

  This indicates that the Caffe model is successfully converted into the MindSpore Lite model and the new file lenet.ms is generated.

• The following uses the MindSpore, TensorFlow Lite, ONNX and perception quantization models as examples to describe how to run the conversion command.

  • MindSpore model model.mindir

    ./converter_lite --fmk=MS --modelFile=model.mindir --outputFile=model
    

  • TensorFlow Lite model model.tflite

    ./converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model
    

  • ONNX model model.onnx

    ./converter_lite --fmk=ONNX --modelFile=model.onnx --outputFile=model
    

  • TensorFlow Lite aware quantization model model_quant.tflite

    ./converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model --quantType=AwareTraining
    

  • TensorFlow Lite aware quantization model model_quant.tflite set the input and output data type to be int8

    ./converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model --quantType=AwareTraining  --inputInferenceType=INT8  --inferenceType=INT8
    

  In the preceding scenarios, the following information is displayed, indicating that the conversion is successful. In addition, the target file model.ms is obtained.

  INFO [converter/converter.cc:190] Runconverter] CONVERTER RESULT: SUCCESS!